<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>Hadoop Developers</p>Overview<p>Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:How data is distributed, stored, and processed in a Hadoop clusterHow to use Sqoop and Flume to ingest dataHow to process distributed data with Apache SparkHow to model structured data as tables in Impala and HiveHow to choose the best data storage format for different data usage patternsBest practices for data storage</p><p>This training course is the best preparation for the challenges faced by Hadoop developers. Participants will learn to identify which tool is the right one to use in a given situation, and will gain hands-on experience in developing using those tools.</p></div><div id="course-pills-2"><h4>Course Outline</h4><ul><li>Introduction</li><li>Introduction to Hadoop and the Hadoop Ecosystem</li><li>Hadoop Architecture and HDFS</li><li>Importing Relational Data with Apache Sqoop</li><li>Introduction to Impala and Hive</li><li>Modeling and Managing Data with Impala and Hive</li><li>Data Formats</li><li>Data Partitioning</li><li>Capturing Data with Apache Flume</li><li>Spark Basics</li><li>Working with RDDs in Spark</li><li>Writing and Deploying Spark Applications</li><li>Parallel Programming with Spark</li><li>Spark Caching and Persistence</li><li>Common Patterns in Spark Data Processing</li><li>Spark SQL and DataFrames</li><li>Conclusion</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Developer Training for Spark and Hadoop training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Developer Training for Spark and Hadoop course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Learning & Development

Data Engineering

Apache Spark

AWS Certified Developer

Hadoop

Trainers Course

Developer Training for Spark and Hadoop

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>Project administrators and ETL developers responsible for data extraction and transformation using DataStage.</p>Overview<p>Describe the uses of DataStage and the DataStage workflowDescribe the Information Server architecture and how DataStage fits within itDescribe the Information Server and DataStage deployment optionsUse the Information Server Web Console and the DataStage Administrator client to create DataStage users and to configure the DataStage environmentImport and export DataStage objects to a fileImport table definitions for sequential files and relational tablesDesign, compile, run, and monitor DataStage parallel jobsDesign jobs that read and write to sequential filesDescribe the DataStage parallel processing architectureDesign jobs that combine data using joins and lookupsDesign jobs that sort and aggregate dataImplement complex business logic using the DataStage Transformer stageDebug DataStage jobs using the DataStage PX Debugger</p><p>This course enables the project administrators &amp; developers to acquire the skills necessary to develop parallel jobs in DataStage. Students will learn to create parallel jobs that access sequential &amp; relational data, and combine and transform the data.</p></div><div id="course-pills-2"><h4>Course Outline</h4><ul><li>Introduction to DataStage</li><li>Deployment</li><li>DataStage Administration</li><li>Work with Metadata</li><li>Create Parallel Jobs</li><li>Access Sequential Data</li><li>Partitioning and Collecting Algorithms</li><li>Combine Data</li><li>Group Processing Stages</li><li>Transformer Stage</li><li>Repository Functions</li><li>Work with Relational Data</li><li>Control Jobs</li></ul></div>

Service Design Courses (SDC)

SSIS

Data Skills

Data Processing

KM204 IBM InfoSphere DataStage Essentials (v11.5)

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek techniques for working with complex types of data resources.</p>Overview<p>Use Connector stages to read from and write to database tables<br />Handle SQL errors in Connector stages<br />Use Connector stages with multiple input links<br />Use the File Connector stage to access Hadoop HDFS data<br />Optimize jobs that write to database tables<br />Use the Unstructured Data stage to extract data from Excel spreadsheets<br />Use the Data Masking stage to mask sensitive data processed within a DataStage job<br />Use the Hierarchical stage to parse, compose, and transform XML data<br />Use the Schema Library Manager to import and manage XML schemas<br />Use the Data Rules stage to validate fields of data within a DataStage job<br />Create custom data rules for validating data<br />Design a job that processes a star schema data warehouse with Type 1 and Type 2 slowly changing dimensions</p><p>This course is designed to introduce you to advanced parallel job data processing techniques in DataStage v11.5. In this course you will develop data techniques for processing different types of complex data resources including relational data, unstructured data (Excel spreadsheets), and XML data. In addition, you will learn advanced techniques for processing data, including techniques for masking data and techniques for validating data using data rules. Finally, you will learn techniques for updating data in a star schema data warehouse using the DataStage SCD (Slowly Changing Dimensions) stage. Even if you are not working with all of these specific types of data, you will benefit from this course by learning advanced DataStage job design techniques, techniques that go beyond those utilized in the DataStage Essentials course.</p></div><div id="course-pills-2"><h4>Accessing databases</h4><ul><li>Connector stage overview</li><li>- Use Connector stages to read from and write to relational tables</li><li>- Working with the Connector stage properties</li><li>Connector stage functionality</li><li>- Before / After SQL</li><li>- Sparse lookups</li><li>- Optimize insert/update performance</li><li>Error handling in Connector stages</li><li>- Reject links</li><li>- Reject conditions</li><li>Multiple input links</li><li>- Designing jobs using Connector stages with multiple input links</li><li>- Ordering records across multiple input links</li><li>File Connector stage</li><li>- Read and write data to Hadoop file systems</li><li>Demonstration 1: Handling database errors</li><li>Demonstration 2:  Parallel jobs with multiple Connector input links</li><li>Demonstration 3:  Using the File Connector stage to read and write HDFS files</li></ul><h4>Processing unstructured data</h4><ul><li>Using the Unstructured Data stage in DataStage jobs</li><li>- Extract data from an Excel spreadsheet</li><li>- Specify a data range for data extraction in an Unstructured Data stage</li><li>- Specify document properties for data extraction.</li><li>Demonstration 1:  Processing unstructured data</li></ul><h4>Data masking</h4><ul><li>Using the Data Masking stage in DataStage jobs</li><li>- Data masking techniques</li><li>- Data masking policies</li><li>- Applying policies for masquerading context-aware data types</li><li>- Applying policies for masquerading generic data types</li><li>- Repeatable replacement</li><li>- Using reference tables</li><li>- Creating custom reference tables</li><li>Demonstration 1: Data masking</li></ul><h4>Using data rules</h4><ul><li>Introduction to data rules</li><li>- Using the Data Rules Editor</li><li>- Selecting data rules</li><li>- Binding data rule variables</li><li>- Output link constraints</li><li>- Adding statistics and attributes to the output information</li><li>Use the Data Rules stage to valid foreign key references in source data</li><li>Create custom data rules</li><li>Demonstration 1:  Using data rules</li></ul><h4>Processing XML data</h4><ul><li>Introduction to the Hierarchical stage</li><li>- Hierarchical stage Assembly editor</li><li>- Use the Schema Library Manager to import and manage XML schemas</li><li>Composing XML data</li><li>- Using the HJoin step to create parent-child relationships between input lists</li><li>- Using the Composer step</li><li>Writing Hierarchical data to a relational table</li><li>Using the Regroup step</li><li>Consuming XML data</li><li>- Using the XML Parser step</li><li>- Propagating columns</li><li>Topic 6:  Transforming XML data</li><li>- Using the Aggregate step</li><li>- Using the Sort step</li><li>- Using the Switch step</li><li>- Using the H-Pivot step</li><li>Demonstration 1:  Importing XML schemas</li><li>Demonstration 2: Compose hierarchical data</li><li>Demonstration 3: Consume hierarchical data</li><li>Demonstration 4:  Transform hierarchical data</li></ul><h4>Updating a star schema database</h4><ul><li>Surrogate keys</li><li>- Design a job that creates and updates a surrogate key source key file from a dimension table</li><li>Slowly Changing Dimensions (SCD) stage</li><li>- Star schema databases</li><li>- SCD stage Fast Path pages</li><li>- Specifying purpose codes</li><li>- Dimension update specification</li><li>- Design a job that processes a star schema database with Type 1 and Type 2 slowly changing dimensions</li><li>Demonstration 1: Build a parallel job that updates a star schema database with two dimensions</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Data Entry Skills

Data Management

KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This course is best suited to developers, engineers, and architects who want to use use Hadoop and related tools to solve real-world problems.</p>Overview<p>Skills learned in this course include:Creating a data set with Kite SDKDeveloping custom Flume components for data ingestionManaging a multi-stage workflow with OozieAnalyzing data with CrunchWriting user-defined functions for Hive and ImpalaWriting user-defined functions for Hive and ImpalaIndexing data with Cloudera Search</p><p>Cloudera University?s four-day course for designing and building Big Data applications prepares you to analyze and solve real-world problems using Apache Hadoop and associated tools in the enterprise data hub (EDH).</p></div><div id="course-pills-2"><h4>Introduction</h4><h4>Application Architecture</h4><ul><li>Scenario Explanation</li><li>Understanding the Development Environment</li><li>Identifying and Collecting Input Data</li><li>Selecting Tools for Data Processing and Analysis</li><li>Presenting Results to the Use</li></ul><h4>Defining &amp; Using Datasets</h4><ul><li>Metadata Management</li><li>What is Apache Avro?</li><li>Avro Schemas</li><li>Avro Schema Evolution</li><li>Selecting a File Format</li><li>Performance Considerations</li></ul><h4>Using the Kite SDK Data Module</h4><ul><li>What is the Kite SDK?</li><li>Fundamental Data Module Concepts</li><li>Creating New Data Sets Using the Kite SDK</li><li>Loading, Accessing, and Deleting a Data Set</li></ul><h4>Importing Relational Data with Apache Sqoop</h4><ul><li>What is Apache Sqoop?</li><li>Basic Imports</li><li>Limiting Results</li><li>Improving Sqoop?s Performance</li><li>Sqoop 2</li></ul><h4>Capturing Data with Apache Flume</h4><ul><li>What is Apache Flume?</li><li>Basic Flume Architecture</li><li>Flume Sources</li><li>Flume Sinks</li><li>Flume Configuration</li><li>Logging Application Events to Hadoop</li></ul><h4>Developing Custom Flume Components</h4><ul><li>Flume Data Flow and Common Extension Points</li><li>Custom Flume Sources</li><li>Developing a Flume Pollable Source</li><li>Developing a Flume Event-Driven Source</li><li>Custom Flume Interceptors</li><li>Developing a Header-Modifying Flume Interceptor</li><li>Developing a Filtering Flume Interceptor</li><li>Writing Avro Objects with a Custom Flume Interceptor</li></ul><h4>Managing Workflows with Apache Oozie</h4><ul><li>The Need for Workflow Management</li><li>What is Apache Oozie?</li><li>Defining an Oozie Workflow</li><li>Validation, Packaging, and Deployment</li><li>Running and Tracking Workflows Using the CLI</li><li>Hue UI for Oozie</li></ul><h4>Processing Data Pipelines with Apache Crunch</h4><ul><li>What is Apache Crunch?</li><li>Understanding the Crunch Pipeline</li><li>Comparing Crunch to Java MapReduce</li><li>Working with Crunch Projects</li><li>Reading and Writing Data in Crunch</li><li>Data Collection API Functions</li><li>Utility Classes in the Crunch API</li></ul><h4>Working with Tables in Apache Hive</h4><ul><li>What is Apache Hive?</li><li>Accessing Hive</li><li>Basic Query Syntax</li><li>Creating and Populating Hive Tables</li><li>How Hive Reads Data</li><li>Using the RegexSerDe in Hive</li></ul><h4>Developing User-Defined Functions</h4><ul><li>What are User-Defined Functions?</li><li>Implementing a User-Defined Function</li><li>Deploying Custom Libraries in Hive</li><li>Registering a User-Defined Function in Hive</li></ul><h4>Executing Interactive Queries with Impala</h4><ul><li>What is Impala?</li><li>Comparing Hive to Impala</li><li>Running Queries in Impala</li><li>Support for User-Defined Functions</li><li>Data and Metadata Management</li></ul><h4>Understanding Cloudera Search</h4><ul><li>What is Cloudera Search?</li><li>Search Architecture</li><li>Supported Document Formats</li></ul><h4>Indexing Data with Cloudera Search</h4><ul><li>Collection and Schema Management</li><li>Morphlines</li><li>Indexing Data in Batch Mode</li><li>Indexing Data in Near Real Time</li></ul><h4>Presenting Results to Users</h4><ul><li>Solr Query Syntax</li><li>Building a Search UI with Hue</li><li>Accessing Impala through JDBC</li><li>Powering a Custom Web Application with Impala and Search</li></ul></div>

