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IIT/IIM Courses

Statistics Courses

    Learn core statistics concepts with real-world data examples

    Build skills in probability, inference, and hypothesis testing

    Work on practical datasets and analytical problem-solving

    Train on interpreting results for business and research use

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Learn From The Best

8

Instructors

10

Industry Experts

Slide 1 of 8
Debabrata Das, Instructor

Debabrata Das

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Director, IIITB

Dr. Debabrata Das is Director of IIITB. He has received his PhD from IIT-KGP. His main areas of research are IoT and Wireless Access Network 

IIITB Profile
Prof. Dhiya Al-Jumeily, Instructor

Prof. Dhiya Al-Jumeily

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Professor - Artificial Intelligence

A senior member of the IEEE and a chartered IT Professional, he is a fellow of the UK Higher Education ACademy 

LJMU Profile
Prof. G. Srinivasaraghavan, Instructor

Prof. G. Srinivasaraghavan

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Professor

Prof. Srinivasaraghavan has a PhD in Computer Science from IIT-K and 18 years of experience with Infosys and several other MNCs. 

IIITB Profile
Dr. Atif Waraich, Instructor

Dr. Atif Waraich

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Faculty - Computer Science

A Senior faculty of Engineering and Technology at LJMU who has multiple publications in the health care domain 

LJMU Profile
Chandrashekar Ramanathan, Instructor

Chandrashekar Ramanathan

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Dean Academics

Prof. Chandrashekar has a PhD from Mississippi State University and experience of over 10 years in several multinational organisations. 

IIITB Profile
Prof. Paulo Lisboa, Instructor

Prof. Paulo Lisboa

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Head of Dept - Applied Mathematics

Studied mathematical physics at LU and was the chairman of Industrial Mathematics at KMJU in 1996 and Head of Graduate School in 2002 

LJMU Profile
Dr. Gabriela Czanner, Instructor

Dr. Gabriela Czanner

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Faculty - Engineering and Technology

A Senior lecturer at Department of Applied Mathematics at LJMU. Her research focus is advanced statistics for decision support 

LJMU Profile
Tricha Anjali, Instructor

Tricha Anjali

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Ex-Associate Dean

Prof. Anjali has a PhD from Georgia Institute of Technology as well as an integrated MTech (EE) from IIT Bombay. 

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Learn by Doing

Our data science online certification programs have projects to apply theoretical knowledge in practical scenarios, helps you to tackle real-world problems.

16+

Industry projects to choose from

Slide 1 of 17

In this assignment, you will work for a consumer finance company which specialises in lending various types of loans to urban customers and use EDA to analyse the patterns present in the data. This will ensure that the applicants are capable of repaying the loan are not rejected.

Data Visualisation

Data Analysis

Data Interpretation

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Credit EDA Assignment

In this assignment, you will work for a consumer finance company which specialises in lending various types of loans to urban customers and use EDA to… Know More

Skills learned

Data Cleaning

Data Visualisation

Data Analysis

Data Interpretation

MySQL Queries

Data Manipulation

Data Analysis

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RSVP Case Study

In this assignment, you will work on a movies dataset using SQL to extract exciting insights about popular films and the factors that drive a film. 

Skills learned

MySQL

MySQL Queries

Data Manipulation

Data Analysis

ML Modelling

Model Evaluation

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Bike Sharing Assignment

Build a regression model to understand the factors on which the demand for bike sharing systems vary on and help a company optimise its revenue. 

Skills learned

Linear Regression

ML Modelling

Model Evaluation

In this case study, the company requires you to build a machine learning classification model wherein using some demographics and behavioral data of the leads (potential buyers), it will be able to identify the ones most likely to convert.

Decision Trees

Classification

ML Modelling

Model Evaluation

Business Problem Solving

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Lead Scoring Case Study

In this case study, the company requires you to build a machine learning classification model wherein using some demographics and behavioral data of t… Know More

Skills learned

Logistic Regression

Decision Trees

Classification

ML Modelling

Model Evaluation

Business Problem Solving

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customers that are likely to churn and make data-driven strategies to retain them from the perspective of a business analyst.

Tree Models

Model Selection

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

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Model Selection Case Study - Telecom Churn

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customer… Know More

Skills learned

Logistic Regression

Tree Models

Model Selection

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customers that are likely to churn and make data-driven strategies to retain them from the perspective of a data scientist.

Tree Models

Boosting

Model Selection

Regularization

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

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Advanced ML Case Study - Telecom Churn

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customer… Know More

Skills learned

Logistic Regression

Tree Models

Boosting

Model Selection

Regularization

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

Lexical Processing

Regex

POS Tagging

Dependency Parsing

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Syntactic Processing Assignment

Use the techniques such as POS tagging and Dependency parsing to extract information from unstructured text data. 

