Statistical Foundation for Big Data Analysis Course | IIT Kharagpur
Course Details
| Exam Registration | 1253 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Computer Science and Engineering |
| Credit Points | 3 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 19 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Statistical Foundation for Big Data Analysis: A 12-Week Course Guide
In the era of information overload, the ability to extract meaningful insights from massive, complex datasets—Big Data—is a superpower. However, the sheer volume and dimensionality of such data render classical statistical tools insufficient. The course Statistical Foundation for Big Data Analysis, designed and taught by Prof. Arindam Banerjee of IIT Kharagpur, bridges this critical gap. This 12-week program provides a rigorous statistical framework tailored for the challenges and opportunities presented by high-dimensional data.
About the Instructor: Prof. Arindam Banerjee
Prof. Arindam Banerjee brings a wealth of academic excellence and research expertise to this course. Currently an Assistant Professor in the Department of Mathematics at IIT Kharagpur, his teaching portfolio includes Big Data Analysis, Statistics, and Engineering Mathematics. His research intersects advanced mathematics with practical applications, focusing on:
- Combinatorial and Homological Methods in Commutative Algebra and Algebraic Geometry.
- Application of Algebra, Combinatorics, and Statistical Machine Learning in Medical Bioinformatics.
With a Ph.D. from the University of Virginia and prior academic roles at Purdue University (USA) and Ramakrishna Mission Vivekananda Educational and Research Institute, Prof. Banerjee is uniquely positioned to demystify complex statistical concepts for real-world big data problems.
Who is This Course For?
This course is meticulously designed for:
- Senior Undergraduate & Postgraduate Students in Mathematics, Computer Science (CSE), Electronics (ECE), Artificial Intelligence, and Data Science.
- Professionals and enthusiasts looking to build a strong, theoretical statistical foundation for machine learning and data analysis roles.
Prerequisites: A basic exposure to linear algebra and probability is recommended to fully grasp the course material.
Course Overview & Industry Relevance
Big Data is characterized by its volume, velocity, and variety, often manifesting as very high-dimensional data. This course moves beyond mere data processing to focus on statistical inference and learning from such data. It seamlessly blends classical statistical methods with modern techniques essential for big data.
The curriculum is highly relevant for industries built on Data Science, Machine Learning, and Artificial Intelligence. Understanding the statistical underpinnings is crucial for developing robust models, avoiding pitfalls like overfitting, and making reliable predictions from complex datasets.
Detailed 12-Week Course Layout
| Week | Topics Covered |
|---|---|
| Week 1 | Introduction to Big Data, its challenges, and the role of high-dimensional statistics. Differentiating analysis from processing. |
| Week 2 | Review of Statistical Inference 1: Point Estimation methods (MLE, Moments) and asymptotics. |
| Week 3 | Review of Statistical Inference 2: Interval Estimation and basics of Hypothesis Testing. |
| Week 4 | Statistical Learning Theory 1: Introduction to Bias, Variance, and Mean Squared Error with case studies. |
| Week 5 | Statistical Learning Theory 2: Deep dive into the Bias-Variance Tradeoff, comparing estimation models. |
| Week 6 | Multivariate Linear Models: Regression, Multivariate Regression, and the Gauss-Markov Theorem. |
| Week 7 | Multivariate Probability Distributions: Focus on the Multivariate Normal Distribution, its properties and transformations. |
| Week 8 | Multivariate Analysis & Dimensionality: Unsupervised learning, clustering (K-means), and the "Curse of Dimensionality". |
| Week 9 | Population Principal Component Analysis (PCA): Concepts and applications for dimensionality reduction. |
| Week 10 | Sample Principal Component Analysis (PCA): Practical implementation and case studies. |
| Week 11 | Network Data Analysis: Introduction to network data, random graphs, and associated laws. |
| Week 12 | Application to Social Network Analysis: Using random graph models to analyze real-world social networks. |
Key Learning Outcomes
By the end of this course, participants will be able to:
- Understand the statistical challenges inherent in big, high-dimensional data.
- Apply classical inference techniques (estimation, testing) in modern contexts.
- Articulate and manage the fundamental bias-variance trade-off in predictive modeling.
- Implement and interpret multivariate techniques like regression and PCA.
- Comprehend the principles of network data analysis and its applications.
- Build a strong theoretical foundation for advanced machine learning algorithms.
Recommended Textbooks
To supplement the course lectures, Prof. Banerjee recommends three seminal texts:
- The Elements of Statistical Learning by Friedman, Tibshirani, and Hastie - A cornerstone for statistical learning theory.
- Applied Multivariate Statistical Analysis by Johnson, Wichern, and Johnson - For in-depth multivariate methods.
- Linear Algebra and Learning from Data by Gilbert Strang - Connects linear algebra fundamentals directly to data science applications.
This course is more than just a syllabus; it's a structured journey from the fundamentals of statistical inference to the forefront of big data analysis techniques. For anyone serious about a career in data science or AI, mastering the content of this course provides the indispensable statistical bedrock upon which all successful data-driven decisions are built.
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