Big Data Analytics

Data Architecture

Designing and Building Big Data Applications

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs.</p>Overview<p>Working in a hands-on learning environment led by our expert instructor you'll:<br />Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications.<br />Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions.<br />Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications.<br />Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights.<br />Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data.<br />Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis.</p><p>Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems.</p></div><div id="course-pills-2"><h4>Introduction to Scala</h4><ul><li>Brief history and motivation</li><li>Differences between Scala and Java</li><li>Basic Scala syntax and constructs</li><li>Scala's functional programming features</li></ul><h4>Introduction to Apache Spark</h4><ul><li>Overview and history</li><li>Spark components and architecture</li><li>Spark ecosystem</li><li>Comparing Spark with other big data frameworks</li></ul><h4>Basics of Spark Programming SparkContext and SparkSession</h4><ul><li>Resilient Distributed Datasets (RDDs)</li><li>Transformations and Actions</li><li>Working with DataFrames</li></ul><h4>Spark SQL and Data Sources</h4><ul><li>Spark SQL library and its advantages</li><li>Structured and semi-structured data sources</li><li>Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.)</li><li>Data manipulation using SQL queries</li></ul><h4>Basic RDD Operations</h4><ul><li>Creating and manipulating RDDs</li><li>Common transformations and actions on RDDs</li><li>Working with key-value data</li></ul><h4>Basic DataFrame and Dataset Operations</h4><ul><li>Creating and manipulating DataFrames and Datasets</li><li>Column operations and functions</li><li>Filtering, sorting, and aggregating data</li></ul><h4>Introduction to Spark Streaming</h4><ul><li>Overview of Spark Streaming</li><li>Discretized Stream (DStream) operations</li><li>Windowed operations and stateful processing</li></ul><h4>Performance Optimization Basics</h4><ul><li>Best practices for efficient Spark code</li><li>Broadcast variables and accumulators</li><li>Monitoring Spark applications</li></ul><h4>Integrating External Libraries and Tools, Spark Streaming</h4><ul><li>Using popular external libraries, such as Hadoop and HBase</li><li>Integrating with cloud platforms: AWS, Azure, GCP</li><li>Connecting to data storage systems: HDFS, S3, Cassandra, etc.</li></ul><h4>Introduction to Machine Learning Basics</h4><ul><li>Overview of machine learning</li><li>Supervised and unsupervised learning</li><li>Common algorithms and use cases</li></ul><h4>Introduction to Spark MLlib</h4><ul><li>Overview of Spark MLlib</li><li>MLlib's algorithms and utilities</li><li>Data preparation and feature extraction</li></ul><h4>Linear Regression and Classification</h4><ul><li>Linear regression algorithm</li><li>Logistic regression for classification</li><li>Model evaluation and performance metrics</li></ul><h4>Clustering Algorithms</h4><ul><li>Overview of clustering algorithms</li><li>K-means clustering</li><li>Model evaluation and performance metrics</li></ul><h4>Collaborative Filtering and Recommendation Systems</h4><ul><li>Overview of recommendation systems</li><li>Collaborative filtering techniques</li><li>Implementing recommendations with Spark MLlib</li></ul><h4>Introduction to Graph Processing</h4><ul><li>Overview of graph processing</li><li>Use cases and applications of graph processing</li><li>Graph representations and operations</li><li>Introduction to Spark GraphX</li><li>Overview of GraphX</li><li>Creating and transforming graphs</li><li>Graph algorithms in GraphX</li></ul><h4>Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala</h4><ul><li>Overview of generative AI technologies</li><li>Integrating GPT with Spark and Scala</li><li>Practical applications and use cases Bonus Topics / Time Permitting</li></ul><h4>Introduction to Spark NLP</h4><ul><li>Overview of Spark NLP Preprocessing text data</li><li>Text classification and sentiment analysis</li></ul><h4>Putting It All Together</h4><ul><li>Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.</li></ul></div>

Pyspark

Data Science

Mastering Scala with Apache Spark for the Modern Data Enterprise (TTSK7520)

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>Senior Executives<br />CIOs and CTOs<br />Business Intelligence Executives<br />Marketing Executives<br />Data &amp; Business Analytics Specialists<br />Innovation Specialists &amp; Entrepreneurs<br />Academics, and other people interested in Big Data</p>Overview<p>More specifically, BDAW addresses advanced big data architecture topics, including, data formats, transformation, real-time, batch and machine learning processing, scalability, fault tolerance, security and privacy, minimizing the risk of an unsound architecture and technology selection.</p><p>Big Data Architecture Workshop (BDAW) is a learning event that addresses advanced big data architecture topics. BDAW brings together technical contributors into a group setting to design and architect solutions to a challenging business problem. The workshop addresses big data architecture problems in general, and then applies them to the design of a challenging system. Throughout the highly interactive workshop, students apply concepts to real-world examples resulting in detailed synergistic discussions. The workshop is conducive for students to learn techniques for architecting big data systems, not only from Cloudera?s experience but also from the experiences of fellow students.</p></div><div id="course-pills-2"><h4>Workshop Application Use Cases</h4><ul><li>Oz Metropolitan</li><li>Architectural questions</li><li>Team activity: Analyze Metroz Application Use Cases</li></ul><h4>Application Vertical Slice</h4><ul><li>Definition</li><li>Minimizing risk of an unsound architecture</li><li>Selecting a vertical slice</li><li>Team activity: Identify an initial vertical slice for Metroz</li></ul><h4>Application Processing</h4><ul><li>Real time, near real time processing</li><li>Batch processing</li><li>Data access patterns</li><li>Delivery and processing guarantees</li><li>Machine Learning pipelines</li><li>Team activity: identify delivery and processing patterns in Metroz, characterize response time requirements, identify Machine Learning pipelines</li></ul><h4>Application Data</h4><ul><li>Three V?s of Big Data</li><li>Data Lifecycle</li><li>Data Formats</li><li>Transforming Data</li><li>Team activity: Metroz Data Requirements</li></ul><h4>Scalable Applications</h4><ul><li>Scale up, scale out, scale to X<br />Determining if an application will scale<br />Poll: scalable airport terminal designs<br />Hadoop and Spark Scalability<br />Team activity: Scaling Metroz</li></ul><h4>Fault Tolerant Distributed Systems</h4><ul><li>Principles<br />Transparency<br />Hardware vs. Software redundancy<br />Tolerating disasters<br />Stateless functional fault tolerance<br />Stateful fault tolerance<br />Replication and group consistency<br />Fault tolerance in Spark and Map Reduce<br />Application tolerance for failures<br />Team activity: Identify Metroz component failures and requirements</li></ul><h4>Security and Privacy</h4><ul><li>Principles</li><li>Privacy</li><li>Threats</li><li>Technologies</li><li>Team activity: identify threats and security mechanisms in Metroz</li></ul><h4>Deployment</h4><ul><li>Cluster sizing and evolution</li><li>On-premise vs. Cloud</li><li>Edge computing</li><li>Team activity: select deployment for Metroz</li></ul><h4>Technology Selection</h4><ul><li>HDFS</li><li>HBase</li><li>Kudu</li><li>Relational Database Management Systems</li><li>Map Reduce</li><li>Spark, including streaming, SparkSQL and SparkML</li><li>Hive</li><li>Impala</li><li>Cloudera Search</li><li>Data Sets and Formats</li><li>Team activity: technologies relevant to Metroz</li></ul><h4>Software Architecture</h4><ul><li>Architecture artifacts</li><li>One platform or multiple, lambda architecture</li><li>Team activity: produce high level architecture, selected technologies, revisit vertical slice</li><li>Vertical Slice demonstration</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Big Data Architecture Workshop training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Big Data Architecture Workshop course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Cloud Architecture

