CVF 2206 - Data Science and Big Data Analytics

1. contribute to a data science team.
2. reframe a business challenge as an analytics challenge.
3. deploy a structured lifecycle approach to data analytics problems.
4. apply appropriate analytic techniques and tools to analyze big data.
5. develop a compelling story with the data to drive business action.
6. use open source tools such as R, Hadoop, and Postgres.
7. prepare for EMC ProvenTM Professional Data Scientist certification.

1. Introduction to Big Data Analytics
   • Big Data Overview
   • State of the practice in analytics
   • The Role of the Data scientist
   • Big Data Analytics in industry verticals
2. End-to-end data analytics lifecycle
   • Key role for a successful analytics project
   • Main phases of the lifecycle
   • Developing core deliverables for stakeholders
3. Using R to execute basic analytics methods
   • Introduction to R
   • Analyzing and exploring data with R
   • Statistics for mode building and evaluation
4. Advance analytics and statistical modeling for Big Data - Theory and methods
   • K-Means Clustering
   • Association rules
   • Linear and logistic regression
   • Naive Bayesian classifier
   • Decision tree
   • Time series analysis
   • Text analysis
5. Advance analytics and statistical modeling for Big Data - Technology and Tools
   • Using MapReduce / Hadoop for analyzing unstructured data
   • Hadoop ecosystem of tools
   • In-database analytics
   • MADlib and advanced SQL Techniques
6. Endgame, or Putting it all together
   • How to operationalize an analytics project
   • Creating the final deliverables
   • Data visualization techniques
   • Hands-on Application of analytics lifecycle to a Big Data Analytics problem

Courses and Registration