<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer.</p>Overview<p>Working in a hands-on learning environment, guided by our expert team, attendees will learn to<br />Understand the main concepts and principles of predictive analytics<br />Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects<br />Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations<br />Learn to deploy a predictive model's results as an interactive application<br />Learn about the stages involved in producing complete predictive analytics solutions<br />Understand how to define a problem, propose a solution, and prepare a dataset<br />Use visualizations to explore relationships and gain insights into the dataset<br />Learn to build regression and classification models using scikit-learn<br />Use Keras to build powerful neural network models that produce accurate predictions<br />Learn to serve a model's predictions as a web application</p><p>Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models.  Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.</p></div><div id="course-pills-2"><h4>The Predictive Analytics Process</h4><ul><li>Technical requirements</li><li>What is predictive analytics?</li><li>Reviewing important concepts of predictive analytics</li><li>The predictive analytics process</li><li>A quick tour of Python's data science stack</li></ul><h4>Problem Understanding and Data Preparation</h4><ul><li>Technical requirements</li><li>Understanding the business problem and proposing a solution</li><li>Practical project ? diamond prices</li><li>Practical project ? credit card default</li></ul><h4>Dataset Understanding ? Exploratory Data Analysis</h4><ul><li>Technical requirements</li><li>What is EDA?</li><li>Univariate EDA</li><li>Bivariate EDA</li><li>Introduction to graphical multivariate EDA</li></ul><h4>Predicting Numerical Values with Machine Learning</h4><ul><li>Technical requirements</li><li>Introduction to ML</li><li>Practical considerations before modeling</li><li>MLR</li><li>Lasso regression</li><li>KNN</li><li>Training versus testing error</li></ul><h4>Predicting Categories with Machine Learning</h4><ul><li>Technical requirements</li><li>Classification tasks</li><li>Credit card default dataset</li><li>Logistic regression</li><li>Classification trees</li><li>Random forests</li><li>Training versus testing error</li><li>Multiclass classification</li><li>Naive Bayes classifiers</li></ul><h4>Introducing Neural Nets for Predictive Analytics</h4><ul><li>Technical requirements</li><li>Introducing neural network models</li><li>Introducing TensorFlow and Keras</li><li>Regressing with neural networks</li><li>Classification with neural networks</li><li>The dark art of training neural networks</li></ul><h4>Model Evaluation</h4><ul><li>Technical requirements</li><li>Evaluation of regression models</li><li>Evaluation for classification models</li><li>The k-fold cross-validation</li></ul><h4>Model Tuning and Improving Performance</h4><ul><li>Technical requirements</li><li>Hyperparameter tuning</li><li>Improving performance</li></ul><h4>Implementing a Model with Dash</h4><ul><li>Technical requirements</li><li>Model communication and/or deployment phase</li><li>Introducing Dash</li><li>Implementing a predictive model as a web application</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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

Predictive Analytics

Business Analytics & Intelligence

Data Engineering

Big Data Analytics

Data Science

Data Skills

Hands-on Predicitive Analytics with Python (TTPS4879)