Big Data

Big Data Architecture Workshop

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.</p>Overview<p>By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.</p><p>In this course you will start with the absolute basics of Python, focusing mainly on data structures.  Then you will delve into the fundamental tools of data wrangling like NumPy and Pandas libraries.  You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python.This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables.  To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets.</p></div><div id="course-pills-2"><h4>Introduction to Data Structure using Python</h4><ul><li>Python for Data Wrangling</li><li>Lists, Sets, Strings, Tuples, and Dictionaries</li></ul><h4>Advanced Operations on Built-In Data Structure</h4><ul><li>Advanced Data Structures</li><li>Basic File Operations in Python</li></ul><h4>Introduction to NumPy, Pandas, and Matplotlib</h4><ul><li>NumPy Arrays</li><li>Pandas DataFrames</li><li>Statistics and Visualization with NumPy and Pandas</li><li>Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame</li></ul><h4>Deep Dive into Data Wrangling with Python</h4><ul><li>Subsetting, Filtering, and Grouping</li><li>Detecting Outliers and Handling Missing Values</li><li>Concatenating, Merging, and Joining</li><li>Useful Methods of Pandas</li></ul><h4>Get Comfortable with a Different Kind of Data Sources</h4><ul><li>Reading Data from Different Text-Based (and Non-Text-Based) Sources</li><li>Introduction to BeautifulSoup4 and Web Page Parsing</li></ul><h4>Learning the Hidden Secrets of Data Wrangling</h4><ul><li>Advanced List Comprehension and the zip Function</li><li>Data Formatting</li></ul><h4>Advanced Web Scraping and Data Gathering</h4><ul><li>Basics of Web Scraping and BeautifulSoup libraries</li><li>Reading Data from XML</li></ul><h4>RDBMS and SQL</h4><ul><li>Refresher of RDBMS and SQL</li><li>Using an RDBMS (MySQL/PostgreSQL/SQLite)</li></ul><h4>Application in real life and Conclusion of course</h4><ul><li>Applying Your Knowledge to a Real-life Data Wrangling Task</li><li>An Extension to Data Wrangling</li></ul></div>

Data Analyst

Data Wrangling with Python

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators.</p>Overview<p>Skills gained in this training include:The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysisThe fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with HadoopHow Pig, Hive, and Impala improve productivity for typical analysis tasksJoining diverse datasets to gain valuable business insightPerforming real-time, complex queries on datasets</p><p>Cloudera University?s four-day data analyst training course focusing on Apache Pig and Hive and Cloudera Impala will teach you to apply traditional data analytics and business intelligence skills to big data.</p></div><div id="course-pills-2"><h4>Hadoop Fundamentals</h4><ul><li>The Motivation for Hadoop</li><li>Hadoop Overview</li><li>Data Storage: HDFS</li><li>Distributed Data Processing: YARN, MapReduce, and Spark</li><li>Data Processing and Analysis: Pig, Hive, and Impala</li><li>Data Integration: Sqoop</li><li>Other Hadoop Data Tools</li><li>Exercise Scenarios Explanation</li></ul><h4>Introduction to Pig</h4><ul><li>What Is Pig?</li><li>Pig?s Features</li><li>Pig Use Cases</li><li>Interacting with Pig</li></ul><h4>Basic Data Analysis with Pig</h4><ul><li>Pig Latin Syntax</li><li>Loading Data</li><li>Simple Data Types</li><li>Field Definitions</li><li>Data Output</li><li>Viewing the Schema</li><li>Filtering and Sorting Data</li><li>Commonly-Used Functions</li></ul><h4>Processing Complex Data with Pig</h4><ul><li>Storage Formats</li><li>Complex/Nested Data Types</li><li>Grouping</li><li>Built-In Functions for Complex Data</li><li>Iterating Grouped Data</li></ul><h4>Multi-Dataset Operations with Pig</h4><ul><li>Techniques for Combining Data Sets</li><li>Joining Data Sets in Pig</li><li>Set Operations</li><li>Splitting Data Sets</li></ul><h4>Pig Troubleshoot &amp; Optimization</h4><ul><li>Troubleshooting Pig</li><li>Logging</li><li>Using Hadoop?s Web UI</li><li>Data Sampling and Debugging</li><li>Performance Overview</li><li>Understanding the Execution Plan</li><li>Tips for Improving the Performance of Your Pig Jobs</li></ul><h4>Introduction to Hive &amp; Impala</h4><ul><li>What Is Hive?</li><li>What Is Impala?</li><li>Schema and Data Storage</li><li>Comparing Hive to Traditional Databases</li><li>Hive Use Cases</li></ul><h4>Querying with Hive &amp; Impala</h4><ul><li>Databases and Tables</li><li>Basic Hive and Impala Query Language Syntax</li><li>Data Types</li><li>Differences Between Hive and Impala Query Syntax</li><li>Using Hue to Execute Queries</li><li>Using the Impala Shell</li></ul><h4>Data Management</h4><ul><li>Data Storage</li><li>Creating Databases and Tables</li><li>Loading Data</li><li>Altering Databases and Tables</li><li>Simplifying Queries with Views</li><li>Storing Query Results</li></ul><h4>Data Storage &amp; Performance</h4><ul><li>Partitioning Tables</li><li>Choosing a File Format</li><li>Managing Metadata</li><li>Controlling Access to Data</li></ul><h4>Relational Data Analysis with Hive &amp; Impala</h4><ul><li>Joining Datasets</li><li>Common Built-In Functions</li><li>Aggregation and Windowing</li></ul><h4>Working with Impala</h4><ul><li>How Impala Executes Queries</li><li>Extending Impala with User-Defined Functions</li><li>Improving Impala Performance</li></ul><h4>Analyzing Text and Complex Data with Hive</h4><ul><li>Complex Values in Hive</li><li>Using Regular Expressions in Hive</li><li>Sentiment Analysis and N-Grams</li><li>Conclusion</li></ul><h4>Hive Optimization</h4><ul><li>Understanding Query Performance</li><li>Controlling Job Execution Plan</li><li>Bucketing</li><li>Indexing Data</li></ul><h4>Extending Hive</h4><ul><li>SerDes</li><li>Data Transformation with Custom Scripts</li><li>User-Defined Functions</li><li>Parameterized Queries</li></ul><h4>Choosing the Best Tool for the Job</h4><ul><li>Comparing MapReduce, Pig, Hive, Impala, and Relational Databases</li><li>Which to Choose?</li></ul></div>

Business Analytics & Intelligence

Cloudera Data Analyst Training - Using Pig, Hive, and Impala with Hadoop

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.</p>Overview<p>By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data.</p><p>This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive.<br /><br />You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions.</p></div><div id="course-pills-2"><h4>Data Exploration and Cleaning</h4><ul><li>Python and the Anaconda Package Management System</li><li>Different Types of Data Science Problems</li><li>Loading the Case Study Data with Jupyter and pandas</li><li>Data Quality Assurance and Exploration</li><li>Exploring the Financial History Features in the Dataset</li><li>Activity 1: Exploring Remaining Financial Features in the Dataset</li></ul><h4>Introduction to Scikit-Learn and Model Evaluation</h4><ul><li>Introduction</li><li>Model Performance Metrics for Binary Classification</li><li>Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve</li></ul><h4>Details of Logistic Regression and Feature Exploration</h4><ul><li>Introduction</li><li>Examining the Relationships between Features and the Response</li><li>Univariate Feature Selection: What It Does and Doesn't Do</li><li>Building Cloud-Native Applications</li><li>Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients</li></ul><h4>The Bias-Variance Trade-off</h4><ul><li>Introduction</li><li>Estimating the Coefficients and Intercepts of Logistic Regression</li><li>Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters</li><li>Activity 4: Cross-Validation and Feature Engineering with the Case Study Data</li></ul><h4>Decision Trees and Random Forests</h4><ul><li>Introduction</li><li>Decision trees</li><li>Random Forests: Ensembles of Decision Trees</li><li>Activity 5: Cross-Validation Grid Search with Random Forest</li></ul><h4>Imputation of Missing Data, Financial Analysis, and Delivery to Client</h4><ul><li>Introduction</li><li>Review of Modeling Results</li><li>Dealing with Missing Data: Imputation Strategies</li><li>Activity 6: Deriving Financial Insights</li><li>Final Thoughts on Delivering the Predictive Model to the Client</li></ul></div>

