IT Expert System | Data Analytics

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.