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems.<br />A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language.</p>Overview<p>In this course, you will use R to perform common data science tasks.You will:<br />Set up an R development environment and execute simple code.<br />Perform operations on atomic data types in R, including characters, numbers, and logicals.<br />Perform operations on data structures in R, including vectors, lists, and data frames.<br />Write conditional statements and loops.<br />Structure code for reuse with functions and packages.<br />Manage data by loading and saving datasets, manipulating data frames, and more.<br />Analyze data through exploratory analysis, statistical analysis, and more.<br />Create and format data visualizations using base R and ggplot2.<br />Create simple statistical models from data.</p><p>In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable.<br /><br />This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business.</p></div><div id="course-pills-2"><h4>Lesson 1: Setting Up R and Executing Simple Code</h4><ul><li>Topic A: Set Up the R Development Environment</li><li>Topic B: Write R Statements</li></ul><h4>Lesson 2: Processing Atomic Data Types</h4><ul><li>Topic A: Process Characters</li><li>Topic B: Process Numbers</li><li>Topic C: Process Logicals</li></ul><h4>Lesson 3: Processing Data Structures</h4><ul><li>Topic A: Process Vectors</li><li>Topic B: Process Factors</li><li>Topic C: Process Data Frames</li><li>Topic D: Subset Data Structures</li></ul><h4>Lesson 4: Writing Conditional Statements and Loops</h4><ul><li>Topic A: Write Conditional Statements</li><li>Topic B: Write Loops</li></ul><h4>Lesson 5: Structuring Code for Reuse</h4><ul><li>Topic A: Define and Call Functions</li><li>Topic B: Apply Loop Functions</li><li>Topic C: Manage R Packages</li></ul><h4>Lesson 6: Managing Data in R</h4><ul><li>Topic A: Load Data</li><li>Topic B: Save Data</li><li>Topic C: Manipulate Data Frames Using Base R</li><li>Topic D: Manipulate Data Frames Using dplyr</li><li>Topic E: Handle Dates and Times</li></ul><h4>Lesson 7: Analyzing Data in R</h4><ul><li>Topic A: Examine Data</li><li>Topic B: Explore the Underlying Distribution of Data</li><li>Topic C: Identify Missing Values</li></ul><h4>Lesson 8: Visualizing Data in R</h4><ul><li>Topic A: Plot Data Using Base R Functions</li><li>Topic B: Plot Data Using ggplot2</li><li>Topic C: Format Plots in ggplot2</li><li>Topic D: Create Combination Plots</li></ul><h4>Lesson 9: Modeling Data in R</h4><ul><li>Topic A: Create Statistical Models in R</li><li>Topic B: Create Machine Learning Models in R</li></ul></div>

Online Course Creation

R (programming language)

Statistical Computing

R Programming for Data Science (v1.0)

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.</p>Overview<p>By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.</p><p>The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.</p></div><div id="course-pills-2"><h4>Data Preparation and Cleaning</h4><ul><li>Data Models and Structured Data</li><li>pandas</li><li>Data Manipulation</li></ul><h4>Data Exploration and Visualization</h4><ul><li>Identifying the Right Attributes</li><li>Generating Targeted Insights</li><li>Visualizing Data</li></ul><h4>Unsupervised Learning: Customer Segmentation</h4><ul><li>Customer Segmentation Methods</li><li>Similarity and Data Standardization</li><li>k-means Clustering</li></ul><h4>Choosing the Best Segmentation Approach</h4><ul><li>Choosing the Number of Clusters</li><li>Different Methods of Clustering</li><li>Evaluating Clustering</li></ul><h4>Predicting Customer Revenue Using Linear Regression </h4><ul><li>Understanding Regression</li><li>Feature Engineering for Regression</li><li>Performing and Interpreting Linear Regression</li></ul><h4>Other Regression Techniques and Tools for Evaluation</h4><ul><li>Evaluating the Accuracy of a Regression Model</li><li>Using Regularization for Feature Selection</li><li>Tree-Based Regression Models</li></ul><h4>Supervised Learning: Predicting Customer Churn</h4><ul><li>Classification Problems</li><li>Understanding Logistic Regression</li><li>Creating a Data Science Pipeline</li></ul><h4>Fine-Tuning Classification Algorithms</h4><ul><li>Support Vector Machine</li><li>Decision Trees</li><li>Random Forest</li><li>Preprocessing Data for Machine Learning Models</li><li>Model Evaluation</li><li>Performance Metrics</li></ul><h4>Modeling Customer Choice</h4><ul><li>Understanding Multiclass Classification</li><li>Class Imbalanced Data</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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>

