32 Data Quality courses

Duration 3 Days 18 CPD hours This course is intended for This class is designed for individuals who are (or will soon be) supporting a Salesforce implementation in a decision-making capacity. This includes, but is not limited to, business analysts, IT managers, project managers, executive leaders, and executive sponsors. This class is not recommended for individuals tasked with solution-building. Overview When you complete this course, you will be able to: Identify key stakeholders needed for a successful Salesforce implementation. Describe the Salesforce data model as it relates to Customer 360, Salesforce Clouds, and the Salesforce Platform. Communicate the appropriate security measures needed to control org and data access. Discuss which standard or custom objects and applications should be implemented based on specific requirements and use cases. Effectively strategize how to migrate data into your Salesforce org while maintaining high data quality. Understand Salesforce automation tools and how they solve for various business challenges. Analyze Salesforce data with Reports and Dashboards. Navigate the key phases and milestones of a Salesforce implementation. Explore Salesforce features and functionality and gain the knowledge to make Salesforce implementation decisions with confidence. In this 3-day, heavily discussion-based class, learn about standard and custom objects and applications, data management, data visualization, flow automation tools, security mechanisms, and more. Successfully navigate the key phases and milestones of a Salesforce implementation, effectively communicate business needs, and provide directives to team members tasked with solution-building to deliver a robust Salesforce solution that achieves business goals. SALESFORCE DATA MODEL * Discover the Customer 360 Platform * Examine Salesforce Clouds * Navigate the Salesforce Platform * Review the Salesforce Platform Data Model * Understand Data Visualization SECURITY & ACCESS * Create Users * Access the Org * Control Data OBJECTS & APPLICATIONS * Review Standard Objects * Understand Custom Objects * Explore Standard Applications * Discover Custom Applications SALESFORCE CUSTOMIZATIONS * Work with Fields * Design Page Layouts * Understand Record Types * Review Dynamic Capabilities SUCCESSFUL DATA MANAGEMENT * Determine Data Strategy * Create Data * Ensure Data Quality PROCESS AUTOMATION * Streamline Business Processes Using Automation Tools * Learn Purpose-Driven Automation * Automate With Flow DATA ANALYSIS USING REPORTS & DASHBOARDS * Organize Reports and Dashboards * Build Reports * Create Dashboards * Create an Analytics Strategy ADOPTION & CONTINUED IMPROVEMENT * Adopt Your Implementation * Evaluate Continued Improvements ADDITIONAL COURSE DETAILS: Nexus Humans Understand and Drive Your Salesforce Implementation ( BSX101 ) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. 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. While we feel this is the best course for the Understand and Drive Your Salesforce Implementation ( BSX101 ) 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. 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.

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. OVERVIEW OF DATA SCIENCE AND MACHINE LEARNING AT SCALE OVERVIEW OF THE HADOOP ECOSYSTEM WORKING WITH HDFS DATA AND HIVE TABLES USING HUE INTRODUCTION TO CLOUDERA DATA SCIENCE WORKBENCH OVERVIEW OF APACHE SPARK 2 READING AND WRITING DATA INSPECTING DATA QUALITY CLEANSING AND TRANSFORMING DATA SUMMARIZING AND GROUPING DATA COMBINING, SPLITTING, AND RESHAPING DATA EXPLORING DATA CONFIGURING, MONITORING, AND TROUBLESHOOTING SPARK APPLICATIONS OVERVIEW OF MACHINE LEARNING IN SPARK MLLIB EXTRACTING, TRANSFORMING, AND SELECTING FEATURES BUILDING AND EVAUATING REGRESSION MODELS BUILDING AND EVALUATING CLASSIFICATION MODELS BUILDING AND EVALUATING CLUSTERING MODELS CROSS-VALIDATING MODELS AND TUNING HYPERPARAMETERS BUILDING MACHINE LEARNING PIPELINES DEPLOYING MACHINE LEARNING MODELS ADDITIONAL COURSE DETAILS: Nexus Humans Cloudera Data Scientist Training training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. 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. While we feel this is the best course for the Cloudera Data Scientist Training 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. 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.

