Data Management - Online

• Getting Started

• Working with Charts and Sparklines

• Using Formatting, Styles, and Themes

• Linking, Printing, and Protecting Workbooks

• Validating, Cleaning, and Performing Lookups on Data

• What-if Analysis, Solver, and Analysis ToolPak

• Pivot, PowerPivot, and Financial Modeling

• LAB: Business Analyst

• FINAL EXAM: Business Analyst

• Tableau for Data Visualization: Introduction

• Tableau for Data Visualization: Exploring Visualizations and Data Formats

• Tableau for Data Visualization: Advanced Features

• Business Reporting: Getting Started with Power BI Desktop for Data Analysis

• Business Reporting: Visualizing and Merging Data in Power BI

• Business Reporting: Creating and Formatting Matrix Visualizations in Power BI

• Business Reporting: Leveraging Treemaps, Matrices, & Slicers in Power BI

• LAB: Decision Analyst

• FINAL EXAM: Decision Analyst

• Google Chart Tools: Basic Charts

• Google Chart Tools: Interacting with Charts

• Google Chart Tools: Advanced Visuals with Charts

• Plotly for Data Visualization: an Introduction to Plotly Chart Studio

• Plotly for Data Visualization: Exploring Chart Studio Visualization

• Plotly for Data Visualization: Advanced Charts and Features in Chart Studio

• SQL Programming with MariaDB: Getting Started with MariaDB for Data Analysis

• SQL Programming with MariaDB: Analyzing Relational Data

• SQL Programming with MariaDB: Using Joins, Triggers, and Stored Procedures

• LAB: Systems Analyst

• FINAL EXAM: Systems Analyst

• VBA: Getting Started with VBA in Excel

• VBA: Building User Interfaces with Forms in VBA and Excel

• VBA: Leveraging VBA to Work with Charts, Stocks, and MS Access

• Using BigML: an Introduction to Machine Learning and BigML

• Using BigML: Getting Hands-on with BigML

• Using BigML: Building Supervised Learning Models

• Using BigML: Unsupervised Learning

• Analyzing Data Using Python: Data Analytics Using Pandas

• Analyzing Data Using Python: Importing, Exporting, and Analyzing Data withPandas

• Analyzing Data Using Python: Filtering Data in Pandas

• Analyzing Data Using Python: Cleaning and Analyzing Data in Pandas

• LAB: Data Analyst

• FINAL EXAM: Data Analyst

• Introducing Sets and Set Operations

• Introducing Graphs and Graph Operations

• Solving Optimization Problems Using Linear Programming

• Solving Optimization Problems Using Integer Programming

• Getting Started with Derivatives

• Derivatives with Linear and Quadratic Functions and Partial Derivatives

• Understanding Integration

• Exploring Linear Algebra

• Getting Started with Matrix Decomposition

• Using Eigen Decomposition and Singular Value Decomposition

• FINAL EXAM: Introduction to Math

• An Overview of Statistics and Sampling

• Statistics and Sampling with Python

• Getting Started with Probability

• Understanding Joint, Marginal, and Conditional Probability

• Creating Bayesian Models

• Getting Started with Probability Distribution

• Uniform, Binomial, and Poisson Distributions

• Understanding Normal Distributions

• Getting Started with Hypothesis Testing

• Using the One-sample T-test

• Performing Two-sample T-tests and Paired T-Tests

• Using Non-parametric Tests and ANOVA Analysis

• FINAL EXAM: Statistics and Probability

• Getting Started with Linear Regression

• Using Gradient Descent and Logistic Regression

• An Exploration of Decision Trees

• Overview of Distance-based Metrics and Algorithms

• Implementing Distance-based Algorithms

• A Conceptual Look at Support Vector Machines

• Building and Applying SVM Models in Python

• Understanding the Mathematics of a Neuron

• Exploring the Math Behind Gradient Descent

• FINAL EXAM: Math Behind ML Algorithms

• Performing Principal Component Analysis

• Under the Hood of Recommendation Systems

• FINAL EXAM: Advanced Math

• Data Architecture Primer

• Data Engineering Fundamentals

• Python for Data Science: Introduction to NumPy for Multi-dimentional Data

• Python for Data Science: Advanced Operations with NumPy Arrays

• Python for Data Science: Introduction to Pandas

• Python for Data Science: Manipulating & Analyzing Data in Pandas DataFrames

• R for Data Science: Data Structures

• R for Data Science: Importing & Exporting Data

• R for Data Science: Data Exploration

• R for Data Science: Regression Models

• R for Data Science: Classification & Clustering

• Data Science Statistics: Simple Descriptive Statistics

• Data Science Statistics: Common Approaches to Sampling Data

• Data Science Statistics: Inferential Statistics

• An Introduction to Spark

• Getting Started with Hadoop: Fundamentals & MapReduce

• Getting Started with Hadoop: Developing a Basic MapReduce Application

• Hadoop HDFS: Introduction

• Hadoop HDFS: Introduction to the Shell

• Hadoop HDFS: Working with Files

• Hadoop HDFS: File Permissions

• Data Silos, Lakes, & Streams: Introduction

• Data Silos, Lakes, & Streams: Data Lakes on AWS

• Data Silos, Lakes, & Streams: Sources, Visualizations, & ETL Operations

• Data Analysis Application

• Practice Labs: Analyzing Data with Python

• Final Exam: Data Analyst

Data Wrangling with Pandas

• Working with Series & DataFrames

• Visualizations & Time-series Data

• Advanced Features

Data Wrangler 4: Cleaning Data in R

Data Tools:

• Technology Landscape & Tools for Data Management

• Machine Learning & Deep Learning in the Cloud

Trifacta for Data Wrangling: Wrangling Data

MongoDB for Data Wrangling:

• Querying

• Aggregation

Getting Started with Hive

• Introduction

• Loading & Querying Data

• Viewing & Querying Complex Data

• Optimizing Query Executions

• Optimizing Query Executions with Partitioning

• Bucketing & Window Functions

Getting Started with Hadoop:

• Filtering Data Using MapReduce

• MapReduce Applications with Combiners

• Advanced Operations using MapReduce

Accessing Data with Spark:

• Data Analysis Using the Spark DataFrame API

• Data Analysis Using Spark SQL

Data Lake:

• Framework & Design Implementation

• Architectures & Data Management Principles

Data Architecture – Deep Dive:

• Design & Implementation

• Microservices & Serverless Computing

Practice Labs: Data Wrangling with Python

Final Exam: Data Wrangler

• Deploying Data Tools: Data Science Tools

• Delivering Dashboards: Management Patterns

• Delivering Dashboards: Exploration & Analytics

• Cloud Data Architecture: DevOps & Containerization

• Compliance Issues & Strategies: Data Compliance

• Implementing Governance Strategies

• Data Access & Governance Policies: Data Access Oversight & IAM

• Data Access & Governance Policies: Data Classification, Encryption, & Monitoring

