Business Analytics Curriculum (30 Hours)

Module 1: Introduction to Business Analytics (3 hours)

  • Topics Covered:
    • Definition and Importance of Business Analytics
    • Types of Analytics: Descriptive, Predictive, Prescriptive
    • Role of Data in Business Analytics
    • Overview of Business Analytics Tools and Software

Module 2: Data Collection and Data Management (3 hours)

  • Topics Covered:
    • Sources of Data
    • Data Collection Methods
    • Data Warehousing and Data Lakes
    • Data Cleaning and Pre-processing

Module 3: Exploratory Data Analysis (3 hours)

  • Topics Covered:
    • Descriptive Statistics
    • Data Visualization Techniques
    • Identifying Patterns and Trends
    • Hypothesis Testing

Module 4: Statistical Analysis (3 hours)

  • Topics Covered:
    • Probability Distributions
    • Inferential Statistics
    • Regression Analysis
    • Time Series Analysis

Module 5: Predictive Analytics (4 hours)

  • Topics Covered:
    • Introduction to Predictive Modeling
    • Linear and Logistic Regression
    • Decision Trees
    • Machine Learning Algorithms

Module 6: Prescriptive Analytics (4 hours)

  • Topics Covered:
    • Optimization Techniques
    • Simulation Modeling
    • Scenario Analysis
    • Decision Analysis

Module 7: Data Visualization and Reporting (3 hours)

  • Topics Covered:
    • Principles of Effective Data Visualization
    • Advanced Charting Techniques
    • Dashboards and Interactive Reports
    • Data Storytelling

Module 8: Big Data Analytics (3 hours)

  • Topics Covered:
    • Introduction to Big Data
    • Hadoop and Spark Frameworks
    • NoSQL Databases
    • Big Data Analytics Tools

Module 9: Analytics in Business Domains (4 hours)

  • Topics Covered:
    • Marketing Analytics
    • Financial Analytics
    • Operations Analytics
    • HR Analytics

Module 10: Ethics and Data Privacy (2 hours)

  • Topics Covered:
    • Ethical Considerations in Business Analytics
    • Data Privacy Regulations (GDPR, CCPA)
    • Ensuring Data Security
    • Responsible Data Usage

Capstone Project 1: Customer Segmentation Analysis (3 hours)

  • Objective:
    • Perform customer segmentation using a dataset of customer transactions.
    • Apply clustering techniques to identify distinct customer groups.
    • Provide actionable insights for targeted marketing strategies.
  • Skills Applied:
    • Data Collection and Pre-processing
    • Exploratory Data Analysis
    • Predictive Analytics
    • Data Visualization

Capstone Project 2: Sales Forecasting Model (3 hours)

  • Objective:
    • Develop a sales forecasting model for a retail business.
    • Use historical sales data to predict future sales.
    • Implement machine learning algorithms to improve forecast accuracy.
  • Skills Applied:
    • Statistical Analysis
    • Time Series Analysis
    • Predictive Analytics
    • Data Visualization and Reporting