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. FROM BUSINESS QUESTIONS TO DATA ANALYTICS, AND BEYOND * For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: * to describe and understand the general analytics process. * to describe and understand the different types of analytics can be used to derive data driven solutions to business * to apply that knowledge to their business context BASIC PYTHON PROGRAMMING CONVENTIONS * This section will cover the basics of writing R programs. Topics covered will include: * What is Python? * Using Anaconda * Writing Python programs * Expressions and objects * Functions and arguments * Basic Python programming conventions DATA STRUCTURES IN PYTHON * This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: * Vectors * Arrays and matrices * Factors * Lists * Data frames * Loading .csv files into Python CONNECTING TO EXTERNAL DATA * This section will look at loading data from other sources into Python. Topics covered will include: * Loading .csv files into a pandas data frame * Connecting to and loading data from a database into a panda data frame DATA MANIPULATION IN PYTHON * This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: * Filtering data * Deriving new fields * Aggregating data * Joining data sources * Connecting to external data sources DESCRIPTIVE ANALYTICS AND BASIC REPORTING IN PYTHON * This section will explain how Python can be used to perform basic descriptive. Topics covered will include: * Summary statistics * Grouped summary statistics * Using descriptive analytics to assess data quality * Using descriptive analytics to created business report * Using descriptive analytics to conduct exploratory analysis STATISTICAL ANALYSIS IN PYTHON * This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: * Significance tests * Correlation * Linear regressions * Using statistical output to create better business decisions. DATA VISUALISATION IN PYTHON * This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: * Creating different chart types such as bar charts, box plots, histograms and line plots * Formatting charts BEST PRACTICES HINTS AND TIPS * This section will go through some best practice considerations that should be adopted of you are applying Python in a business context. *

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. INTRODUCTION TO DATA ENGINEERING * Explore the role of a data engineer. * Analyze data engineering challenges. * Intro to BigQuery. * Data Lakes and Data Warehouses. * Demo: Federated Queries with BigQuery. * Transactional Databases vs Data Warehouses. * Website Demo: Finding PII in your dataset with DLP API. * Partner effectively with other data teams. * Manage data access and governance. * Build production-ready pipelines. * Review GCP customer case study. * Lab: Analyzing Data with BigQuery. BUILDING A DATA LAKE * Introduction to Data Lakes. * Data Storage and ETL options on GCP. * Building a Data Lake using Cloud Storage. * Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. * Securing Cloud Storage. * Storing All Sorts of Data Types. * Video Demo: Running federated queries on Parquet and ORC files in BigQuery. * Cloud SQL as a relational Data Lake. * Lab: Loading Taxi Data into Cloud SQL. BUILDING A DATA WAREHOUSE * The modern data warehouse. * Intro to BigQuery. * Demo: Query TB+ of data in seconds. * Getting Started. * Loading Data. * Video Demo: Querying Cloud SQL from