Predictive Analytics

Data Mining

Data Science Projects with Python

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming<br />Also familiar with Python</p>Overview<p>? NumPy, pandas, Matplotlib,  scikit-learn<br />? Python REPLs<br />? Jupyter Notebooks<br />? Data analytics life-cycle phases<br />? Data repairing and normalizing<br />? Data aggregation and grouping<br />? Data visualization<br />? Data science algorithms for supervised and unsupervised machine learning</p><p>Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases.</p></div><div id="course-pills-2"><h4>Python for Data Science</h4><ul><li>? Using Modules</li><li>? Listing Methods in a Module</li><li>? Creating Your Own Modules</li><li>? List Comprehension</li><li>? Dictionary Comprehension</li><li>? String Comprehension</li><li>? Python 2 vs Python 3</li><li>? Sets (Python 3+)</li><li>? Python Idioms</li><li>? Python Data Science ?Ecosystem?</li><li>? NumPy</li><li>? NumPy Arrays</li><li>? NumPy Idioms</li><li>? pandas</li><li>? Data Wrangling with pandas' DataFrame</li><li>? SciPy</li><li>? Scikit-learn</li><li>? SciPy or scikit-learn?</li><li>? Matplotlib</li><li>? Python vs R</li><li>? Python on Apache Spark</li><li>? Python Dev Tools and REPLs</li><li>? Anaconda</li><li>? IPython</li><li>? Visual Studio Code</li><li>? Jupyter</li><li>? Jupyter Basic Commands</li><li>? Summary</li></ul><h4>Applied Data Science</h4><ul><li>? What is Data Science?</li><li>? Data Science Ecosystem</li><li>? Data Mining vs. Data Science</li><li>? Business Analytics vs. Data Science</li><li>? Data Science, Machine Learning, AI?</li><li>? Who is a Data Scientist?</li><li>? Data Science Skill Sets Venn Diagram</li><li>? Data Scientists at Work</li><li>? Examples of Data Science Projects</li><li>? An Example of a Data Product</li><li>? Applied Data Science at Google</li><li>? Data Science Gotchas</li><li>? Summary</li></ul><h4>Data Analytics Life-cycle Phases</h4><ul><li>? Big Data Analytics Pipeline</li><li>? Data Discovery Phase</li><li>? Data Harvesting Phase</li><li>? Data Priming Phase</li><li>? Data Logistics and Data Governance</li><li>? Exploratory Data Analysis</li><li>? Model Planning Phase</li><li>? Model Building Phase</li><li>? Communicating the Results</li><li>? Production Roll-out</li><li>? Summary</li></ul><h4>Repairing and Normalizing Data</h4><ul><li>? Repairing and Normalizing Data</li><li>? Dealing with the Missing Data</li><li>? Sample Data Set</li><li>? Getting Info on Null Data</li><li>? Dropping a Column</li><li>? Interpolating Missing Data in pandas</li><li>? Replacing the Missing Values with the Mean Value</li><li>? Scaling (Normalizing) the Data</li><li>? Data Preprocessing with scikit-learn</li><li>? Scaling with the scale() Function</li><li>? The MinMaxScaler Object</li><li>? Summary</li></ul><h4>Descriptive Statistics Computing Features in Python</h4><ul><li>? Descriptive Statistics</li><li>? Non-uniformity of a Probability Distribution</li><li>? Using NumPy for Calculating Descriptive Statistics Measures</li><li>? Finding Min and Max in NumPy</li><li>? Using pandas for Calculating Descriptive Statistics Measures</li><li>? Correlation</li><li>? Regression and Correlation</li><li>? Covariance</li><li>? Getting Pairwise Correlation and Covariance Measures</li><li>? Finding Min and Max in pandas DataFrame</li><li>? Summary</li></ul><h4>Data Aggregation and Grouping</h4><ul><li>? Data Aggregation and Grouping</li><li>? Sample Data Set</li><li>? The pandas.core.groupby.SeriesGroupBy Object</li><li>? Grouping by Two or More Columns</li><li>? Emulating the SQL's WHERE Clause</li><li>? The Pivot Tables</li><li>? Cross-Tabulation</li><li>? Summary</li></ul><h4>Data Visualization with matplotlib</h4><ul><li>? Data Visualization<br />? What is matplotlib?<br />? Getting Started with matplotlib<br />? The Plotting Window<br />? The Figure Options<br />? The matplotlib.pyplot.plot() Function<br />? The matplotlib.pyplot.bar() Function<br />? The matplotlib.pyplot.pie () Function<br />? Subplots<br />? Using the matplotlib.gridspec.GridSpec Object<br />? The matplotlib.pyplot.subplot() Function<br />? Hands-on Exercise<br />? Figures<br />? Saving Figures to File<br />? Visualization with pandas<br />? Working with matplotlib in Jupyter Notebooks<br />? Summary</li></ul><h4>Data Science and ML Algorithms in scikit-learn</h4><ul><li>? Data Science, Machine Learning, AI?</li><li>? Types of Machine Learning</li><li>? Terminology: Features and Observations</li><li>? Continuous and Categorical Features (Variables)</li><li>? Terminology: Axis</li><li>? The scikit-learn Package</li><li>? scikit-learn Estimators</li><li>? Models, Estimators, and Predictors</li><li>? Common Distance Metrics</li><li>? The Euclidean Metric</li><li>? The LIBSVM format</li><li>?  Scaling of the Features</li><li>? The Curse of Dimensionality</li><li>? Supervised vs Unsupervised Machine Learning</li><li>? Supervised Machine Learning Algorithms</li><li>? Unsupervised Machine Learning Algorithms</li><li>? Choose the Right Algorithm</li><li>? Life-cycles of Machine Learning Development</li><li>? Data Split for Training and Test Data Sets</li><li>? Data Splitting in scikit-learn</li><li>? Hands-on Exercise</li><li>? Classification Examples</li><li>? Classifying with k-Nearest Neighbors (SL)</li><li>? k-Nearest Neighbors Algorithm</li><li>? k-Nearest Neighbors Algorithm</li><li>? The Error Rate</li><li>? Hands-on Exercise</li><li>? Dimensionality Reduction</li><li>? The Advantages of Dimensionality Reduction</li><li>? Principal component analysis (PCA)</li><li>? Hands-on Exercise</li><li>? Data Blending</li><li>? Decision Trees (SL)</li><li>? Decision Tree Terminology</li><li>? Decision Tree Classification in Context of Information Theory</li><li>? Information Entropy Defined</li><li>? The Shannon Entropy Formula</li><li>? The Simplified Decision Tree Algorithm</li><li>? Using Decision Trees</li><li>? Random Forests</li><li>? SVM</li><li>? Naive Bayes Classifier (SL)</li><li>? Naive Bayesian Probabilistic Model in a Nutshell</li><li>? Bayes Formula</li><li>? Classification of Documents with Naive Bayes</li><li>? Unsupervised Learning Type: Clustering</li><li>? Clustering Examples</li><li>? k-Means Clustering (UL)</li><li>? k-Means Clustering in a Nutshell</li><li>? k-Means Characteristics</li><li>? Regression Analysis</li><li>? Simple Linear Regression Model</li><li>? Linear vs Non-Linear Regression</li><li>? Linear Regression Illustration</li><li>? Major Underlying Assumptions for Regression Analysis</li><li>? Least-Squares Method (LSM)</li><li>? Locally Weighted Linear Regression</li><li>? Regression Models in Excel</li><li>? Multiple Regression Analysis</li><li>? Logistic Regression</li><li>? Regression vs Classification</li><li>? Time-Series Analysis</li><li>? Decomposing Time-Series</li><li>? Summary</li></ul><h4>Lab Exercises</h4><ul><li>Lab 1 - Learning the Lab Environment     </li><li>Lab 2 -  Using Jupyter Notebook </li><li>Lab 3 -  Repairing and Normalizing Data  </li><li>Lab 4 -  Computing Descriptive Statistics </li><li>Lab 5 - Data Grouping and Aggregation   </li><li>Lab 6 - Data Visualization with matplotlib              </li><li>Lab 7 - Data Splitting       </li><li>Lab 8 -  k-Nearest Neighbors Algorithm  </li><li>Lab 9 -  The k-means Algorithm  </li><li>Lab 10 -  The Random Forest Algorithm </li></ul></div>

Machine Learning (ML)

Python With Data Science

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course includes data analysts and data scientists who work with analytical solutions built on Microsoft Azure.</p><p></p><p></p><p>In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage.</p><p></p>Prerequisites<p></p><p>Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.</p>  <li>AZ-900T00 Microsoft Azure Fundamentals</li>  <li>DP-900T00 Microsoft Azure Data Fundamentals</li><p></p></div><div id="course-pills-2"><h3>1 - Introduction to data engineering on Azure</h3> <ul> <li>What is data engineering</li> <li>Important data engineering concepts</li> <li>Data engineering in Microsoft Azure</li> </ul> <h3>2 - Introduction to Azure Data Lake Storage Gen2</h3> <ul> <li>Understand Azure Data Lake Storage Gen2</li> <li>Enable Azure Data Lake Storage Gen2 in Azure Storage</li> <li>Compare Azure Data Lake Store to Azure Blob storage</li> <li>Understand the stages for processing big data</li> <li>Use Azure Data Lake Storage Gen2 in data analytics workloads</li> </ul> <h3>3 - Introduction to Azure Synapse Analytics</h3> <ul> <li>What is Azure Synapse Analytics</li> <li>How Azure Synapse Analytics works</li> <li>When to use Azure Synapse Analytics</li> </ul> <h3>4 - Use Azure Synapse serverless SQL pool to query files in a data lake</h3> <ul> <li>Understand Azure Synapse serverless SQL pool capabilities and use cases</li> <li>Query files using a serverless SQL pool</li> <li>Create external database objects</li> </ul> <h3>5 - Use Azure Synapse serverless SQL pools to transform data in a data lake</h3> <ul> <li>Transform data files with the CREATE EXTERNAL TABLE AS SELECT statement</li> <li>Encapsulate data transformations in a stored procedure</li> <li>Include a data transformation stored procedure in a pipeline</li> </ul> <h3>6 - Create a lake database in Azure Synapse Analytics</h3> <ul> <li>Understand lake database concepts</li> <li>Explore database templates</li> <li>Create a lake database</li> <li>Use a lake database</li> </ul> <h3>7 - Analyze data with Apache Spark in Azure Synapse Analytics</h3> <ul> <li>Get to know Apache Spark</li> <li>Use Spark in Azure Synapse Analytics</li> <li>Analyze data with Spark</li> <li>Visualize data with Spark</li> </ul> <h3>8 - Transform data with Spark in Azure Synapse Analytics</h3> <ul> <li>Modify and save dataframes</li> <li>Partition data files</li> <li>Transform data with SQL</li> </ul> <h3>9 - Use Delta Lake in Azure Synapse Analytics</h3> <ul> <li>Understand Delta Lake</li> <li>Create Delta Lake tables</li> <li>Create catalog tables</li> <li>Use Delta Lake with streaming data</li> <li>Use Delta Lake in a SQL pool</li> </ul> <h3>10 - Analyze data in a relational data warehouse</h3> <ul> <li>Design a data warehouse schema</li> <li>Create data warehouse tables</li> <li>Load data warehouse tables</li> <li>Query a data warehouse</li> </ul> <h3>11 - Load data into a relational data warehouse</h3> <ul> <li>Load staging tables</li> <li>Load dimension tables</li> <li>Load time dimension tables</li> <li>Load slowly changing dimensions</li> <li>Load fact tables</li> <li>Perform post load optimization</li> </ul> <h3>12 - Build a data pipeline in Azure Synapse Analytics</h3> <ul> <li>Understand pipelines in Azure Synapse Analytics</li> <li>Create a pipeline in Azure Synapse Studio</li> <li>Define data flows</li> <li>Run a pipeline</li> </ul> <h3>13 - Use Spark Notebooks in an Azure Synapse Pipeline</h3> <ul> <li>Understand Synapse Notebooks and Pipelines</li> <li>Use a Synapse notebook activity in a pipeline</li> <li>Use parameters in a notebook</li> </ul> <h3>14 - Plan hybrid transactional and analytical processing using Azure Synapse Analytics</h3> <ul> <li>Understand hybrid transactional and analytical processing patterns</li> <li>Describe Azure Synapse Link</li> </ul> <h3>15 - Implement Azure Synapse Link with Azure Cosmos DB</h3> <ul> <li>Enable Cosmos DB account to use Azure Synapse Link</li> <li>Create an analytical store enabled container</li> <li>Create a linked service for Cosmos DB</li> <li>Query Cosmos DB data with Spark</li> <li>Query Cosmos DB with Synapse SQL</li> </ul> <h3>16 - Implement Azure Synapse Link for SQL</h3> <ul> <li>What is Azure Synapse Link for SQL?</li> <li>Configure Azure Synapse Link for Azure SQL Database</li> <li>Configure Azure Synapse Link for SQL Server 2022</li> </ul> <h3>17 - Get started with Azure Stream Analytics</h3> <ul> <li>Understand data streams</li> <li>Understand event processing</li> <li>Understand window functions</li> </ul> <h3>18 - Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics</h3> <ul> <li>Stream ingestion scenarios</li> <li>Configure inputs and outputs</li> <li>Define a query to select, filter, and aggregate data</li> <li>Run a job to ingest data</li> </ul> <h3>19 - Visualize real-time data with Azure Stream Analytics and Power BI</h3> <ul> <li>Use a Power BI output in Azure Stream Analytics</li> <li>Create a query for real-time visualization</li> <li>Create real-time data visualizations in Power BI</li> </ul> <h3>20 - Introduction to Microsoft Purview</h3> <ul> <li>What is Microsoft Purview?</li> <li>How Microsoft Purview works</li> <li>When to use Microsoft Purview</li> </ul> <h3>21 - Integrate Microsoft Purview and Azure Synapse Analytics</h3> <ul> <li>Catalog Azure Synapse Analytics data assets in Microsoft Purview</li> <li>Connect Microsoft Purview to an Azure Synapse Analytics workspace</li> <li>Search a Purview catalog in Synapse Studio</li> <li>Track data lineage in pipelines</li> </ul> <h3>22 - Explore Azure Databricks</h3> <ul> <li>Get started with Azure Databricks</li> <li>Identify Azure Databricks workloads</li> <li>Understand key concepts</li> </ul> <h3>23 - Use Apache Spark in Azure Databricks</h3> <ul> <li>Get to know Spark</li> <li>Create a Spark cluster</li> <li>Use Spark in notebooks</li> <li>Use Spark to work with data files</li> <li>Visualize data</li> </ul> <h3>24 - Run Azure Databricks Notebooks with Azure Data Factory</h3> <ul> <li>Understand Azure Databricks notebooks and pipelines</li> <li>Create a linked service for Azure Databricks</li> <li>Use a Notebook activity in a pipeline</li> <li>Use parameters in a notebook</li> </ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans DP-203T00 Data Engineering on Microsoft Azure training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the DP-203T00 Data Engineering on Microsoft Azure course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Microsoft AZ-900