Online Course Marketing

Marketing Analytics

Data Science for Marketing Analytics

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform</p>Overview<p>This course teaches students the following skills:<br />Derive insights from data using the analysis and visualization tools on Google Cloud Platform<br />Interactively query datasets using Google BigQuery<br />Load, clean, and transform data at scale<br />Visualize data using Google Data Studio and other third-party platforms<br />Distinguish between exploratory and explanatory analytics and when to use each approach<br />Explore new datasets and uncover hidden insights quickly and effectively<br />Optimizing data models and queries for price and performance</p><p>Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL.</p></div><div id="course-pills-2"><h4>Introduction to Data on the Google Cloud Platform</h4><ul><li>Highlight Analytics Challenges Faced by Data Analysts</li><li>Compare Big Data On-Premises vs on the Cloud</li><li>Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud</li><li>Navigate Google Cloud Platform Project Basics</li><li>Lab: Getting started with Google Cloud Platform</li></ul><h4>Big Data Tools Overview</h4><ul><li>Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools</li><li>Demo: Analyze 10 Billion Records with Google BigQuery</li><li>Explore 9 Fundamental Google BigQuery Features</li><li>Compare GCP Tools for Analysts, Data Scientists, and Data Engineers</li><li>Lab: Exploring Datasets with Google BigQuery</li></ul><h4>Exploring your Data with SQL</h4><ul><li>Compare Common Data Exploration Techniques</li><li>Learn How to Code High Quality Standard SQL</li><li>Explore Google BigQuery Public Datasets</li><li>Visualization Preview: Google Data Studio</li><li>Lab: Troubleshoot Common SQL Errors</li></ul><h4>Google BigQuery Pricing</h4><ul><li>Walkthrough of a BigQuery Job</li><li>Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs</li><li>Optimize Queries for Cost</li><li>Lab: Calculate Google BigQuery Pricing</li></ul><h4>Cleaning and Transforming your Data</h4><ul><li>Examine the 5 Principles of Dataset Integrity</li><li>Characterize Dataset Shape and Skew</li><li>Clean and Transform Data using SQL</li><li>Clean and Transform Data using a new UI: Introducing Cloud Dataprep</li><li>Lab: Explore and Shape Data with Cloud Dataprep</li></ul><h4>Storing and Exporting Data</h4><ul><li>Compare Permanent vs Temporary Tables</li><li>Save and Export Query Results</li><li>Performance Preview: Query Cache</li><li>Lab: Creating new Permanent Tables</li></ul><h4>Ingesting New Datasets into Google BigQuery</h4><ul><li>Query from External Data Sources</li><li>Avoid Data Ingesting Pitfalls</li><li>Ingest New Data into Permanent Tables</li><li>Discuss Streaming Inserts</li><li>Lab: Ingesting and Querying New Datasets</li></ul><h4>Data Visualization</h4><ul><li>Overview of Data Visualization Principles</li><li>Exploratory vs Explanatory Analysis Approaches</li><li>Demo: Google Data Studio UI</li><li>Connect Google Data Studio to Google BigQuery</li><li>Lab: Exploring a Dataset in Google Data Studio</li></ul><h4>Joining and Merging Datasets</h4><ul><li>Merge Historical Data Tables with UNION</li><li>Introduce Table Wildcards for Easy Merges</li><li>Review Data Schemas: Linking Data Across Multiple Tables</li><li>Walkthrough JOIN Examples and Pitfalls</li><li>Lab: Join and Union Data from Multiple Tables</li></ul><h4>Advanced Functions and Clauses</h4><ul><li>Review SQL Case Statements</li><li>Introduce Analytical Window Functions</li><li>Safeguard Data with One-Way Field Encryption</li><li>Discuss Effective Sub-query and CTE design</li><li>Compare SQL and Javascript UDFs</li><li>Lab: Deriving Insights with Advanced SQL Functions</li></ul><h4>Schema Design and Nested Data Structures</h4><ul><li>Compare Google BigQuery vs Traditional RDBMS Data Architecture</li><li>Normalization vs Denormalization: Performance Tradeoffs</li><li>Schema Review: The Good, The Bad, and The Ugly</li><li>Arrays and Nested Data in Google BigQuery</li><li>Lab: Querying Nested and Repeated Data</li></ul><h4>More Visualization with Google Data Studio</h4><ul><li>Create Case Statements and Calculated Fields</li><li>Avoid Performance Pitfalls with Cache considerations</li><li>Share Dashboards and Discuss Data Access considerations</li></ul><h4>Optimizing for Performance</h4><ul><li>Avoid Google BigQuery Performance Pitfalls</li><li>Prevent Hotspots in your Data</li><li>Diagnose Performance Issues with the Query Explanation map</li><li>Lab: Optimizing and Troubleshooting Query Performance</li></ul><h4>Advanced Insights</h4><ul><li>Introducing Cloud Datalab</li><li>Cloud Datalab Notebooks and Cells</li><li>Benefits of Cloud Datalab</li></ul><h4>Data Access</h4><ul><li>Compare IAM and BigQuery Dataset Roles</li><li>Avoid Access Pitfalls</li><li>Review Members, Roles, Organizations, Account Administration, and Service Accounts</li></ul></div>

