Predictive Analytics and Data Modelling

Course details

Predictive Analytics and Data Modelling course dives deep into statistical methods and data techniques to predict future trends and patterns. Through hands-on exercises and real-world case studies, students learn to harness the power of data for decision-making in business scenarios.

Start Date

TBD

Target Audience

3rd & 5th semester undergraduates with foundational knowledge in data analytics. Ideal for students aiming for careers in analytics, finance, and consulting.

Duration

14-15 weeks

Learning Objectives

Understand the principles of predictive analytics.
Gain proficiency in statistical and machine learning models for prediction.
Acquire hands-on experience with modeling tools and software.
Learn to interpret and communicate modeling results effectively.

Key Features

Lectures by internationally recognized professors.
Practical labs focusing on real-world applications.
Group projects with peers, simulating real-world business challenges.
Access to industry-standard predictive analytics tools via LMS.

Learning path

Unit 1: Introduction to Predictive Analytics (4)
Unit 2: Regression Analysis (6)
Unit 3: Time Series Analysis (4)
Unit 4: Classification Algorithms (6)
Unit 5: Model Evaluation and Optimization (4)
Unit 6: Advanced Predictive Models (4)

Topics covered

Overview of Predictive Analytics and Its Importance
Types of Predictive Models
The Predictive Analytics Process
Data Collection and Preparation

Linear Regression
Multiple Regression
Logistic Regression
Model Evaluation Techniques
Overfitting and Regularization
Non-linear Regression Models

Basics of Time Series Analysis
ARIMA Models
Exponential Smoothing Methods
Forecasting with Time Series Data

Decision Trees
Random Forests
Support Vector Machines
Neural Networks
k-Nearest Neighbors
Ensemble Methods

Confusion Matrix and Classification Reports
Cross-Validation Techniques
Grid Search and Random Search
ROC and AUC Analysis

Gradient Boosting Machines
Clustering for Predictive Modelling
Deep Learning Foundations
Special Topics and Recent Trends in Predictive Modelling

Grading:

Participation: 10%
Assignments: 30%
Projects: 30%
Final Exam: 30%

Prerequisites: "Business Intelligence and Visualization" and foundational knowledge in statistics.

Reading Material: Course material available on LMS. Additional readings may be suggested by the faculty.

Faculty: A distinguished professor with expertise in Predictive Analytics and Machine Learning.

Teaching Instructors: TBD