Microsoft AZ-104

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

DP-203T00 Data Engineering on Microsoft Azure

General Admission

<p><strong>24 Hour Flash Deal **25-in-1 </strong><strong>Data Engineering Advanced Diploma </strong><strong>Mega Bundle**</strong></p><p><strong> </strong><strong>Data Engineering Advanced Diploma </strong><strong>Enrolment Gifts  **FREE PDF Certificate**FREE PDF Transcript ** FREE Exam** FREE Student ID ** Lifetime Access **FREE Enrolment Letter **</strong></p><p>Take the initial steps toward a successful long-term career by studying the <strong>Data Engineering Advanced Diploma</strong> package online with <strong>Studyhub </strong>through our online learning platform. The Data Engineering Advanced Diploma bundle can help you improve your CV, wow potential employers, and differentiate yourself from the mass.</p><p>This <strong>Data Engineering Advanced Diploma </strong>course provides complete 360-degree training on Data Engineering Advanced Diploma. You'll get not one, not two, not three, but <strong>twenty-five </strong>Data Engineering Advanced Diploma courses included in this course. Plus Studyhub's signature <strong>Forever Access</strong> is given as always, meaning these Data Engineering Advanced Diploma courses are yours for as long as you want them once you enrol in this course</p><p><strong>This Data Engineering Advanced Diploma Bundle consists the following career oriented courses:</strong></p><ul><li>Course 01: Data Analysis</li><li>Course 02: Data Science &amp; Machine Learning with R</li><li>Course 03: Data Analytics with Tableau</li><li>Course 04: SQL Programming</li><li>Course 05: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines</li><li>Course 06: Data Analytics</li><li>Course 07: Python Data Science with Numpy, Pandas and Matplotlib</li><li>Course 08: Google Analytics</li><li>Course 09: Business Intelligence and Data Mining</li><li>Course 10: Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query</li><li>Course 11: JavaScript Functions</li><li>Course 12: Data Protection and Data Security (GDPR) Training Certification</li><li>Course 13: GDPR Training</li><li>Course 14: Diploma in Python Programming</li><li>Course 15: Data Center Training Essentials: General Introduction</li><li>Course 16: Quick Data Science Approach from Scratch</li><li>Course 17: Diploma in Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li>Course 18: Master JavaScript with Data Visualization</li><li>Course 19: Data Analysis and Forecasting in Excel</li><li>Course 20: AWS Certified Solutions Architect Associate Preparation</li><li>Course 21: Statistical Analysis Course</li><li>Course 22: SQL for Data Science, Data Analytics and Data Visualization</li><li>Course 23: Learn MySQL from Scratch for Data Science and Analytics</li><li>Course 24: Data Analysis &amp; Forecasting</li><li>Course 25: Data Structure</li></ul> <p>The <strong>Data Engineering Advanced Diploma </strong>course has been prepared by focusing largely on Data Engineering Advanced Diploma career readiness. It has been designed by our Data Engineering Advanced Diploma specialists in a manner that you will be likely to find yourself head and shoulders above the others. For better learning, one to one assistance will also be provided if it's required by any learners.</p><p>The Data Engineering Advanced Diploma Bundle is one of the most prestigious training offered at StudyHub and is highly valued by employers for good reason. This Data Engineering Advanced Diploma bundle course has been created with <strong>twenty-five </strong>premium courses to provide our learners with the best learning experience possible to increase their understanding of their chosen field.</p><p>This Data Engineering Advanced Diploma Course, like every one of Study Hub's courses, is meticulously developed and well researched. Every one of the topics is divided into Data Engineering Advanced Diploma Elementary modules, allowing our students to grasp each lesson quickly.</p><p>The Data Engineering Advanced Diploma course is self-paced and can be taken from the comfort of your home, office, or on the go! With our Student ID card you will get discounts on things like music, food, travel and clothes etc.</p><p><strong>In this exclusive </strong><strong>Data Engineering Advanced Diploma </strong><strong>bundle, you really hit the jackpot. Here's what you get:</strong></p><ul><li>Step by step Data Engineering Advanced Diploma lessons</li><li>One to one assistance from Data Engineering Advanced Diplomaprofessionals if you need it</li><li>Innovative exams to test your knowledge after the Data Engineering Advanced Diplomacourse</li><li>24/7 customer support should you encounter any hiccups</li><li>Top-class learning portal</li><li>Unlimited lifetime access to all twenty-five Data Engineering Advanced Diploma courses</li><li>Digital Certificate, Transcript and student ID are all included in the price</li><li>PDF certificate immediately after passing</li><li>Original copies of your Data Engineering Advanced Diploma certificate and transcript on the next working day</li><li>Easily learn the Data Engineering Advanced Diploma skills and knowledge you want from the comfort of your home</li></ul> <h2>
        
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                            <div>
                                <div>
                                    <div>
                                        250 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This <strong>Data Engineering Advanced Diploma </strong>training is suitable for -</p><ul><li>Students</li><li>Recent graduates</li><li>Job Seekers</li><li>Individuals who are already employed in the relevant sectors and wish to enhance their knowledge and expertise in Data Engineering Advanced Diploma</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>To participate in this <strong>Data Engineering Advanced Diploma </strong>course, all you need is -</p><ul><li>A smart device</li><li>A secure internet connection</li><li>And a keen interest in Data Engineering Advanced Diploma</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>You will be able to kickstart your <strong>Data Engineering Advanced Diploma </strong>career because this course includes various courses as a bonus. This Data Engineering Advanced Diploma is an excellent opportunity for you to learn multiple skills from the convenience of your own home and explore Data Engineering Advanced Diploma career opportunities.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><ul><li>CPD Accredited e-Certificate - Free</li><li>CPD Accredited Hardcopy Certificate - Free</li><li>Enrolment Letter - Free</li><li>Student ID Card - Free</li></ul></div>

                        </div>

Studyhub UK

Diploma in Education & Training (DET)

European Computer Driving Licence (ECDL)

Diploma in Management Studies (DMS)

Diploma in Professional Studies (DPS)