Skills learned

Natural Language Processing

Lexical Processing

Regex

POS Tagging

Dependency Parsing

Sqoop

Redshift

Spark

ETL Pipeline

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ETL Project

Make use of Sqoop, Redshift & Spark to design an ETL data pipeline. 

Skills learned

AWS

Sqoop

Redshift

Spark

ETL Pipeline

Analyse movie data from the past 100 years and find out various insights to determine what makes a movie do well. Use data manipulation, slicing, and various other dataframe operations to successfully find usable insights from the movies.

Data Processing

Data Visualisation

Data Analysis

Dashboarding

Data Storytelling

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IPL Visualization Assignment

Analyse movie data from the past 100 years and find out various insights to determine what makes a movie do well. Use data manipulation, slicing, and … Know More

Skills learned

Tableau/Power BI

Data Processing

Data Visualisation

Data Analysis

Dashboarding

Data Storytelling

Linear Regression

ML Model Evaluation

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Advanced Regression

Build a regularized regression model to understand the most important variables to predict 

Skills learned

ML Modeling

Linear Regression

ML Model Evaluation

Hadoop

MapReduce Programming

mrjob

Linux

Sqoop

Apache HBase

SQL

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MapReduce Programming Assignment

Perform MapReduce Programming in a big data environment on a large dataset. 

Skills learned

AWS

Hadoop

MapReduce Programming

mrjob

Linux

Sqoop

Apache HBase

SQL

Big Data with Spark

PySpark

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Big Data Case Study

Understand how a big data project in the industry is taken up and solved through a comprehensive big data case study on cloud. 

Skills learned

AWS

Big Data with Spark

PySpark

Python

Data Analysis

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SQL & Python Lab

Solve intensive programming questions in a competitive lab environment to showcase and evaluate your SQL and Python coding skills. 

Skills learned

SQL

Python

Data Analysis

Understand the factors that drive the Airbnb business and help the team leads tackle problems and bottlenecks through extensive data analysis on UI tools.

Data Storytelling techniques

Data Visualisation

Tableau

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Airbnb Case Study

Understand the factors that drive the Airbnb business and help the team leads tackle problems and bottlenecks through extensive data analysis on UI to… Know More

Skills learned

Data Analysis

Data Storytelling techniques

Data Visualisation

Tableau

Spark

Spark Streaming

Apache Kafka

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Retail Project

Build an end-to-end real-time data processing application using Spark Streaming and Kafka. 

Skills learned

AWS

Spark

Spark Streaming

Apache Kafka

Domain understanding

Data Analysis

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Business Case Study

Understand how data projects are taken up in the industry and implement the steps required to successfully manage and complete a data project. 

Skills learned

Business Problem Solving

Domain understanding

Data Analysis

In this assignment, you will build a complete neural network using Numpy. You will implement all the steps required to build a network - feedforward, loss computation, backpropagation, weight updates etc. You will use the MNIST dataset to train your model to classify handwritten digits between 0-9.

Neural Networks

PyTorch

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NN Assignment

In this assignment, you will build a complete neural network using Numpy. You will implement all the steps required to build a network - feedforward, … Know More

Skills learned

Deep Learning

Neural Networks

PyTorch

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What Our Learners Have To Say

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upGrad's student mentor played a very importany role in my learning journey

upGrad's student mentors played a crucial part in my learning journey. I followed them and they helped a lot in clarifying my queries related to my online data science course. They also gave me placement assistance as a part of my data science course, where they perfectly guide you on how to start a career as a Data Analyst.

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Abhinay Bandaru

Product Analyst, Head Infotech

3 Years of Experience

The upGrad's placement drive helped me to secure a job in MNC

My online data science course's content was so well-structured that it helped me grasp concepts that were entirely new to me. Thanks to upGrad's placement drive, I was able to secure a job at Kantar Analytics.

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Data Analyst, Kantar

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Additional benifits of upGrad helped a lot in terms of gaining practical exposure

upGrad's online data science courses are truly amazing. The mentorship through industry veterans, BaseCamps, and student mentors makes the programme extremely engaging. I Would definitely endorse the data science programme for its rich content and comprehensive approach to Data Science.

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Technology Project Manager & Commercial Strategy Associate, Victory Farms

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Being online, it was like a university experience

Learning with upGrad for my data science course was like going back to University for me. Their data science certification, career support and mentorship calls really helped me switch to a career in the field of Data Science.

