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Data Science and Big Data Analytics

Discover how to use big data and the Data Analytics Lifecycle to address your business challenges.

GK# 4447

Course Overview

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In this course, you will gain practical foundation level training that enables immediate and effective participation in big data and other analytics projects. You will cover basic and advanced analytic methods and big data analytics technology and tools, including MapReduce and Hadoop. The extensive labs throughout the course provide you with the opportunity to apply these methods and tools to real world business challenges. This course takes a technology-neutral approach. In a final lab, you will address a big data analytics challenge by applying the concepts taught in the course to the context of the Data Analytics Lifecycle. You will prepare for the Data Scientist Associate (EMCDSA) certification exam, and establish a baseline of Data Science skills.

Schedule

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  • Delivery Format:
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What You'll Learn

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  • Deploy the Data Analytics Lifecycle to address big data analytics projects
  • Reframe a business challenge as an analytics challenge
  • Apply appropriate analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results
  • Select appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audiences
  • Use R and RStudio, MapReduce/Hadoop, in-database analytics, Windows, and MADlib functions
  • Use advanced analytics create competitive advantage
  • Data scientist role and skills vs. traditional business intelligence analyst

Outline

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Viewing outline for:

Virtual Classroom Live Outline

The following modules and lessons included in this course are designed to support the course objectives:

Introduction and Course Agenda

Introduction to Big Data Analytics

  • Big Data Overview
  • State of the Practice in Analytics
  • The Data Scientist
  • Big Data Analytics in Industry Verticals

Data Analytics Lifecycle

  • Discovery
  • Data Preparation
  • Model Planning
  • Model Building
  • Communicating Results
  • Operationalizing

Review of Basic Data Analytic Methods Using R

  • Using R to Look at Data – Introduction to R
  • Analyzing and Exploring the Data
  • Statistics for Model Building and Evaluation

Advanced Analytics – Theory And Methods

  • K Means Clustering
  • Association Rules
  • Linear Regression
  • Logistic Regression
  • Naïve Bayesian Classifier
  • Decision Trees
  • Time Series Analysis
  • Text Analysis

Advanced Analytics - Technologies and Tools

  • Analytics for Unstructured Data - MapReduce and Hadoop
  • The Hadoop Ecosystem
  • In-database Analytics – SQL Essentials
  • Advanced SQL and MADlib for In-database Analytics

The Endgame, or Putting it All Together

  • Operationalizing an Analytics Project
  • Creating the Final Deliverables
  • Data Visualization Techniques
  • Final Lab Exercise on Big Data Analytics

Labs

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Viewing labs for:

Virtual Classroom Live Labs

In addition to the examples provided in the lectures, this course includes labs to allow practical experience for the participant. Note: There are no demonstrations.

1. Big Data Analytics

  • Big Data
  • State of the Practice in Analytics
  • Data Scientist
  • Big Data Analytics in Industry Verticals

2. Data Analytics Lifecycle

  • Discovery
  • Data Preparation
  • Model Planning
  • Model Building
  • Communicating Results
  • Operationalizing

3. Basic Data Analytic Methods Using R

  • Using R to Look at Data
  • Analyzing and Exploring the Data
  • Statistics for Model Building and Evaluation

4. Advanced Analytics: Theory and Methods

  • K Means Clustering
  • Association Rules
  • Linear Regression
  • Logistic Regression
  • Naïve Bayesian Classifier
  • Decision Trees
  • Time Series Analysis
  • Text Analysis

5. Advanced Analytics: Technologies and Tools

  • Analytics for Unstructured Data
    • MapReduce and Hadoop
  • Hadoop Ecosystem
    • In-Database Analytics: SQL Essentials
    • Advanced SQL and MADlib for In-Database Analytics

6. Putting it All Together

  • Operationalizing an Analytics Project
  • Creating the Final Deliverables
  • Data Visualization Techniques
  • Final Lab Exercise on Big Data Analytics

Who Should Attend

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  • Managers of teams of business intelligence, analytics, and big data professionals
  • Current business and data analysts looking to add big data analytics to their skills
  • Data and database professionals looking to exploit their analytic skills in a big data environment
  • Recent college graduates and graduate students with academic experience in a related discipline looking to move into the world of Data Science and big data
  • Individuals looking to take the Data Scientist Associate (EMCDSA) certification

Follow-On Courses

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Course Delivery

This course is available in the following formats:

Virtual Classroom Live

Experience expert-led online training from the convenience of your home, office or anywhere with an internet connection.

Duration: 5 day

Request this course in a different delivery format.
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