top of page

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.

 

Pre-requisite

  • 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.

bottom of page