Google Data Studio

Google Data Analytics

Google Cloud

From Data to Insights with Google Cloud Platform

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is intended for:<br />Database architects<br />Database administrators<br />Database developers<br />Data analysts and scientists</p>Overview<p>This course is designed to teach you how to:<br />Discuss the core concepts of data warehousing, and the intersection between data warehousing and big data solutions<br />Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud<br />Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3, to contribute to the data warehousing solution<br />Architect the data warehouse<br />Identify performance issues, optimize queries, and tune the database for better performance<br />Use Amazon Redshift Spectrum to analyze data directly from an Amazon S3 bucket<br />Use Amazon QuickSight to perform data analysis and visualization tasks against the data warehouse</p><p>Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course demonstrates how to use Amazon QuickSight to perform analysis on your data</p></div><div id="course-pills-2"><h4>Module 1: Introduction to Data Warehousing </h4><ul><li>Relational databases</li><li>Data warehousing concepts</li><li>The intersection of data warehousing and big data</li><li>Overview of data management in AWS</li><li>Hands-on lab 1: Introduction to Amazon Redshift</li></ul><h4>Module 2: Introduction to Amazon Redshift </h4><ul><li>Conceptual overview</li><li>Real-world use cases</li><li>Hands-on lab 2: Launching an Amazon Redshift cluster</li></ul><h4>Module 3: Launching clusters </h4><ul><li>Building the cluster</li><li>Connecting to the cluster</li><li>Controlling access</li><li>Database security</li><li>Load data</li><li>Hands-on lab 3: Optimizing database schemas</li></ul><h4>Module 4: Designing the database schema </h4><ul><li>Schemas and data types</li><li>Columnar compression</li><li>Data distribution styles</li><li>Data sorting methods</li></ul><h4>Module 5: Identifying data sources </h4><ul><li>Data sources overview</li><li>Amazon S3</li><li>Amazon DynamoDB</li><li>Amazon EMR</li><li>Amazon Kinesis Data Firehose</li><li>AWS Lambda Database Loader for Amazon Redshift</li><li>Hands-on lab 4: Loading real-time data into an Amazon Redshift database</li></ul><h4>Module 6: Loading data </h4><ul><li>Preparing Data</li><li>Loading data using COPY</li><li>Data Warehousing on AWS</li><li>AWS Classroom Training</li><li>Concurrent write operations</li><li>Troubleshooting load issues</li><li>Hands-on lab 5: Loading data with the COPY command</li></ul><h4>Module 7: Writing queries and tuning for performance </h4><ul><li>Amazon Redshift SQL</li><li>User-Defined Functions (UDFs)</li><li>Factors that affect query performance</li><li>The EXPLAIN command and query plans</li><li>Workload Management (WLM)</li><li>Hands-on lab 6: Configuring workload management</li></ul><h4>Module 8: Amazon Redshift Spectrum </h4><ul><li>Amazon Redshift Spectrum</li><li>Configuring data for Amazon Redshift Spectrum</li><li>Amazon Redshift Spectrum Queries</li><li>Hands-on lab 7: Using Amazon Redshift Spectrum</li></ul><h4>Module 9: Maintaining clusters </h4><ul><li>Audit logging</li><li>Performance monitoring</li><li>Events and notifications</li><li>Lab 8: Auditing and monitoring clusters</li><li>Resizing clusters</li><li>Backing up and restoring clusters</li><li>Resource tagging and limits and constraints</li><li>Hands-on lab 9: Backing up, restoring and resizing clusters</li></ul><h4>Module 10: Analyzing and visualizing data </h4><ul><li>Power of visualizations</li><li>Building dashboards</li><li>Amazon QuickSight editions and feature</li></ul></div>

