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Statistical Methods and Data Analysis

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Subhasish Ray

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For statistical methods, the course focuses on understanding and application of different concepts and tools of statistics. You will be introduced to data visualization, which is an indispensable tool in this period of vast information around us. In this course, you will learn how to summarize data using descriptive statistics. Next, you will learn about sampling and how to make inferences about the population from a sample via probability theory. You will understand the difference between experimental and observational data. You will learn how to analyze experimental data using tests of significance. For observational data, you will learn correlation analysis and regression analysis. Finally, the course will help you to learn about regression with big data.

For programming skills, you will learn R as a programming language for statistical computing. R is a free software environment for statistical computing and has lots of support information available on the Internet. At the end of this course, you will have the confidence of executing your research project using R.

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What's inside

Syllabus

Data Visualization

Statistical methods, by definition, are tools for identifying patterns in large datasets. This module takes the first step towards statistical analysis by exploring various strategies for visualizing data, an increasingly important skill in today’s era of big data. This module explains the different forms of data, types of plots, and charts used to depict the different forms of data. In addition, the module focuses on different visualization techniques appropriate for big data.

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Bayesian Methods for Data Analysis, Third Edition

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This advanced textbook provides a unified framework for statistical modeling, covering topics such as hierarchical models, Bayesian inference, and Markov chain Monte Carlo methods. It is intended for graduate students and researchers in statistics and related fields.

Bayesian Methods for Data Analysis (Chapman &...

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Causal Inference in Statistics

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This comprehensive book provides a rigorous introduction to causal inference, covering topics such as graphical models, structural equation modeling, and counterfactuals. It is intended for advanced graduate students and researchers in statistics and related fields.

Causal Inference in Statistics - A Primer

Causal Inference in Statistics: A Primer

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Python Machine Learning

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This practical guide provides a comprehensive overview of machine learning concepts and algorithms, including supervised learning, unsupervised learning, and deep learning. It is written in a clear and approachable style, making it suitable for beginners and experienced practitioners alike.

Python Machine Learning

Python Machine Learning

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Bayesian Data Analysis, Third Edition

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This influential textbook provides a comprehensive introduction to Bayesian data analysis, covering concepts such as probability models, prior distributions, Markov chain Monte Carlo methods, and model checking. It is suitable for advanced undergraduate and graduate students.

Bayesian Data Analysis (Chapman & Hall/CRC Texts in...

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Data Mining: Concepts and Techniques

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This widely-used textbook covers data mining techniques such as clustering, classification, association rule mining, and text mining. It provides both theoretical foundations and practical applications, making it suitable for both researchers and practitioners.

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques

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Statistical Methods for Psychology

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This widely-used textbook covers statistical methods specifically tailored for psychology students, including descriptive statistics, hypothesis testing, ANOVA, and regression analysis. It provides clear explanations and practical examples.

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Statistical Methods for Psychology

Statistical Methods for Psychology (with CD-ROM)

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Introductory Statistics

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Provides a broad overview of statistical methods, covering fundamental concepts such as data collection, probability distributions, hypothesis testing, and regression analysis. It is suitable for beginners in statistics and those seeking a refresher.

Introductory Statistics

Introductory Statistics

Introductory Statistics, 10th Edition

Introductory Statistics

Introductory Statistics

Introductory Statistics: Using Technology

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This open-source textbook provides a gentle introduction to statistical concepts and methods using Python. It is suitable for beginners with no prior programming experience and covers topics such as data visualization, hypothesis testing, and regression analysis.

Think Stats: Exploratory Data Analysis

Think Stats 1st edition by Allen B. Downey (2011)...

Think Stats Paperback October 27, 2014

Unknown Binding

Think Stats: Exploratory Data Analysis

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All of Statistics

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This concise and engaging book provides a comprehensive overview of statistical inference, covering topics such as probability, sampling, estimation, and hypothesis testing. It is written in a conversational style, making it accessible to a wide audience.

All of Statistics: A Concise Course in Statistical...

All of Nonparametric Statistics (Springer Texts in...

All of statistics : A concise course in statistic...

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The Elements of Statistical Learning

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This widely-cited book focuses on modern statistical methods for predictive modeling, such as regression, classification, and clustering. It covers both theoretical foundations and practical applications, making it suitable for both researchers and practitioners.

The Elements of Statistical Learning: Data Mining,...

The Elements of Statistical Learning: Data Mining,...

The Elements of Statistical Learning: Data Mining,...

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Analytics in a Big Data World

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Provides a comprehensive overview of big data analytics, covering topics such as data management, data mining, and data visualization. It valuable resource for students and practitioners who want to gain a better understanding of big data analytics.

Analytics in a Big Data World: The Essential Guide...

By Bart Baesens Analytics in a Big Data World: The...

Analytics in a Big Data World: The Essential Guide...

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Data Science for Business

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Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.

Data Science for Business: What You Need to Know...

Data Science for Business: What You Need to Know...

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Understanding Machine Learning

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Provides a comprehensive overview of machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. Written by leading experts in the field, it valuable resource for students and practitioners who want to gain a deep understanding of machine learning.

Understanding Machine Learning: From Theory to...

Understanding Machine Learning: From Theory to...

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Storytelling with Data

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Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.

Storytelling with Data: A Data Visualization Guide...

Daphne Draws Data: A Storytelling with Data...

Storytelling with Data: Let's Practice!

storytelling with data podcast

(Deutsch) Storytelling mit Daten: Die Grundlagen der...

(Italiano) Data storytelling: generare valore dalla...

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An Introduction to Statistical Learning

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A widely-used textbook for undergraduate and graduate-level statistics and data science courses. It provides a comprehensive overview of statistical learning methods with practical applications in R. While it can be challenging, it solidifies understanding of key modeling and prediction techniques. This core textbook for those seeking a deeper understanding.

An Introduction to Statistical Learning

An Introduction to Statistical Learning

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Python for Data Analysis

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A hands-on guide to data analysis using Python, this book covers a wide range of topics, including data cleaning, transformation, visualization, and modeling. Written by the creator of Pandas, it practical resource for students and professionals in various fields.

Python for Data Analysis: Data Wrangling with...

Python for Data Analysis

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A classic text in the field of data mining, this book provides a comprehensive overview of techniques and algorithms used for extracting knowledge from large datasets. Written by leading experts in the field, it valuable resource for students and researchers.

Data Mining: Practical Machine Learning Tools and...

DATA MINING, 4TH EDITION

Data Mining: Practical Machine Learning Tools and...

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An excellent overview of Bayesian statistics, this book provides a comprehensive introduction to the theory and practice of Bayesian data analysis. The focus on practical applications and real-life examples makes it a great choice for students and practitioners alike.

Data Analysis: A Bayesian Tutorial

Data Analysis: A Bayesian Tutorial

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Fundamentals of Data Visualization

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Provides a guide to creating effective and aesthetically pleasing data visualizations. It delves into the principles behind good visualization design, helping readers make informed choices about how to represent their data. It valuable reference for anyone creating visualizations, from students to professionals.

Fundamentals of Data Visualization: A Primer on...

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The Elements of Statistical Learning

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A classic text in the field of statistical learning, this book covers a wide range of topics, including linear and nonlinear regression, classification, unsupervised learning, and model selection. It comprehensive resource for students and practitioners in various fields.

The Elements of Statistical Learning: Data Mining,...

The Elements of Statistical Learning: Data Mining,...

The Elements of Statistical Learning: Data Mining,...

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Effort

6 hours/week

Via

Coursera

Institution

O.P. Jindal Global University

Instructor

Subhasish Ray

Session

October 27, 2025 - December 29, 2025

Language

English

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