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Data Science and Big Data Analytics

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 Proven Professional Data Scientist Associate (EMCDSA) certification exam, and establish a baseline of Data Science skills.

  • Skills Gained
  • Who Can Benefit
  • Prerequisites
  • Syllabus
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

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 EMC Proven Professional Data Scientist Associate (EMCDSA) certification

A strong quantitative background with a solid understanding of basic statistics, as would be found in a statistics 101 level course
Experience with a scripting language, such as Java, Perl, or Python (or R)
Experience with SQL

 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
  • Nave 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
 EMC

  • Duration : 5 days
  • Certified by: EMC

In this course, you will gain practical foundation level training that enables immediate and effective participation in big data and other analytics projects.

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