• Streaming Data Architectures: Introduction to Streaming Data

• Streaming Data Architectures: Processing Streaming Data

• Scalable Data Architectures: Introduction

• Scalable Data Architectures: Introduction to Amazon Redshift

• Scalable Data Architectures: Working with Amazon Redshift & QuickSight

• Building Data Pipelines

• Data Pipeline: Process Implementation Using Tableau & AWS

• Data Pipeline: Using Frameworks for Advanced Data Management

• Data Sources: Integration

• Data Sources: Implementing Edge on the Cloud

• Securing Big Data Streams

• Harnessing Data Volume & Velocity: Big Data to Smart Data

• Data Rollbacks: Transaction Rollbacks & Their Impacts

• Data Rollbacks: Transaction Management & Rollbacks in NoSQL

• Practice Labs: Implementing Data Ops with Python

• Final Exam: Data Ops

• The Four Vs of Data

• Data Driven Organizations

• Data Management & Decision Making

• Tableau Desktop: Real Time Dashboards

• Storytelling with Data: Introduction

• Storytelling with Data: Tableau & PowerBI

• Python for Data Science: Basic Data Visualization Using Seaborn

• Python for Data Science: Advanced Data Visualization Using Seaborn

• Using Python to Compute & Visualize Statistics

• Advanced Visualizations & Dashboards: Visualization Using Python

• Advanced Visualizations & Dashboards: Visualization Using R

• R for Data Science: Data Visualization

• Recommendation Engines

• Handling Anomalies

• ML & Visualization Tools

• Applied Inferential Statistics

• Data Research Techniques

• Data Research Exploration Techniques

• Data Research Statistical Approaches

• Machine & Deep Learning Algorithms: Introduction

• Machine & Deep Learning Algorithms: Regression & Clustering

• Machine & Deep Learning Algorithms: Data Preparation in Pandas ML

• Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML

• Creating Data APIs using Node.JS

• Practice Labs: Implementing Data Ops with Python

• Final Exam: Data Scientist

• Best Practices for Creating Visuals

• Getting Started with Excel for Data Visualization

• Building Column Charts, Bar Charts, & Histograms

• Visualizing Data Using Line Charts & Area Charts

• Plotting Stock Charts, Radar Charts, Treemaps, & Donuts

• Building Box Plots, Sunburst Plots, Gantt Charts, & More

• Final Exam: Data Visualization with Excel

• QlikView: Getting Started with QlikView for Data Visualization

• QlikView: Creating Line Charts, Combo Charts, Pivot Tables, & Block Charts

• QlikView: Creating Mekko Charts, Radar Charts, Gauge Charts, & Scatter Charts

• Practice Lab: Data Visualization with Excel and BI Tools

• Final Exam: Data Visualization with BI Tools

• Infogram: Getting Started

• Infogram: Advanced Features

• Visme: Introduction

• Visme: Exploring Charts

• Visme: Designing a Presentation

• Final Exam: Creating Infographics for Data Visualization

• Python & Matplotlib: Getting Started with Matplotlib for Data Visualization

• Python & Matplotlib: Creating Box Plots, Scatter Plots, Heatmaps, & Pie Charts

• Data Visualization: Building Interactive Visualizations with Bokeh

• Data Visualization: More Specialized Visualizations in Bokeh

• Data Visualization: Getting Started with Plotly

• Data Visualization: Visualizing Data Using Advanced Charts in Plotly

• Practice Lab: Creating Infographics and Data Visualization with Python

• Final Exam: Data Visualization with Python

• Getting Started

• Exploring R Vectors

• Leveraging R with Matrices, Arrays, and Lists

• Understanding Data Frames, Factors, and Strings

• Final Exam: Getting Started with R Programming

• Leveraging R with Control Flow and Looping

• Functions and Environments

• Object Systems

• Final Exam: Applying and Using R Programming Structures

• Loading and Saving Data

• Transforming Data

• Selecting, Filtering, Ordering, and Grouping Data

• Joining and Visualizing Data

• Final Exam: Working with Datasets in R

• Working with Probability Distributions

• Understanding and Interpreting Statistical Tests

• Statistical Analysis on Your Data

• Performing Regression Analysis

• Performing Classification

• Performing Clustering

• Building Regularized Models and Ensemble Models

• Final Exam: Statistical Analysis and Modeling in R

• Understanding Graphs and Knowledge Graphs

• Representing Graphs using Matrices, Lists, and Sets

• Implementing Graph Traversal and Shortest Path Algorithms

• An Introduction to Graph Databases and Neo4j

• Administering a Neo4j Database

• Managing Databases with the Neo4j Browser

• Creating Nodes and Relationships with Cypher

• Basic Reads and Writes with Cypher

• Advanced Operations with Cypher

• Analyzing Graphs

• An Introduction to Modeling Graphs

• Automating and Refactoring Graph Models

• The AuraDB Cloud Database Service

• Building Graphs with Neo4j Graph Data Science Library

• Managing Graphs with the Graph Data Science Library

• Applying Graph Algorithms on In-Memory graphs

• Working with Apache Spark Graph Frames

• An Introduction to Graph Neural Networks

• Classifying Graph Nodes with the Spectral Library

• Final Exam: Graph Analytics

• Fundamentals of Data

• Relational Databases

• Data Warehousing and ETL Systems

• Factors Driving Data Infrastructures

• Final Exam: Data Primer

• Getting to Know Big Data

• Big Data Essentials

• Non-relational Databases

• Techniques for Big Data Analytics

• Spark for High-speed Big Data Analytics

• Final Exam: Big Data Infrastructures

• Modern Data Science Lifecycle

• Data Preparation and Predictive Analytics

• Data Mining for Answering Business Questions

• Predictive Analytics for Business Strategies

• Final Exam: Raw Data to Insights

• Cloud Computing

• Cloud-based Applications and Storage

• AWS, Azure, and GCP comparison

• Data Lakes

• Modern Data Warehouses

• Azure Databricks and Data Pipelines

• Final Exam: Emerging New Age Architectures

• Data Management Systems

• Data Governance

• Data Quality Management

• Final Exam: Data Governance and Management

• Data Workloads

• Data Analytics

• Relational Data Workloads

• Relational Data Management

• Provisioning and Configuring Relational Data Services

• Azure SQL Querying TechniquesNon-Relational Data Workloads

• Azure Analytics Workloads

• Modern Data Warehousing

• Azure Data Ingestion & Processing

• Azure Data Visualization

Level: Beginner

Role: Data Engineer

Duration: 45 Minutes

Exam policy

This exam will be proctored, and is not open book. You may have interactive components to complete as part of this exam. To learn more about exam duration and experience, visit: Exam duration and exam experience.

If you fail a certification exam, don’t worry. You can retake it 24 hours after the first attempt. For subsequent retakes, the amount of time varies. For full details, visit: Exam retake policy.

• Machine Learning

• ML Services

• ML Regression Models

• ML Classification Models

• ML Clustering Models

• Project Jupyter & Notebooks

• Azure ML Workspaces

• Azure Data Platform Services

• Azure Storage Accounts

• Storage Strategy

• Azure Data Factory

• Non-relational Data Stores

• ML Data Stores & Compute

• ML Orchestration & Deployment

• Model Features & Differential Privacy

• ML Model Monitoring

• Azure Data Storage Monitoring

• Data Process Monitoring

• Data Solution Optimization

• High Availability & Disaster Recovery

• Storage Accounts

• Designing Data Storage Structures

• Data Partitioning

• Designing the Serving Layer

• Physical Data Storage Structure

• Logical Data Structures

• The Serving Layer

• Data Policies & Standards

• Securing Data Access

• Securing Data

• Data Lake Storage

• Data Flow Transformations

• Data Factory

• Databricks

• Databrick Processing

• Stream Analytics

• Synapse Analytics

• Data Storage Monitoring

• Data Process Monitoring

• Data Solution Optimization