Predictive Analytics Using R Curriculum (30 Hours)

Module 1: Introduction to Predictive Analytics and R (2 hours)

  • Topics Covered:
    • Overview of Predictive Analytics
    • Importance of Predictive Analytics in Business
    • Introduction to R Programming
    • Setting Up R and RStudio

Module 2: Data Preparation and Cleaning (3 hours)

  • Topics Covered:
    • Importing Data in R
    • Data Cleaning Techniques
    • Handling Missing Values
    • Data Transformation and Feature Engineering

Module 3: Exploratory Data Analysis (3 hours)

  • Topics Covered:
    • Descriptive Statistics
    • Data Visualization with ggplot2
    • Identifying Patterns and Trends
    • Correlation Analysis

Module 4: Introduction to Predictive Modeling (3 hours)

  • Topics Covered:
    • Overview of Predictive Modeling Techniques
    • Splitting Data: Training and Test Sets
    • Evaluation Metrics for Predictive Models
    • Introduction to Linear Regression

Module 5: Regression Analysis (3 hours)

  • Topics Covered:
    • Simple Linear Regression
    • Multiple Linear Regression
    • Assumptions of Linear Regression
    • Model Diagnostics and Validation

Module 6: Classification Techniques (4 hours)

  • Topics Covered:
    • Logistic Regression
    • Decision Trees
    • Random Forests
    • Evaluation Metrics for Classification Models (ROC, AUC)

Module 7: Clustering and Segmentation (3 hours)

  • Topics Covered:
    • Introduction to Clustering
    • K-Means Clustering
    • Hierarchical Clustering
    • Evaluating Clustering Results

Module 8: Advanced Predictive Modeling Techniques (4 hours)

  • Topics Covered:
    • Support Vector Machines (SVM)
    • Gradient Boosting Machines (GBM)
    • Neural Networks
    • Hyperparameter Tuning

Module 9: Time Series Analysis and Forecasting (3 hours)

  • Topics Covered:
    • Introduction to Time Series Analysis
    • Decomposition of Time Series
    • ARIMA Models
    • Forecasting with R

Module 10: Model Deployment and Visualization (2 hours)

  • Topics Covered:
    • Introduction to Model Deployment
    • Shiny for Building Interactive Applications
    • Visualizing Predictive Models
    • Sharing Results with Stakeholders

Capstone Project 1: Sales Forecasting Model (3 hours)

  • Objective:
    • Develop a sales forecasting model using historical sales data.
    • Implement time series analysis and forecasting techniques.
    • Evaluate and validate the forecasting model.
  • Skills Applied:
    • Data Preparation and Cleaning
    • Time Series Analysis
    • Predictive Modeling
    • Model Validation

Capstone Project 2: Customer Churn Prediction (3 hours)

  • Objective:
    • Build a customer churn prediction model using customer data.
    • Apply classification techniques to predict churn.
    • Develop a strategy to reduce customer churn based on model insights.
  • Skills Applied:
    • Data Preparation and Cleaning
    • Classification Techniques
    • Model Evaluation and Validation
    • Predictive Analytics