Data Warehouse

AWS Certified Database

AWS (Amazon Web Services)

Data Warehousing on AWS

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This class is intended for the following:<br />Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform.<br />Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports.<br />Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists.</p>Overview<p>This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform.</p><p>This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.</p></div><div id="course-pills-2"><h4>Introducing Google Cloud Platform</h4><ul><li>Google Platform Fundamentals Overview.</li><li>Google Cloud Platform Big Data Products.</li><li></li></ul><h4>Compute and Storage Fundamentals</h4><ul><li>CPUs on demand (Compute Engine).</li><li>A global filesystem (Cloud Storage).</li><li>CloudShell.</li><li>Lab: Set up a Ingest-Transform-Publish data processing pipeline.</li><li></li></ul><h4>Data Analytics on the Cloud</h4><ul><li>Stepping-stones to the cloud.</li><li>Cloud SQL: your SQL database on the cloud.</li><li>Lab: Importing data into CloudSQL and running queries.</li><li>Spark on Dataproc.</li><li>Lab: Machine Learning Recommendations with Spark on Dataproc.</li><li></li></ul><h4>Scaling Data Analysis</h4><ul><li>Fast random access.</li><li>Datalab.</li><li>BigQuery.</li><li>Lab: Build machine learning dataset.</li><li></li></ul><h4>Machine Learning</h4><ul><li>Machine Learning with TensorFlow.</li><li>Lab: Carry out ML with TensorFlow</li><li>Pre-built models for common needs.</li><li>Lab: Employ ML APIs.</li><li></li></ul><h4>Data Processing Architectures</h4><ul><li>Message-oriented architectures with Pub/Sub.</li><li>Creating pipelines with Dataflow.</li><li>Reference architecture for real-time and batch data processing.</li><li></li></ul><h4>Summary</h4><ul><li>Why GCP?</li><li>Where to go from here</li><li>Additional Resources     </li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals 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 Google Cloud Platform Big Data and Machine Learning Fundamentals 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>

Azure Machine Learning

Google Digital Marketing

Google Cloud Platform Big Data and Machine Learning Fundamentals

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is intended for:<br />Data platform engineers<br />Architects and operators who build and manage data analytics pipelines</p>Overview<p>In this course, you will learn to:<br />Compare the features and benefits of data warehouses, data lakes, and modern data architectures<br />Design and implement a batch data analytics solution<br />Identify and apply appropriate techniques, including compression, to optimize data storage<br />Select and deploy appropriate options to ingest, transform, and store data<br />Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case<br />Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights<br />Secure data at rest and in transit<br />Monitor analytics workloads to identify and remediate problems<br />Apply cost management best practices</p><p>In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR.</p></div><div id="course-pills-2"><h4>Module A: Overview of Data Analytics and the Data Pipeline</h4><ul><li>Data analytics use cases</li><li>Using the data pipeline for analytics</li></ul><h4>Module 1: Introduction to Amazon EMR</h4><ul><li>Using Amazon EMR in analytics solutions</li><li>Amazon EMR cluster architecture</li><li>Interactive Demo 1: Launching an Amazon EMR cluster</li><li>Cost management strategies</li></ul><h4>Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage</h4><ul><li>Storage optimization with Amazon EMR</li><li>Data ingestion techniques</li></ul><h4>Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR</h4><ul><li>Apache Spark on Amazon EMR use cases</li><li>Why Apache Spark on Amazon EMR</li><li>Spark concepts</li><li>Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell</li><li>Transformation, processing, and analytics</li><li>Using notebooks with Amazon EMR</li><li>Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR</li></ul><h4>Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive</h4><ul><li>Using Amazon EMR with Hive to process batch data</li><li>Transformation, processing, and analytics</li><li>Practice Lab 2: Batch data processing using Amazon EMR with Hive</li><li>Introduction to Apache HBase on Amazon EMR</li></ul><h4>Module 5: Serverless Data Processing</h4><ul><li>Serverless data processing, transformation, and analytics</li><li>Using AWS Glue with Amazon EMR workloads</li><li>Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions</li></ul><h4>Module 6: Security and Monitoring of Amazon EMR Clusters</h4><ul><li>Securing EMR clusters</li><li>Interactive Demo 3: Client-side encryption with EMRFS</li><li>Monitoring and troubleshooting Amazon EMR clusters</li><li>Demo: Reviewing Apache Spark cluster history</li></ul><h4>Module 7: Designing Batch Data Analytics Solutions</h4><ul><li>Batch data analytics use cases</li><li>Activity: Designing a batch data analytics workflow</li></ul><h4>Module B: Developing Modern Data Architectures on AWS</h4><ul><li>Modern data architectures</li></ul></div>

