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.

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