Nursing Diploma

Data Engineering Advanced Diploma - CPD Certified

<p><strong>24 Hours Left! Don't Let Winter Sale Slip Away - Enrol Now!</strong></p><p><strong>With over 100,000 civil engineers in the UK, the demand for data-savvy professionals is high. According to a recent study, 72% of UK civil engineering firms are investing in data analytics to enhance decision-making processes, with data engineering experiencing a 40% year-on-year growth.</strong></p><p>This surge outpaces other tech sectors, highlighting the critical role of data engineers in big data and advanced analytics. This Data Engineering bundle empowers you to capitalise on this growing trend, equipping you with the proficiency to extract valuable insights from vast datasets, optimise project planning, and enhance overall efficiency.</p><p>Embark on a transformative journey with our Data Engineering Bundle, thoroughly crafted to bridge the skill gap in one of the UK's fastest-growing job markets. Our bundle offers a comprehensive exploration into the world of data, equipping you with the analytical prowess to harness, process, and visualise data effectively. From mastering SQL programming to delving into the complexities of machine learning with R, this bundle is your gateway to becoming a data engineering maestro.</p><p>In today's data-driven landscape, possessing a robust set of analytical skills is no longer a luxury but a necessity. With an emphasis on Data engineering, our courses are designed to foster a deep understanding of data analytics, visualisation, and mining, ensuring you can convert raw data into strategic insights that propel business decisions.</p><p>Seize the opportunity to be at the helm of innovation and become the architect of tomorrow's data landscape with our Data Engineering Bundle. Join us now!</p><p><strong>Courses are Included in This Data Engineering Bundle:</strong></p><ul><li><strong> Course 01: </strong>Introduction to Data Analysis</li><li><strong> Course 02: </strong>Data Science &amp; Machine Learning with R</li><li><strong> Course 03: </strong>Data Analytics with Tableau</li><li><strong> Course 04: </strong>SQL Programming Masterclass</li><li><strong> Course 05: </strong>Excel Pivot table for Data analytics</li><li><strong> Course 06: </strong>Data analytics with Excel</li><li><strong> Course 07: </strong>Python for Data Analysis</li><li><strong> Course 08: </strong>Basic Google Data Studio</li><li><strong> Course 09: </strong>Business Intelligence and Data Mining Masterclass</li><li><strong> Course 10: </strong>Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query</li><li><strong> Course 11: </strong>JavaScript Functions for Data Analyst</li><li><strong> Course 12: </strong>GDPR Data Protection Level 5</li><li><strong> Course 13: </strong>GDPR Training</li><li><strong> Course 14: </strong>Learn Programming with Python</li><li><strong> Course 15: </strong>Data Center Training Essentials: General Introduction</li><li><strong> Course 16: </strong>Quick Data Science Approach from Scratch</li><li><strong> Course 17: </strong>Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong> Course 18: </strong>Master JavaScript with Data Visualization</li><li><strong> Course 19: </strong>Excel Data Tools and Data Management</li><li><strong> Course 20: </strong>Cloud Computing Essentials for Data Analysis</li></ul><p><strong>Learning Outcomes of Data Engineering Bundle:</strong></p><ul><li>Gain proficiency in SQL for complex database management and analytics tasks.</li><li>Master R for data science and machine learning applications in various industries.</li><li>Develop advanced Excel skills, including data analytics and pivot table expertise.</li><li>Acquire hands-on experience with Tableau for effective data visualisation.</li><li>Learn Python programming essentials for data analysis and engineering solutions.</li><li>Understand Google Data Studio to create dynamic reports and dashboards.</li></ul><p><strong>Why Choose Our Data Engineering Course?</strong></p><ul><li>FREE Data Engineering certificate accredited</li><li>Get a free student ID card with Data Engineering Training</li><li>Get instant access to this Data Engineering course.</li><li>Learn Data Engineering from anywhere in the world</li><li>The Data Engineering is affordable and simple to understand</li><li>The Data Engineering is an entirely online, interactive lesson with voiceover audio</li><li>Lifetime access to the Data Engineering course materials</li><li>The Data Engineering comes with 24/7 tutor support</li></ul> <p><strong>Start your learning journey straightaway with our Data Engineering Bundle!</strong></p><p>This Data Engineering bundle will equip you with the skills and knowledge to become a data-savvy civil engineer. You will learn how to collect, analyze, and interpret data to improve infrastructure projects and processes. This Data Engineering bundle includes courses on data analysis, data science, machine learning, SQL programming, Excel pivot tables, Excel data analytics, Python for data analysis, Google Data Studio, business intelligence, data mining, GDPR data protection, JavaScript functions for data analysts, and Python programming.</p><p><strong>Assessment Process of Data Engineering</strong></p><p>Once you have completed all the courses in the Data Engineering bundle, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        200 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This Data Engineering bundle is suitable for everyone. Also, our Data Engineering Bundle is perfect for:</p><ul><li>Aspiring data engineers</li><li>Business intelligence analysts</li><li>Data-driven marketers</li><li>IT professionals</li><li>Statistics enthusiasts</li><li>Career changers</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You will not need any prior background or expertise in the Data Engineering Bundle.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This Data Engineering bundle will allow you to kickstart or take your career to the next stage in the related sector such as:</p><ul><li><strong>Data Analyst</strong></li><li><strong>BI Developer</strong></li><li><strong>ML Engineer</strong></li><li><strong>Data Scientist</strong></li><li><strong>Database Administrator</strong></li><li><strong>Data Strategist</strong></li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>If you are an <strong>international student</strong>, then you have to pay an additional <strong>10 GBP</strong> for each certificate as an international delivery charge.</p></div>

                        </div>

Academy for Health and Fitness

Data Engineering - CPD Certified

<p>If you aim to enhance your Data Engineering skills, our comprehensive Data Engineering course is perfect for you. Designed for success, this Data Engineering course covers everything from basics to advanced topics in Data Engineering.</p><p>Each lesson in this Data Engineering course is crafted for easy understanding, enabling you to become proficient in Data Engineering. Whether you are a beginner or looking to sharpen your existing skills, this Data Engineering is the ideal choice.</p><p>With our Data Engineering exclusive bundle, you will get<strong> a PDF Certificate, PDF Transcript and Digital Student ID Card (worth £50) Absolutely FREE. </strong></p><p><strong>Courses are Included in This Data Engineering Bundle:</strong></p><ul><li><strong>Course 01: </strong>Diploma in Data Analysis Fundamentals</li><li><strong>Course 02: </strong>Python for Data Analysis</li><li><strong>Course 03: </strong>Data Analytics with Tableau</li><li><strong>Course 04: </strong>SQL Masterclass: SQL For Data Analytics</li><li><strong>Course 05: </strong>Basic Google Data Studio</li><li><strong>Course 06: </strong>Data Analysis and Forecasting in Excel</li></ul><p><strong>Why Choose Our Data Engineering Course?</strong></p><ul><li>FREE Data Engineering certificate accredited</li><li>Get a free student ID card with Data Engineering Training</li><li>Get instant access to this Data Engineering course.</li><li>Learn Data Engineering from anywhere in the world</li><li>The Data Engineering is affordable and simple to understand</li><li>The Data Engineering is an entirely online, interactive lesson with voiceover audio</li><li>Lifetime access to the Data Engineering course materials</li><li>The Data Engineering comes with 24/7 tutor support</li></ul><p>So enrol now in this Data Engineering Today to advance your career!</p> <p>Start your learning journey straightaway!</p><p>This Data Engineering's curriculum has been designed by Data Engineering experts with years of Data Engineering experience behind them. The Data Engineering course is extremely dynamic and well-paced to help you understand Data Engineering with ease. You'll discover how to master the Data Engineering skill while exploring relevant and essential topics.</p><p><strong>Assessment Process</strong></p><p>Once you have completed all the courses in the Data Engineering bundle, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        60 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This Data Engineering bundle is suitable for everyone.</p>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You will not need any prior background or expertise.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This Data Engineering bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited  Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited   Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            £29
        </p>
            <div><p>If you are an international student, you will be required to pay an additional fee of <strong>10 GBP</strong> for international delivery, and <strong>4.99 GBP</strong> for delivery within the UK, for each certificate</p></div>

                        </div>

Certificate in Further Education & Training (Cert Ed)

Certified in Data Protection (CDP)

Continuous Professional Development (CPD)