Excel Analytics

Building Batch Data Analytics Solutions on AWS

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.<br />Completed either AWS Technical Essentials or Architecting on AWS<br />Completed Building Data Lakes on AWS</p>Overview<p>In this course, you will learn to:<br />Compare the features and benefits of data warehouses, data lakes, and modern data architectures<br />Design and implement a data warehouse analytics solution<br />Identify and apply appropriate techniques, including compression, to optimize data storage<br />Select and deploy appropriate options to ingest, transform, and store data<br />Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case<br />Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights<br />Secure data at rest and in transit<br />Monitor analytics workloads to identify and remediate problems<br />Apply cost management best practices</p><p>In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift.</p></div><div id="course-pills-2"><h4>Module A: Overview of Data Analytics and the Data Pipeline</h4><ul><li>Data analytics use cases</li><li>Using the data pipeline for analytics</li></ul><h4>Module 1: Using Amazon Redshift in the Data Analytics Pipeline</h4><ul><li>Why Amazon Redshift for data warehousing?</li><li>Overview of Amazon Redshift</li></ul><h4>Module 2: Introduction to Amazon Redshift</h4><ul><li>Amazon Redshift architecture<br />Interactive Demo 1: Touring the Amazon Redshift console<br />Amazon Redshift features<br />Practice Lab 1: Load and query data in an Amazon Redshift cluster</li></ul><h4>Module 3: Ingestion and Storage</h4><ul><li>Ingestion</li><li>Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API</li><li>Data distribution and storage</li><li>Interactive Demo 3: Analyzing semi-structured data using the SUPER data type</li><li>Querying data in Amazon Redshift</li><li>Practice Lab 2: Data analytics using Amazon Redshift Spectrum</li></ul><h4>Module 4: Processing and Optimizing Data</h4><ul><li>Data transformation</li><li>Advanced querying</li><li>Practice Lab 3: Data transformation and querying in Amazon Redshift</li><li>Resource management</li><li>Interactive Demo 4: Applying mixed workload management on Amazon Redshift</li><li>Automation and optimization</li><li>Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster</li></ul><h4>Module 5: Security and Monitoring of Amazon Redshift Clusters</h4><ul><li>Securing the Amazon Redshift cluster</li><li>Monitoring and troubleshooting Amazon Redshift clusters</li></ul><h4>Module 6: Designing Data Warehouse Analytics Solutions</h4><ul><li>Data warehouse use case review</li><li>Activity: Designing a data warehouse analytics workflow</li></ul><h4>Module B: Developing Modern Data Architectures on AWS</h4><ul><li>Modern data architectures</li></ul></div>

