Minor Course

## **Course Summary – Analytics Foundations (T2839)**

**Faculty:** Management | **Credits:** 4 | **Teaching Hours:** 30

### **Course Overview**

Analytics Foundations introduces students to the fundamental concepts of **Business Analytics**, enabling them to use data for informed business decision-making. The course emphasizes the application of **statistics, probability, predictive modeling, machine learning techniques, and time series analysis** in solving real-world business problems. It provides a strong conceptual foundation for advanced analytics and data-driven management.

### **Learning Objective**

The primary objective of this course is to help students **understand the application of statistics in real-life business environments** and develop the analytical mindset required to interpret data and support managerial decisions.

### **Course Modules**

#### **1. Basic Statistics and Probability Theory (10 Hours)**

Students learn descriptive statistics, probability distributions, sampling techniques, confidence intervals, and hypothesis testing. These concepts form the foundation of business analytics and decision-making.

#### **2. Linear Models (4 Hours)**

Introduction to predictive modeling through regression, classification, and non-parametric approaches. Students understand how analytical models predict outcomes using business data.

#### **3. General Linear Models (4 Hours)**

Covers Logistic Regression, Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). Students learn classification techniques used in customer segmentation, fraud detection, loan approval, disease prediction, and marketing analytics.

#### **4. Resampling Methods (2 Hours)**

Focuses on Cross-Validation and Bootstrap techniques for evaluating model performance and improving prediction accuracy.

#### **5. Model Selection and Regularization (2 Hours)**

Introduces feature selection methods along with Shrinkage, Ridge Regression, and Lasso Regression to build accurate and efficient predictive models while avoiding overfitting.

#### **6. Splines and Generalized Additive Models (GAMs) (2 Hours)**

Explores regression splines, basis functions, smoothing splines, and local regression for modeling complex non-linear relationships in business data.

#### **7. Principal Components Analysis (2 Hours)**

Students learn dimensionality reduction techniques, including Principal Component Regression (PCR) and Partial Least Squares (PLS), to simplify large datasets while retaining meaningful information.

#### **8. Tree-Based Methods (2 Hours)**

Introduces Decision Trees, Bagging, Random Forests, and Boosting. These machine learning techniques improve predictive accuracy and are widely used in business intelligence and forecasting.

#### **9. Time Series Models (2 Hours)**

Covers ARMA and ARIMA models for analyzing and forecasting time-dependent business data such as sales, stock prices, inventory, and demand forecasting.

### **Prerequisite**

Students should have a basic understanding of statistics and probability, as the course focuses on applying these concepts to Business Analytics.

### **Evaluation Pattern**

Student performance is assessed through:

* Class Tests
* Case Studies
* Laboratory Tests

### **Teaching Methodology**

The course is delivered through a combination of:

* Classroom Lectures
* Laboratory Sessions

### **Recommended References**

* *An Introduction to Statistical Learning* – James, Witten, Hastie & Tibshirani (Springer, 2013)
* *Applied Predictive Modeling* – Kuhn & Johnson (Springer, 2013)
* *Introductory Time Series with R* – Paul Cowpertwait & Andrew Metcalfe (Springer, 2009)

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## **Course Outcome**

After successfully completing this course, students will be able to:

* Apply statistical methods to solve business problems.
* Build and interpret regression and classification models.
* Evaluate predictive models using resampling techniques.
* Select appropriate machine learning models for business applications.
* Analyze complex datasets using dimensionality reduction techniques.
* Use tree-based methods for prediction and classification.
* Forecast business trends using time series models.
* Make data-driven business decisions with confidence.

This course equips BBA students with the analytical foundation required for careers in **Business Analytics, Marketing Analytics, Finance, Operations, Human Resources, Consulting, and Data-Driven Management**.