Data Engineering (Online) - CPD Certified

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Azure Machine Learning

DP-100T01 Designing and Implementing a Data Science Solution on Azure

<p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(15, 21, 26);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">✿ 17</strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">&nbsp;in 1 "Data Engineering: Big Data &amp; Google Cloud Architect" CPD Certified Diploma ✿</strong></p><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">Are you interested in pursuing a career in data analysis? Do you want to gain the skills and knowledge necessary to excel in the field of data analytics? Look no further! Our comprehensive&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Engineering: Big Data &amp; Google Cloud Architect Diploma</strong>&nbsp;bundle offers a thorough overview of the essential concepts, techniques, and tools required to succeed in this exciting and rapidly growing field.</p><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Engineering: Big Data &amp; Google Cloud Architect Diploma training offers :</strong></p><ul style="color: rgb(15, 21, 26); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">17&nbsp;</strong>Free PDF Certificates</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Lifetime&nbsp;</strong>Access</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">24/7</strong>&nbsp;Learner Support</li></ul><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">Enter into the world of data engineering with our "<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Engineering: Big Data &amp; Google Cloud Architect</strong>" bundle. Designed for both beginner and those with foundational knowledge, this Data Engineering: Big Data &amp; Google Cloud Architect bundle will elevate your understanding of big data and cloud architecture. With the proliferation of data in today&#39;s digital landscape, mastering these skills is crucial for anyone aiming to make a significant impact in the tech industry.</p><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">The&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Engineering: Big Data &amp; Google Cloud Architect&nbsp;</strong>diploma training provides a comprehensive curriculum that covers everything from the basics of data engineering to advanced topics in Google Cloud, Big Query, Data Structures, SQL/MySQL Database etc. Through engaging modules, you&#39;ll gain insight into the intricacies of data processing, storage solutions, and cloud infrastructure.</p><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">By completing this&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Engineering: Big Data &amp; Google Cloud Architect&nbsp;</strong>training, you&#39;ll be equipped with the knowledge needed to architect robust data solutions using Google Cloud. You&#39;ll understand the principles behind big data management and how to apply them in real-world contexts. Whether you&#39;re looking to enhance your career prospects or pivot into a new field, this&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Engineering: Big Data &amp; Google Cloud Architect</strong>&nbsp;bundle offers the tools and understanding required to succeed in the ever-evolving data engineering landscape.</p><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Engineering: Big Data &amp; Google Cloud Architect Bundle Consists the Following Courses:</strong></p><ol style="color: rgb(15, 21, 26); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><li fr-original-style="" style="box-sizing: border-box;">Data Engineering: Google Big Query &amp; Google Cloud</li><li fr-original-style="" style="box-sizing: border-box;">Mastering MySQL Database Development</li><li fr-original-style="" style="box-sizing: border-box;">Data Analytics with Tableau</li><li fr-original-style="" style="box-sizing: border-box;">Data Structures : Easy to Advanced</li><li fr-original-style="" style="box-sizing: border-box;">JavaScript for Data Structures</li><li fr-original-style="" style="box-sizing: border-box;">SQL Complete Training</li><li fr-original-style="" style="box-sizing: border-box;">Microsoft SQL Queries</li><li fr-original-style="" style="box-sizing: border-box;">Python Programming Complete Training</li><li fr-original-style="" style="box-sizing: border-box;">Python Data Science - For Beginners</li><li fr-original-style="" style="box-sizing: border-box;">Machine Learning in Flutter: Complete Guide</li><li fr-original-style="" style="box-sizing: border-box;">Data Science &amp; Machine Learning with R Complete Training</li><li fr-original-style="" style="box-sizing: border-box;">Python: Higher Order Functions</li><li fr-original-style="" style="box-sizing: border-box;">Linux File System</li><li fr-original-style="" style="box-sizing: border-box;">LabVIEW &ndash; Step By Step Beginners Guide</li><li fr-original-style="" style="box-sizing: border-box;">SQL NoSQL Big Data and Hadoop All in One Course</li><li fr-original-style="" style="box-sizing: border-box;">Basic Data Analysis Training</li><li fr-original-style="" style="box-sizing: border-box;">Data Analysis: Getting Results with PowerBI</li></ol><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learning Outcomes of Data Engineering: Big Data &amp; Google Cloud Architect Diploma</strong></p><ul style="color: rgb(15, 21, 26); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><li fr-original-style="" style="box-sizing: border-box;">Understand the fundamentals of data engineering.</li><li fr-original-style="" style="box-sizing: border-box;">Gain proficiency in Google Cloud Platform services.</li><li fr-original-style="" style="box-sizing: border-box;">Master techniques for big data processing.</li><li fr-original-style="" style="box-sizing: border-box;">Learn to design scalable data architecture.</li><li fr-original-style="" style="box-sizing: border-box;">Implement effective data storage solutions.</li><li fr-original-style="" style="box-sizing: border-box;">Analyse and optimise data workflows.</li><li fr-original-style="" style="box-sizing: border-box;">Develop skills in managing cloud-based data systems.</li><li fr-original-style="" style="box-sizing: border-box;">Comprehend security measures for data protection.</li><li fr-original-style="" style="box-sizing: border-box;">Explore methods for data integration and migration.</li><li fr-original-style="" style="box-sizing: border-box;">Apply knowledge to design and implement data solutions.</li></ul><h2 fr-original-style=" color: rgb(15, 21, 26);" style="color: rgb(15, 21, 26); box-sizing: border-box;" id="isPasted">Certificates</h2><div fr-original-style=" color: rgb(15, 21, 26);" style="color: rgb(15, 21, 26); box-sizing: border-box;"><p fr-original-style=" color: var(--text);" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Digital Certificate</strong></p><p fr-original-style=" color: var(--text);" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This bundle comes with a free <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPD Accredited PDF Copy certificate+ Transcript&nbsp;</strong>(according to the Learner&#39;s Choice). For additional courses, it costs <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">&pound;14.99</strong> per copy.</p><p fr-original-style=" color: var(--text);" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Hard Copy Certificate</strong></p><p fr-original-style=" color: var(--text);" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Get <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPD Accredited Hard Copy certificate</strong> for <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">&pound;19.99</strong> per copy. The delivery fee for the Hard Copy certificate, however, is free for UK residents, whereas it costs <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">&pound;10</strong> for international students as delivery charge.</p><section style="color: rgb(15, 21, 26); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(15, 21, 26);"><h2 style="color: var(--bs-heading-color); box-sizing: border-box;" fr-original-style=" color: var(--bs-heading-color);">Who is this course for?</h2><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Engineering: Big Data &amp; Google Cloud Architect</strong>&nbsp;diploma training is open to everyone. Specially designed for:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Aspiring data engineers.</li><li fr-original-style="" style="box-sizing: border-box;">IT professionals seeking to specialise in data engineering.</li><li fr-original-style="" style="box-sizing: border-box;">Individuals interested in big data technologies.</li><li fr-original-style="" style="box-sizing: border-box;">Those aiming to understand Google Cloud architecture.</li><li fr-original-style="" style="box-sizing: border-box;">Career changers looking to enter the tech industry.</li></ul></section><section style="color: rgb(15, 21, 26); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><h2 style="color: var(--bs-heading-color); box-sizing: border-box;" fr-original-style=" color: var(--bs-heading-color);">Requirements</h2><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">For this&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Engineering: Big Data &amp; Google Cloud Architect</strong>&nbsp;bundle, you don&#39;t need any formal qualifications.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Take your time to finish this Data Science Engineering: Data Analytics &amp; Database Management training, there is no deadline for the bundle.</p></section><section style="color: rgb(15, 21, 26); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><h2 style="color: var(--bs-heading-color); box-sizing: border-box;" fr-original-style=" color: var(--bs-heading-color);">Career path</h2><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Engineering: Big Data &amp; Google Cloud Architect</strong>&nbsp;training can lead you towards successful careers in these related fields. Such as:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Data Engineer: &pound;40,000 - &pound;70,000</li><li fr-original-style="" style="box-sizing: border-box;">Cloud Solutions Architect: &pound;50,000 - &pound;90,000</li><li fr-original-style="" style="box-sizing: border-box;">Big Data Analyst: &pound;35,000 - &pound;60,000</li><li fr-original-style="" style="box-sizing: border-box;">Data Architect: &pound;60,000 - &pound;100,000</li><li fr-original-style="" style="box-sizing: border-box;">Business Intelligence Developer: &pound;40,000 - &pound;75,000</li><li fr-original-style="" style="box-sizing: border-box;">Machine Learning Engineer: &pound;50,000 - &pound;85,000</li></ul></section></div>

Skill Up

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CPD Certified | 17-in-1 Diploma Bundle | 170 CPD Points | Free PDF & Transcript Certificate | Lifetime Access


Data Engineering: Big Data & Google Cloud Architect Diploma - CPD Certified


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					<div><p>Gain the solid skills and knowledge to kickstart a successful career and learn from the experts with this step-by-step Data Engineering : Google BigQuery &amp; Google Cloud training course. This Data Engineering : Google BigQuery &amp; Google Cloud course for Consistent Profits has been specially designed to help learners gain a good command of Data Engineering : Google BigQuery &amp; Google Cloud, providing them with a solid foundation of knowledge to understand relevant professionals' job roles.</p><p>Through this Data Engineering : Google BigQuery &amp; Google Cloud course, you will gain a theoretical understanding of Data Engineering : Google BigQuery &amp; Google Cloud and others relevant subjects that will increase your employability in this field, help you stand out from the competition, and boost your earning potential in no time.</p><p>Not only that, but this Data Engineering : Google BigQuery &amp; Google Cloud training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. </p><p>This course is fully CPD-accredited and broken down into several manageable modules, making it ideal for aspiring professionals.</p></div>
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			<h2><b> Learning outcome</b></h2>		</div>
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										<span>Study at your convenient time and from wherever you want</span>
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										<span>No formal qualifications required, anyone from any academic background can take this course.</span>
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										<span>Access to a computer or digital device with internet connectivity.</span>
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                            <div>
                                <h2>Course Curriculum</h2>
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					<td colspan="4"><strong>Unit 01: Introduction</strong></td>
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					<td>Module 01: Welcome to This Course</td><td><span>🕐 00:01:00</span></td>
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					<td>Module 02: How to Get Maximum Value from This Course</td><td><span>🕐 00:06:00</span></td>
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					<td>Module 03: Course Structure &amp; Coverage</td><td><span>🕐 00:02:00</span></td>
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					<td>Module 04: Technology in This Course</td><td><span>🕐 00:02:00</span></td>
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					<td>Module 01: Data Warehouse</td><td><span>🕐 00:07:00</span></td>
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					<td>Module 02: Start With BigQuery</td><td><span>🕐 00:05:00</span></td>
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					<td>Module 03: BigQuery Web User Interface</td><td><span>🕐 00:04:00</span></td>
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					<td colspan="4"><strong>Unit 02: Introducing Data Warehouse &amp; BigQuery</strong></td>
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					<td>Module 01: First Data</td><td><span>🕐 00:04:00</span></td>
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					<td>Module 02: Basic Exploration</td><td><span>🕐 00:16:00</span></td>
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					<td>Module 03: Functions</td><td><span>🕐 00:14:00</span></td>
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					<td>Module 05: Different Query</td><td><span>🕐 00:01:00</span></td>
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					<td>Module 06: Exploring Common Data Types</td><td><span>🕐 00:25:00</span></td>
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					<td>Module 07: Converting Data Types</td><td><span>🕐 00:07:00</span></td>
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					<td colspan="4"><strong>Unit 03: First Exploration</strong></td>
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					<td>Module 01: Data Quality</td><td><span>🕐 00:06:00</span></td>
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					<td>Module 02: Clean &amp; Transform</td><td><span>🕐 00:13:00</span></td>
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					<td>Module 03: Store Data</td><td><span>🕐 00:06:00</span></td>
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					<td>Module 04: Upgrading From Sandbox Account</td><td><span>🕐 00:01:00</span></td>
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					<td>Module 05: Clean &amp; Transform With Dataprep</td><td><span>🕐 00:25:00</span></td>
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					<td>Module 06: Scheduled Query</td><td><span>🕐 00:04:00</span></td>
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					<td>Module 07: Analyze Data</td><td><span>🕐 00:05:00</span></td>
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					<td>Module 08: Data Visualization</td><td><span>🕐 00:06:00</span></td>
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					<td colspan="4"><strong>Unit 04: Data Flow BasicUnit 05: Diving into BigQuery</strong></td>
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					<td>Module 01: Essential BigQuery</td><td><span>🕐 00:15:00</span></td>
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					<td>Module 02: Load Data into BigQuery (Part 1) - The Basic</td><td><span>🕐 00:36:00</span></td>
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					<td>Module 03: Tip: Mock Data</td><td><span>🕐 00:01:00</span></td>
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					<td>Module 04: Load Data into BigQuery (Part 2) - Handling Errors</td><td><span>🕐 00:22:00</span></td>
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					<td>Module 05: Load Data into BigQuery (Part 3) - Efficient Load</td><td><span>🕐 00:14:00</span></td>
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					<td>Module 06: Load Data into BigQuery (Part 4) - From Your Data to BigQuery</td><td><span>🕐 00:23:00</span></td>
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					<td>Module 07: Load Data into BigQuery (Part 5) - In Microservice Architecture</td><td><span>🕐 00:20:00</span></td>
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					<td>Module 08: Tip: Message Broker Overview</td><td><span>🕐 00:08:00</span></td>
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					<td>Module 09: Load Data into BigQuery (Part 6) - Recurring Load</td><td><span>🕐 00:12:00</span></td>
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					<td colspan="4"><strong>Unit 06: Virtual Data using View</strong></td>
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					<td>Module 01: BigQuery View</td><td><span>🕐 00:06:00</span></td>
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					<td colspan="4"><strong>Unit 07: Data Visualization</strong></td>
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					<td>Module 01: What We Will Learn</td><td><span>🕐 00:01:00</span></td>
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					<td>Module 02: Google Sheets &amp; BigQuery</td><td><span>🕐 00:10:00</span></td>
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					<td>Module 03: Google Data Studio</td><td><span>🕐 00:13:00</span></td>
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					<td colspan="4"><strong>Unit 08: Intermediate Query</strong></td>
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					<td>▶</td>
					<td>Module 01: Using Join - Theory</td><td><span>🕐 00:04:00</span></td>
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					<td>Module 02: Using Join - Hands On</td><td><span>🕐 00:16:00</span></td>
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					<td>Module 03: Union &amp; Intersect</td><td><span>🕐 00:06:00</span></td>
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					<td>▶</td>
					<td>Module 04: Basic Statistical Functions</td><td><span>🕐 00:05:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
		                        </div>

