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Big Data Management and Analytics        Management      Big Data

Course Description

Data analytics and data science are popular terms, and skills in these areas are in great demand. Data Analytics means apply analytics/rules on data and find/organize Big Data in meaningful form for business users to make data driven decisions. In predictive modeling (also called predictive analytics) we seek to predict the value of a variable of interest (purchase/no purchase, fraudulent/not fraudulent, malignant/benign, amount of spending, etc.) by using "training" data where the value of this variable is known.  Once a statistical model is built with the training data ("trained"), it is then applied to data where the value is unknown.


  • Students should have basic statistics and database knowledge

Course Content
  • Statistics Overview

  • Descriptive Statistics vs Visual Statistics 

  • Data Distribution: Normal, Triangular, Uniform and more

  • @Risk Monte Carlo

  • Linear Problem solving using Excel Solver

  • Linear Regression [ANOVA], Correlation, Classification,

  • Product Recommendation Techniques 

  • Forecasting/Prediction Techniques/Algorithms

  • ETL [Extract, Transform, Load] Architecture

  • R - Programming for data visualization 

  • Visualization tools: Tableau/Weka/Excel 

  • Database vs Data Warehouse vs Big Data

  • OLTP vs OLAP use cases 

  • Case studies: data volume, velocity, varieties 

  • APM [Asset Performance Monitoring] use cases

  • Supervised/Non-supervised learning

  • Machine Learning/Predictive Analysis

  • Hadoop Technology Overview

  • Project work with [R, Python, MongoDB, Neo4J, @Risk]

Who Should Enroll

​Anyone from data analyst to SVP-level who is looking for a deeper understanding of how to perform the statistical analyses that support key business decisions.

Staffing Support​
  • Resume Preparation

  • Mock Interview Preparation

  • Phone Interview Preparation

  • Face to Face Interview Preparation

  • Project/Technology Preparation

  • Internship with internal project work

  • Externship with client project work

Our Salient Features:
  • Hands-on Labs and Homework

  • Group discussion and Case Study

  • Course Project work

  • Regular Quiz / Exam

  • Regular support beyond the classroom

  • Students can re-take the class at no cost

  • Dedicated conf. rooms for group project work

  • Live streaming for the remote students

  • Video recording capability to catch up the missed class

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