BigQuery. * Lab: Loading Data into BigQuery. * Exploring Schemas. * Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. * Schema Design. * Nested and Repeated Fields. * Demo: Nested and repeated fields in BigQuery. * Lab: Working with JSON and Array data in BigQuery. * Optimizing with Partitioning and Clustering. * Demo: Partitioned and Clustered Tables in BigQuery. * Preview: Transforming Batch and Streaming Data. INTRODUCTION TO BUILDING BATCH DATA PIPELINES * EL, ELT, ETL. * Quality considerations. * How to carry out operations in BigQuery. * Demo: ELT to improve data quality in BigQuery. * Shortcomings. * ETL to solve data quality issues. EXECUTING SPARK ON CLOUD DATAPROC * The Hadoop ecosystem. * Running Hadoop on Cloud Dataproc. * GCS instead of HDFS. * Optimizing Dataproc. * Lab: Running Apache Spark jobs on Cloud Dataproc. SERVERLESS DATA PROCESSING WITH CLOUD DATAFLOW * Cloud Dataflow. * Why customers value Dataflow. * Dataflow Pipelines. * Lab: A Simple Dataflow Pipeline (Python/Java). * Lab: MapReduce in Dataflow (Python/Java). * Lab: Side Inputs (Python/Java). * Dataflow Templates. * Dataflow SQL. MANAGE DATA PIPELINES WITH CLOUD DATA FUSION AND CLOUD COMPOSER * Building Batch Data Pipelines visually with Cloud Data Fusion. * Components. * UI Overview. * Building a Pipeline. * Exploring Data using Wrangler. * Lab: Building and executing a pipeline graph in Cloud Data Fusion. * Orchestrating work between GCP services with Cloud Composer. * Apache Airflow Environment. * DAGs and Operators. * Workflow Scheduling. * Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. * Monitoring and Logging. * Lab: An Introduction to Cloud Composer. INTRODUCTION TO PROCESSING STREAMING DATA * Processing Streaming Data. SERVERLESS MESSAGING WITH CLOUD PUB/SUB * Cloud Pub/Sub. * Lab: Publish Streaming Data into Pub/Sub. CLOUD DATAFLOW STREAMING FEATURES * Cloud Dataflow Streaming Features. * Lab: Streaming Data Pipelines. HIGH-THROUGHPUT BIGQUERY AND BIGTABLE STREAMING FEATURES * BigQuery Streaming Features. * Lab: Streaming Analytics and Dashboards. * Cloud Bigtable. * Lab: Streaming Data Pipelines into Bigtable. ADVANCED BIGQUERY FUNCTIONALITY AND PERFORMANCE * Analytic Window Functions. * Using With Clauses. * GIS Functions. * Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. * Performance Considerations. * Lab: Optimizing your BigQuery Queries for Performance. * Optional Lab: Creating Date-Partitioned Tables in BigQuery. INTRODUCTION TO ANALYTICS AND AI * What is AI?. * From Ad-hoc Data Analysis to Data Driven Decisions. * Options for ML models on GCP. PREBUILT ML MODEL APIS FOR UNSTRUCTURED DATA * Unstructured Data is Hard. * ML APIs for Enriching Data. * Lab: Using the Natural Language API to Classify Unstructured Text. BIG DATA ANALYTICS WITH CLOUD AI PLATFORM NOTEBOOKS * What's a Notebook. * BigQuery Magic and Ties to Pandas. * Lab: BigQuery in Jupyter Labs on AI Platform. PRODUCTION ML PIPELINES WITH KUBEFLOW * Ways to do ML on GCP. * Kubeflow. * AI Hub. * Lab: Running AI models on Kubeflow. CUSTOM MODEL BUILDING WITH SQL IN BIGQUERY ML * BigQuery ML for Quick Model Building. * Demo: Train a model with BigQuery ML to predict NYC taxi fares. * Supported Models. * Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. * Lab Option 2: Movie Recommendations in BigQuery ML. CUSTOM MODEL BUILDING WITH CLOUD AUTOML * Why Auto ML? * Auto ML Vision. * Auto ML NLP. * Auto ML Tables.