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Computing Skills

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Gain the solid skills and knowledge to kickstart a successful career and learn from the experts with this

Data Engineering : Google BigQuery & Google Cloud

<p><strong>Develop in-demand skills for a better future with a super-saver bundle just </strong><strong>£129</strong><strong> and instantly save </strong><strong>£1570</strong><strong>!</strong></p><p><strong>This bundle includes 1 best selling course endorsed by the Quality Licence Scheme with 4 additional CPD-accredited courses, expert assistance and course assessment. Enjoy 12 months of access to this bundle of courses FREE of charge and master a range of in-demand professional skills to advance your career. No hidden costs - simply enrol this Data Engineering with Google BigQuery &amp; Google Cloud bundle.</strong></p><p>This Course package includes:</p><ul><li>Data Engineering with Google BigQuery &amp; Google Cloud with endorsed certificate</li><li>4 additional CPD accredited courses -<br /><ol><li>Linux for Cloud and DevOps Engineers</li><li>SQL NoSQL Big Data and Hadoop</li><li>Linux Shell Scripting</li><li>Linux Security and Hardening</li></ol></li></ul><p>Dedicated tutor support and 24/7 customer support are available to all students with this premium quality bundle. Get the help you require and the answers to all your queries throughout the comprehensive syllabus of this Data Engineering with Google BigQuery &amp; Google Cloud bundle.</p><p>This premium online course ensures your professional development with endorsement from Quality Licence Scheme and also CPD accreditation. You'll have the privilege of learning in your own time, at your own pace and earning an accredited certification.</p><p>With this bundle program, success becomes a lot easier that enables you to monetise your skills. This Data Engineering with Google BigQuery &amp; Google Cloud bundle is designed for both part-time and full-time students and can be completed at a pace that suits your learning style.</p><p><strong>Why People Enrol this Bundle from One Education:</strong></p><ul><li>Receive a Quality Licence Scheme-endorsed certificate upon successful completion of the bundle</li><li>4 additional CPD accredited courses to enrich your knowledge and skills</li><li>Learning materials of our Course contains engaging voiceover and visual elements for your comfort</li><li>Get 24/7 access to all bundle content for a full year</li><li>Complete the Course at your own pace</li><li>Each of our students gets full tutor support on weekdays (Monday to Friday)</li><li>Efficient assessments and instant results upon completion of our Course</li></ul><p><strong>Course Curriculum:</strong></p><p>Here is a curriculum breakdown of the course:</p><ul><li>Unit 01: Introduction</li><li>Unit 02: Introducing Data Warehouse &amp; BigQuery</li><li>Unit 03: First Exploration</li><li>Unit 04: Data Flow Basic</li><li>Unit 05: Diving into BigQuery</li><li>Unit 06: Virtual Data using View</li><li>Unit 07: Data Visualization</li><li>Unit 08: Intermediate Query</li></ul><p><strong>How is the Course assessed?</strong></p><p>To simplify the procedure of evaluation and accreditation for learners, we provide an automated assessment system. Upon completion of an online module, you will immediately be given access to a specifically crafted MCQ test. The results will be evaluated instantly, and the score displayed for your perusal. For each test, the pass mark will be set to 60%.</p><p>When all tests have been successfully passed, you will be able to claim a certificate endorsed by the Quality Licence Scheme.</p><p><strong>Certificate of Achievement</strong></p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme</strong></p><p>After successfully completing the Course, learners will get an endorsed certificate (included with the course) delivered right at their doorstep by post, without paying a single penny! Note that learners residing outside the UK have to pay a £10 postage charge.</p><p><strong>For Additional Courses</strong></p><p>For the additional Quality Licence Scheme endorsed certificates from the additional courses, you have to pay the fees based on level of each course:</p><ul><li>No Level- £29</li><li>Level 1 - £29</li><li>Level 2 - £59</li><li>Level 3 - £69</li><li>Level 4 - £79</li><li>Level 5 - £99</li><li>Level 6 - £109</li><li>Level 7 - £119</li></ul><p><strong>CPD Accredited Certification from One Education</strong></p><p>After successfully completing the courses under this Course, you will also qualify for CPD Accredited Certificates for each of the courses. Certification is available in PDF format, at the cost of £9, or a hard copy can be sent to you via post at £15.</p><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>The bundle training is ideal for highly motivated individuals who want to enhance their professional skills and train for the job they want! This Course also suits people aspiring for some in-depth knowledge on this topic and keep up to date with the latest information.</p><p>Study the Data Engineering with Google BigQuery &amp; Google Cloud bundle today and increase your professional skillset from the comfort of your home!</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal entry requirements for the Course, with enrollment open to anyone!<strong> Anyone and everyone</strong> with a knack for learning can enrol on this course without any hesitation.</p><p>Learn online from any internet device, including your computer, tablet or smartphone. Study when it suits you and complete the Data Engineering with Google BigQuery &amp; Google Cloud bundle at your own pace.</p><p>All students must have a passion for learning and literacy, as well as being over the age of 16.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Studying the Course is designed to help you get the job of your dreams, or even that promotion you've always wanted. Learn the essential skills and knowledge you need to exceed in your professional life with the help &amp; guidance from our Course.</p>
                        </div>

One Education

Data Engineering with Google BigQuery & Google Cloud Course

<p><strong>Level 6 Diploma in Data Engineering with Google BigQuery &amp; Google Cloud</strong></p><p>Turn your skills into a competitive advantage that is recognised globally and endorsed for quality by QLS, the UK's most reputable high-quality course auditor.</p><p>Hone your skills in data analytics, big data, machine learning, data pipelines, and artificial intelligence! Create infrastructure for collecting, managing, and transforming raw data into interpretable information by data scientists and business analysts. Develop your ability to create data processing solutions, make data accessible, and evaluate and optimise organisational performance. Enrol to advance your Cloud career and show your proficiency in one of the most sought-after industries in the market.</p><p>This premium QLS endorsed Data Engineering with Google BigQuery &amp; Google Cloud course comes with intensive study modules, dedicated tutor assistance, customer support, flexible learning opportunity and a globally recognised certification all-together. Thus, successful completion of our Data Engineering with Google BigQuery &amp; Google Cloud course will give you limitless opportunities anywhere and everywhere, since the value of QLS endorsed courses are quite high, to say the least!!</p><p>What's more, the quality of our first-class course is uncompromising for both you and us. You will be taught the richest of materials from this exquisite Data Engineering with Google BigQuery &amp; Google Cloud course, enabling you to open up a vast world of opportunities for you to grasp and gain success from. For the price point, this course is actually an absolute steal! It's an investment you absolutely do not want to miss out on!</p><p>So, why delay any longer? Benefit your career progression by joining us today and distinguish yourself as, unquestionably, the most skilled one in your workplace!</p><p><strong>Learning Outcomes for Level 6 Diploma in Data Engineering with Google BigQuery &amp; Google Cloud:</strong></p><ol><li>Develop a comprehensive understanding of data engineering principles and concepts.</li><li>Utilise Google BigQuery and Google Cloud for efficient data warehousing and analysis.</li><li>Explore and manipulate data effectively using BigQuery for insightful data-driven decisions.</li><li>Apply data flow techniques to design and optimise data pipelines.</li><li>Implement virtual data solutions using views for efficient data organisation and retrieval.</li><li>Create visually appealing and informative data visualisations to communicate insights effectively.</li></ol><p><strong>Why Learn with us: </strong></p><p>We, as one of the leading e-learning service providers across the globe, strive to provide all our learners with the best e-learning experience possible that can make a real difference in their career progression.</p><p><strong>Our Service for the </strong><strong>Data Engineering with Google BigQuery &amp; Google Cloud</strong><strong> Includes:</strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Assignment Help</li><li>QLS Endorsed Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Study Materials</li></ul><p><strong>Course Curriculum of </strong><strong>Data Engineering with Google BigQuery &amp; Google Cloud</strong></p><ul><li><strong>Unit 01: </strong>Introduction<strong><br /></strong></li><li><strong>Unit 02: </strong>Introducing Data Warehouse &amp; BigQuery<strong><br /></strong></li><li><strong>Unit 03: </strong>First Exploration<strong><br /></strong></li><li><strong>Unit 04: </strong>Data Flow Basic<strong><br /></strong></li><li><strong>Unit 05: </strong>Diving into BigQuery<strong><br /></strong></li><li><strong>Unit 06: </strong>Virtual Data using View<strong><br /></strong></li><li><strong>Unit 07: </strong>Data Visualisation<strong><br /></strong></li><li><strong>Unit 08: </strong>Intermediate Query</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Learners must submit a comprehensive summerised assignment on all the units that will be assessed by our expert tutor if all the learning outcomes are met as per the standard set by QLS. After the quality check, learner will be provided with the Data Engineering with Google BigQuery &amp; Google Cloud certificate.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme.</strong></p><p>An endorsed certificate will be issued to learners at the end of the course as recognition of course completion. Provided that learner completes all the assessment of a course can claim for certificate.</p><p><strong>Endorsement:</strong></p><p>QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions including your progression routes into further higher education.</p><h2>
        
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                                        10 CPD hours / points
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                                            Accredited by CPD Quality Standards
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        <div>Who is this course for?</div>
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                            <p>Our course is open for everyone. This Data Engineering with Google BigQuery &amp; Google Cloud is ideal for those who aspire to gain in-depth knowledge &amp; efficient skill to embark into the relevant sector as a promising potential.</p>
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        <div>Requirements</div>
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                            <p>There are no formal requirements needed for this Data Engineering with Google BigQuery &amp; Google Cloud course. So learners do not require any prior qualifications to enrol in this course.</p>
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        <div>Career path</div>
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                            <p><strong>Here are few job roles related to Data Engineering with Google BigQuery &amp; Google Cloud </strong>(Salary-per annum):</p><ol><li>Data Engineer (£50,000 - £55,000)</li><li>Data Analyst: (£38,000 - £40,000)</li><li>Data Scientist (£40,000 - £70,000)</li><li>Data Architect (£80,000 - £90,000)</li><li>Cloud Data Engineer (£50,000 - £80,000)</li><li>Big Data Engineer (£50,000 - £80,000)</li></ol>
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Kingston Open College

ELS (Effective Learning Service)

ICDL (International Certification of Digital Literacy)

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

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