AWS Certified Solutions Architect

Building Data Analytics Solutions Using Amazon Redshift

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is intended for:<br />Data platform engineers<br />Solutions architects<br />IT professionals</p>Overview<p>In this course, you will learn to:<br />Apply data lake methodologies in planning and designing a data lake<br />Articulate the components and services required for building an AWS data lake<br />Secure a data lake with appropriate permission<br />Ingest, store, and transform data in a data lake<br />Query, analyze, and visualize data within a data lake</p><p>In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake architectures.</p></div><div id="course-pills-2"><h4>Module 1: Introduction to data lakes</h4><ul><li>Describe the value of data lakes<br />Compare data lakes and data warehouses<br />Describe the components of a data lake<br />Recognize common architectures built on data lakes</li></ul><h4>Module 2: Data ingestion, cataloging, and preparation</h4><ul><li>Describe the relationship between data lake storage and data ingestion</li><li>Describe AWS Glue crawlers and how they are used to create a data catalog</li><li>Identify data formatting, partitioning, and compression for efficient storage and query</li><li>Lab 1: Set up a simple data lake</li></ul><h4>Module 3: Data processing and analytics</h4><ul><li>Recognize how data processing applies to a data lake<br />Use AWS Glue to process data within a data lake<br />Describe how to use Amazon Athena to analyze data in a data lake</li></ul><h4>Module 4: Building a data lake with AWS Lake Formation</h4><ul><li>Describe the features and benefits of AWS Lake Formation<br />Use AWS Lake Formation to create a data lake<br />Understand the AWS Lake Formation security model<br />Lab 2: Build a data lake using AWS Lake Formation</li></ul><h4>Module 5: Additional Lake Formation configurations</h4><ul><li>Automate AWS Lake Formation using blueprints and workflows<br />Apply security and access controls to AWS Lake Formation<br />Match records with AWS Lake Formation FindMatches<br />Visualize data with Amazon QuickSight<br />Lab 3: Automate data lake creation using AWS Lake Formation blueprints<br />Lab 4: Data visualization using Amazon QuickSight</li></ul><h4>Module 6: Architecture and course review</h4><ul><li>Post course knowledge check</li><li>Architecture review</li><li>Course review</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Building Data Lakes on AWS 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 Building Data Lakes on AWS 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>

Learning & Development

AWS Certified Developer

Building Data Lakes on AWS

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p></p><p><span>This course is intended for:</span></p><br /><p><span>Data platform engineers</span></p><br /><p><span>Solutions architects</span></p><br /><p><span>IT professionals</span></p><p></p>Overview<p></p><p><span>In this course, you will learn to:</span></p><br /><p><span>Apply data lake methodologies in planning and designing a data lake</span></p><br /><p><span>Articulate the components and services required for building an AWS data lake</span></p><br /><p><span>Secure a data lake with appropriate permission</span></p><br /><p><span>Ingest, store, and transform data in a data lake</span></p><br /><p><span>Query, analyze, and visualize data within a data lake</span></p><p></p><p>In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake</p></div><div id="course-pills-2"><h4>Introduction to data lakes</h4><ul><li>Describe the value of data lakes</li><li>Compare data lakes and data warehouses</li><li>Describe the components of a data lake</li><li>Recognize common architectures built on data lakes</li><li></li></ul><h4>Data ingestion, cataloging, and preparation</h4><ul><li>Describe the relationship between data lake storage and data ingestion</li><li>Describe AWS Glue crawlers and how they are used to create a data catalog</li><li>Identify data formatting, partitioning, and compression for efficient storage and query</li><li>Lab 1: Set up a simple data lake</li></ul><h4>Data processing and analytics</h4><ul><li>Recognize how data processing applies to a data lake</li><li>Use AWS Glue to process data within a data lake</li><li>Describe how to use Amazon Athena to analyze data in a data lake</li></ul><h4>Building a data lake with AWS Lake Formation</h4><ul><li>Describe the features and benefits of AWS Lake Formation</li><li>Use AWS Lake Formation to create a data lake</li><li>Understand the AWS Lake Formation security model</li><li>Lab 2: Build a data lake using AWS Lake Formation</li></ul><h4> Additional Lake Formation configurations</h4><ul><li>Automate AWS Lake Formation using blueprints and workflows</li><li>Apply security and access controls to AWS Lake Formation</li><li>Match records with AWS Lake Formation FindMatches</li><li>Visualize data with Amazon QuickSight</li><li>Lab 3: Automate data lake creation using AWS Lake Formation blueprints</li><li>Lab 4: Data visualization using Amazon QuickSight</li></ul><h4>Architecture and course review</h4><ul><li>Post course knowledge check</li><li>Architecture review</li><li>Course review</li></ul></div>

AWS Certification

AWS Certified Cloud Practitioner

AWS Building Data Lakes on AWS

<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>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <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>

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

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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										<span>Access to a computer or digital device with internet connectivity.</span>
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                                <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 01: Data Quality</td><td><span>🕐 00:06:00</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>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>Module 04: Basic Statistical Functions</td><td><span>🕐 00:05:00</span></td>
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				</tbody></table></div>

			</div>
		                        </div>

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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