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News Analyst, Thomson Reuters

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    Receive one-on-one feedback from our seasoned data science faculty on submissions and personalized feedback to improvement

    Our Data Science Syllabus is designed to provide you with ample of industry relevant knowledge with examples

Statistics Courses Overview

Statistics courses focus on helping learners understand, analyze, and interpret data using mathematical and analytical techniques. In 2026, statistics remains a foundational skill for careers in data analytics, research, finance, product management, and machine learning. Well-structured statistics programs emphasize reasoning, interpretation, and real-world decision-making rather than only formulas.

Learning statistics enables you to evaluate data objectively, identify patterns, measure uncertainty, and support conclusions with evidence. Courses typically progress from descriptive statistics to probability, inference, and applied statistical modeling used in business and technology contexts.

Eligibility Criteria for Statistics Courses

Most statistics online courses are designed to be accessible, though basic numerical comfort is helpful.

Educational background

  • Bachelor’s degree from a recognized institution (any discipline).
  • Suitable for learners from arts, commerce, science, and engineering backgrounds.
  • Prior exposure to mathematics is beneficial but not mandatory.
  • Undergraduate students, fresh graduates, and working professionals are eligible.

Basic skills

  • Comfort with numbers, charts, and tables.
  • Willingness to think logically and interpret results.
  • Basic understanding of averages and percentages.
  • Readiness to apply concepts through practice problems.

Who can apply

  • Students pursuing data, analytics, or research roles.
  • Working professionals involved in reporting or analysis.
  • Finance, marketing, and operations professionals.
  • Product managers and strategy teams.
  • Learners transitioning into data-driven careers.

Is a Statistics Course the Right Fit for You?

Statistics is well suited for individuals who enjoy reasoning with data and drawing evidence-based conclusions.

You should consider statistics courses if you:

  • Want to understand the meaning behind data trends.
  • Work with reports, experiments, or performance metrics.
  • Plan to move into analytics, research, or data science roles.
  • Prefer structured thinking and evidence-based reasoning.

Because it helps you:

  • Measure uncertainty and risk.
  • Interpret real-world data correctly.
  • Avoid misleading conclusions from numbers.
  • Support business or research decisions with confidence.

Statistics is especially valuable when decisions rely on reliability and accuracy rather than intuition.

Statistics Course Fees and Duration

Fees and duration vary based on level, applied focus, and project coverage.

Quick overview

Type of Statistics Course

Average Fees (INR)

Duration

Statistics Foundations / Bootcamps

50,000 – 1,00,000

3–6 months

Intermediate Applied Statistics Programs

1,20,000 – 1,90,000

6–12 months

Advanced or Data-Centric Statistics Programs

2,50,000 – 5,50,000

12–18 months

Short-Term Statistics Courses (4–8 weeks)

  • Focus on descriptive measures and probability basics.
  • Suitable for beginners or concept refreshers.

Intermediate Programs (3–6 months)

  • Include inferential methods and hypothesis testing.
  • Ideal for analytics and business roles.

Advanced or Long-Term Programs (6–12 months)

  • Cover modeling, regression, and applied case studies.
  • Suitable for data science and research-oriented careers.

What Skills You Gain from Statistics Courses

Statistics courses build analytical thinking and data interpretation skills.

1. Descriptive statistics

  • Measures of central tendency and spread.
  • Data visualization interpretations.
  • Understanding distributions and variability.
  • Summarizing datasets clearly.

2. Probability concepts

  • Basic probability rules.
  • Random variables and distributions.
  • Real-world uncertainty modeling.
  • Risk-based reasoning.

3. Statistical inference

  • Sampling techniques.
  • Confidence intervals.
  • Hypothesis testing logic.
  • Drawing conclusions from data samples.

4. Regression and relationships

  • Simple and multiple regression concepts.
  • Correlation vs causation.
  • Interpreting coefficients.
  • Predictive reasoning basics.

5. Experimental thinking

  • Designing valid experiments.
  • Bias and error identification.
  • A/B testing interpretation.
  • Evaluating outcomes objectively.

6. Practical application

  • Analyzing realistic datasets.
  • Interpreting business and research results.
  • Communicating insights clearly.
  • Avoiding statistical misinterpretation.

How to Pick the Right Statistics Course

A high-quality statistics course emphasizes understanding and application over memorization.

1. Concept-first approach

  • Focus on intuition behind formulas.
  • Clear explanations of why methods work.
  • Gradual increase in complexity.
  • Emphasis on interpretation.

2. Applied learning

  • Real datasets instead of theoretical-only problems.
  • Business or research-focused examples.
  • Practice interpreting outputs.
  • Assignments that require reasoning.

