Data Science
& Visualization

with DP-100 & PL-300 Exam Prep

ABOUT THE PROGRAM

Data Science provides meaningful information based on large amounts of complex data or big data. Data science, or data-driven science, combines different fields of work in statistics and computation to interpret data for decision-making purposes.

It is becoming more common to present data in a visual way to engage audiences with what the data is saying. To ensure the audiences understand what is being presented, effective data visualizations need to be created, and this all start with knowing who your audience is and what your data is all about.

This is a self-paced program. Self-paced programs create a unique learning experience that allows students to learn independently and at a pace that best suits them.

    • Data Visualization Engineer

    • Data Scientist

    • Data Analyst

    • Data Engineer

    • Data Architect

    • Data Storyteller

Certifications

This program prepares the student to take the following certification exams:

  • DP-100: Designing & Implementing a Data Science Solution on Azure

  • PL-300: Microsoft Power BI Data Analyst

Exam vouchers are not included in tuition.

The certification exam is not a requirement for graduation. Vouchers may be available depending on the student’s funding and financial aid.

Tuition: $3,497

To learn more about ETI’s tuition and financial aid options, click here.

Duration: 200 Hours

This program includes e-books, virtual labs, and mentor support.

Students will have access to the program for 1 full year.

Prerequisites: HS Diploma/GED, basic PC skills and familiarity with the Internet

Course Breakdown

Data Science

    • 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

    • Data Wrangling with Pandas: Visualizations & Time-series Data

    • Data Wrangling with Pandas: Advanced Features

    • Data Wrangler 4: Cleaning Data in R

    • Data Tools: Technology Landscape & Tools for Data Management

    • Data Tools: Machine Learning & Deep Learning in the Cloud

    • Trifacta for Data Wrangling: Wrangling Data

    • MongoDB for Data Wrangling: Querying

    • MongoDB for Data Wrangling: Aggregation

    • Getting Started with Hive: Introduction

    • Getting Started with Hive: Loading & Querying Data

    • Getting Started with Hive: Viewing & Querying Complex Data

    • Getting Started with Hive: Optimizing Query Executions

    • Getting Started with Hive: Optimizing Query Executions with Partitioning

    • Getting Started with Hive: Bucketing & Window Functions

    • Getting Started with Hadoop: Filtering Data Using MapReduce

    • Getting Started with Hadoop: MapReduce Applications with Combiners

    • Getting Started with Hadoop: Advanced Operations using MapReduce

    • Accessing Data with Spark: Data Analysis Using the Spark DataFrame API

    • Accessing Data with Spark: Data Analysis Using Spark SQL

    • Data Lake: Framework & Design Implementation

    • Data Lake: Architectures & Data Management Principles

    • Data Architecture – Deep Dive: Design & Implementation

    • Data Architecture – Deep Dive: 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

    • Machine Learning

    • ML Services

    • ML Regression Models

    • ML Classification Models

    • ML Clustering Models

    • Project Jupiter & Notebooks

    • Azure ML Workspaces

    • Azure Data Platform Services

    • Azure Storage Accounts

    • Storage Strategies

    • 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

Data Visualization

    • 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

  • Visualize and Interpret Data in Power BI

    • Understanding Data Visualization

    • Creating & Formatting Charts in Power BI

    • Leveraging Power BI with Ribbon, Line, Column & Pie Charts

    • Maps, Waterfall Charts, & Scatter Plots in Power BI

    • Matrix & Treemap Controls in Power BI

    • Using the Power BI Service

    Analyze and Share Data with Power BI

    • Analysis & Sharing Features in Power BI

    • Extracting Insights from Data Using Power BI

    • Applying Power BI’s Advanced Analysis Features

    • Sharing Power BI Reports & Workspaces