Duration 4 Days 24 CPD hours This course is intended for This course is for consultants, project team members, and administrators who want to learn how to implement data provisioning and data transformation for their SAP HANA project. In this course, students will learn the essential techniques and tools of data provisioning and data transformation for SAP HANA. This course will help students identify the most effective data provisioning solutions for their SAP HANA project. COURSE OUTLINE * Trigger-based replication with SAP Landscape Transformation * ETL based data provisioning using SAP Data Services * Connecting SAP HANA to data sources using SAP HANA Smart Data Access * Real-time data loading using Smart Data Streaming * ETL based loading using Smart Data Integration and Smart Data Quality * SAP HANA Direct Extractor Connection * Fundamentals of SAP Replication Server ADDITIONAL COURSE DETAILS: Nexus Humans HA350 SAP HANA - Data Management training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. 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. While we feel this is the best course for the HA350 SAP HANA - Data Management 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. 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.

Duration 1 Days 6 CPD hours This course is intended for This class is designed for administrators preparing to take the Salesforce Marketing Cloud Administrator exam who are able to configure Marketing Cloud products using industry and product best practices. You should be generally familiar with data structure in subscriber data management. You should also be able to thoroughly navigate Setup, troubleshoot account configuration, and manage user requests. Overview When you complete this course, you will be able to: Recall exam objectives. Discuss product features and functionality covered on the exam. Assess your exam readiness by answering practice questions. Familiarize yourself with additional resources necessary to prepare for the exam. Take the next step in your career and become a Salesforce Certified Marketing Cloud Administrator. In this 1-day, expert-led certification prep class, boost your exam readiness with a detailed exam overview, exam resources, and practice exam questions to test your knowledge. This course includes a voucher to sit for the Salesforce Marketing Cloud Administrator exam. COURSE OUTLINE * Exam Overview DIGITAL MARKETING PROFICIENCY * Review Governance and Compliance in Relation to Digital Marketing * Review Security Best Practices for Date, Permissions, and PII * Review Marketing Cloud Product Inventory and Offerings SUBSCRIBER DATA MANAGEMENT * Review the Contact Model * Review Data Quality Evaluation * Review Preference and Profile Center SETUP * Review Business Units, Users, and Security Configuration * Review Integrations Configuration * Review Features in Setup Home * Review Marketing Cloud Extension Products CHANNEL MANAGEMENT * Review Mobile Studio Configuration * Review Email Studio Configuration * Review Social Studio and Advertising Configuration * Review Journey Builder Concepts and Use Cases MAINTENANCE * Review Data Extraction and Report Generation Solutions Review Monitoring and System Availability Review Additional Marketing Cloud Product Benefits PRACTICE EXAM AND WRAP-UP * Complete a Practice Exam Review Next Steps

Prepare for success with the Microsoft PL-900 Certification Course, covering the fundamentals of Power Platform, including Power BI, Power Apps, Power Automate, Power Virtual Agents, and related topics such as Dataverse, AI Builder, Connectors, Dynamics 365, Teams, Security, and Administration. Suitable for beginners with no prerequisites.

Duration 2 Days 12 CPD hours This course is intended for The primary audience for this course are Application Consultants/Modelers and Project team members. Overview Get an overview of SAP HANA SPS09 and in-memory computing.Build an analytic data model with native HANA modeling tools.Understand the different approaches to provision data into SAP HANA.Learn how to connect to SAP HANA and consume HANA models. In this course, students get an overview of SAP HANA SPS09 and in-memory computing. Students will also gain an understanding of the different approaches to provision data into SAP HANA. KEY CONCEPTS OF SAP HANA WORKING WITH SAP HANA STUDIO ARCHITECTURE OF SAP IN-MEMORY COMPUTING MODELING WITH SAP HANA * Attribute Views * Analytic Views * Calculation Views OVERVIEW OF DATA PROVISIONING IN SAP HANA WITH THE TOOLS * Flat file upload * SAP BusinessObjects Data Services * SAP Landscape Transformation Replication Server * SAP Replication Server * SAP Direct Extractor Connection * Smart Data Access * Smart Data Integration / Smart Data Quality * Smart Data Streaming SAP HANA INTERFACES TO BI CLIENT TOOLS * SAP BusinessObjects Analysis for Office * SAP Design Studio * SAP Lumira ADDITIONAL COURSE DETAILS: Nexus Humans HA100 SAP HANA - Introduction training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. 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. While we feel this is the best course for the HA100 SAP HANA - Introduction 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. 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.

Duration 1 Days 6 CPD hours This course is intended for This course is ideal for any administrator with an interest in furthering the development of their Salesforce CRM administration, Sales and Service Cloud management, and process automation skills, and who ultimately wants to succeed at the Salesforce Certified Advanced Administrator exam. Overview When you complete this course, you will be able to:Configure data and application security.Describe Sales Cloud and Service Cloud applications.Implement business logic and process automation.Build advanced reports and dashboards.Apply data management best practices. This course will help hone your knowledge of of next-level techniques to administer and manage Salesforce?s CRM capabilities through guided scenarios, lecture, and discussion. SALESFORCE SECURITY AND CUSTOM OBJECTS * Restricting and extending object, record, and field access * Determining appropriate sharing solutions * Territory Management * Data relationships AUTOMATION, CHANGE MANAGEMENT, AND AUDITING * Process automation tools and best practices * Change management options * Sandboxes * Deployment tools * Auditing and monitoring ANALYTICS AND DATA MANAGEMENT * Creating reports * Report types * Dashboards * Data quality features and policies SALES, SERVICE, AND CONTENT APPLICATIONS * Products, price books, schedules and quotes * Forecasting * Salesforce Knowledge * Entitlements * Service Cloud console toolkit * Content management WRAPPING * Test preparation * Practice exam ADDITIONAL COURSE DETAILS: Nexus Humans Salesforce Certification Preparation for Advanced Administrator (CRT211) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. 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. While we feel this is the best course for the Salesforce Certification Preparation for Advanced Administrator (CRT211) 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. 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.

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