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Course Objectives

  • Understand and apply statistical concepts into business analytics.
  • Explore and prepare data for business analytics
  • Identify data structures and describe relationship between variables
  • Construct predictive model for spatial data
  • Apply data mining approaches to extract useful business information from data
  • Interpret data using statistical software

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

Data Science and Analytics
  • Introduction to Data Science:
    • The Rise of Big Data – Datafication
    • Challenges in Data Science
    • Data Science Skill Sets
    • Data Mining Process: CRISP-DM
  • Introduction to Analytics
    • Big Data Analytics
    • Analysis vs Analytics
    • Types of Analytics
    • Business Application with Analytics
  • Statistical Inference and Concept
    • Statistical Thinking in Age of Big Data
    • Population and Samples of Big Data
    • Type of Data and Variables
Data Exploration and Preparation
  • Data Visualization
    • Descriptive Statistics
    • Graphical Displays
  • Data Management
    • Data Errors and Treatment
    • Missing Values Imputation
    • Outliers Identification
Unsupervised Learning
  • Introduction to Statistical Learning
    • Supervised vs Unsupervised
    • Regression vs Classification
  • Clustering Analysis
    • Distance Measure
    • Normalization
    • k-Mean Clustering
  • Case study: Customer Segmentation
Association Analysis
  • Investigating Relationship
    • Pearson’s Correlation
    • Spearman Rank Correlation
    • Contingency Table
  • Application and Interpretation
    • Association Rules
    • Results Interpretation
  • Case study 2: Market Basket Analysis
Regression Analysis
  • Introduction to Supervised Learning
    • Concept of Prediction
    • Bias Variance Trade-Off
  • Linear Regression
    • Simple Linear Regression
    • Multiple Linear Regression
    • Dummy Variables
    • Performance Evaluation
    • Model Selection
    • Results Interpretation
  • Case study 3: House Price Prediction
Classification
  • Introduction to Classification:
    • Validation Approaches
    • Performance Evaluation
  • Classification Techniques:
    • Logistic Regression
    • 𝑘-Nearest Neighbours
    • Naïve Bayes
    • Linear Discriminant Analysis
    • Decision Tree
    • Neural Network
    • Support Vector Machine
  • Case study 4: Fraud Detection

Prerequisite

Nil.