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Course Objectives

  • Understand and apply statistical concepts into business analytics.
  • Explore and prepare data for business analytics
  • Describe the relationship between variables and construct predictive model for spatial and time series data
  • Apply data mining approaches to extract useful business information from data
  • Use tools like R programming and MS Excel to interpret data

Description

This is a three days course. Whether you are in finance, operations, sales & marketing or planning, you may be in touch with millions of data points every day without being aware of how to derive valuable information from this data. Business Analytics thus finds favor as it utilizes tools and techniques like data mining, pattern matching, data visualizations and predictive modeling to predict and optimize outcomes and derive value from the data. Equipped with this useful information, organizations can compete better in cut-throat markets both locally and globally.

Target Audience

This training is ideal for both researchers who focus on algorithms, and for professionals who intend to work on data analytics applications such as critical product analysis, targeted marketing, customer lifecycle management, social media analytics, fraud detection, and inventory management.

Training Outline

Introduction to Analytics
  • Differences between Analysis and Analytics
  • Types of Analytics
  • Business domains within Analytics
  • Challenges in Data Science
  • Data Products
Data Exploration
  • Variable and Data Types
  • Data Management and Cleaning
  • Data Summarization and Visualization
Probability Concept and Statistical Inference
  • Random Variables
  • Probability Distribution
  • Sampling Distribution
  • Inferential Statistics: Hypothesis Testing and
  • Confidence Intervals
Data Acquisition
  • Introduction to R-programming
  • Loading data from local files (using R and MS Excel)
  • Importing data from external sources (using R and MS Excel)
  • Case Study 1: Employee Training Needs Analysis
Investigating Relationship
  • Pearson’s correlation
  • Spearman rank correlation
  • Contingency tables and Chi-squares test
Predictive Modeling Using Regression
  • Simple Linear Regression
  • Multivariate Linear Regression (using R and MS Excel)
  • Goodness of fit of the model: ANOVA and R-Square
  • Model Checking
  • Model Selection
  • Dummy Variable in regression
  • Nonlinear regression and curve estimation
  • Logistic Regression analysis (using R and MS Excel)
  • Multicollinearity and Heteroscedasticity issues with Gini coefficient, vif (using R and MS Excel)
Trend Analysis
  • Time series data
  • Autocorrelation function (ACF)
  • Moving average and exponential Smoothing Methods
  • AR, MA, ARMA, and ARIMA models
  • Case Study 2: Predicting House Selling Price and Forecasting Stock Market Returns
Classification
  • Similarity measures
  •  k-Nearest Neighbors Algorithm
  • Naïve Bayes Algorithm
  • Neural Network
  • Support Vector Machine
Performance Evaluation and Validation
  • Confusion Matrix, ROC and LOF
  • Split and Cross Validation
  • Normalization
Cluster Analysis
  • k-Means Clustering

Prerequisite

Nil.