3. Tool integration

  • Use of spreadsheets, statistical software, or coding tools.
  • Interpreting outputs rather than manual calculation only.
  • Understanding assumptions behind tools.
  • Practical exposure to analysis workflows.

4. Mentorship and guidance

  • Support in understanding statistical logic.
  • Feedback on interpretation accuracy.
  • Clarification of common misconceptions.
  • Guided walkthroughs of problems.

5. Career alignment

  • Relevant use cases for target roles.
  • Projects that demonstrate analytical thinking.
  • Interview-oriented problem solving.
  • Focus on clarity and reasoning in explanations.

Quick comparison

Factor

Good Course

Weak Course

Understanding

Concept clarity

Formula memorization

Practice

Real examples

Pure theory

Guidance

Interpretation feedback

Minimal explanation

Outcome

Analytical confidence

Surface knowledge

Statistics Career Opportunities and Job Roles

Statistics knowledge supports roles where decisions depend on data accuracy and interpretation.

Popular Job Roles After Statistics Courses

Job Role

Average Pay (INR)

Product Analyst

7-15 LPA

Business Analyst

5-11 LPA

Data Analyst

4-9 LPA

Statistician

3-8 LPA

Research Analyst

3-7 LPA

Source- Glassdoor

Industries Hiring Statistics Professionals

  • Data and analytics firms
  • Finance and insurance
  • Healthcare and research organizations
  • E-commerce and consumer tech
  • Consulting and market research

Statistics Career Growth Path

Career Stage

Typical Roles

Entry-Level

Junior Analyst, Data Associate

Mid-Level

Data Analyst, Research Specialist

Senior-Level

Senior Analyst, Statistician

Leadership

Analytics Lead, Research Manager

Frequently Asked Questions

1. What are statistics courses designed to help learners achieve?

Statistics courses aim to build the ability to make sense of data rather than just calculate numbers. They train learners to question results, evaluate reliability, and draw accurate conclusions, which is essential when data is used to justify decisions in professional or academic settings.

2. What courses are under statistics?

Statistics includes courses such as descriptive statistics, probability theory, inferential statistics, business statistics, biostatistics, econometrics, applied statistics, statistical modeling, and experimental design. These courses vary in depth and application, serving learners across analytics, research, finance, healthcare, and product-based domains.

3. Do statistics courses focus more on theory or application?

Modern statistics courses emphasize application alongside theory. While concepts form the foundation, most programs focus on interpreting outputs, understanding assumptions, and applying methods to real-world datasets so learners can confidently justify insights rather than rely only on calculations.

4. Who should avoid taking a statistics course?

Statistics may not be suitable for learners expecting fast, tool-driven results without analytical thinking. It requires patience, curiosity, and logical reasoning. Those uninterested in problem-solving, interpretation, or data-driven reasoning may find the learning process disengaging over time.

5. How does studying statistics improve decision-making skills?

Statistics trains you to assess uncertainty, evaluate risk, and distinguish meaningful patterns from noise. This improves decision-making by replacing intuition with evidence, helping professionals justify choices, anticipate outcomes, and avoid common data misinterpretations in business or research environments.

6. Can statistics courses help with academic and research careers?

Yes. Statistics is essential for academic research, helping learners design sound experiments, analyze results correctly, and validate conclusions. Strong statistical skills improve research credibility, publication quality, and the ability to critically evaluate existing studies across disciplines.

7. Is learning statistics useful without learning programming?

Statistics can be learned without programming, especially at foundational and intermediate levels. Many concepts focus on reasoning and interpretation. However, combining statistics with tools or programming later enhances efficiency and career applicability, particularly in data-focused roles.

8. How do statistics courses support problem-solving ability?

Statistics strengthens structured thinking by teaching learners how to approach problems methodically—defining variables, forming hypotheses, testing assumptions, and interpreting results. This problem-solving framework transfers beyond data tasks into strategic thinking, analysis, and logical reasoning in everyday work.

9. What challenges do learners commonly face in statistics courses?

Learners often struggle with abstract reasoning, interpreting results correctly, or understanding assumptions behind methods. These challenges usually arise from rushing through concepts. Courses that prioritize intuition, visual understanding, and gradual complexity help overcome these difficulties effectively.

10. How important is practice when learning statistics?

Practice is critical in statistics because understanding comes from application. Repeated exposure to datasets, scenarios, and interpretations builds confidence. Without consistent practice, learners may remember formulas but struggle to apply reasoning accurately in unfamiliar or real-world situations.

11. When is the right time to enroll in a statistics course?

The right time is when your role or goals require evidence-based reasoning, such as analytics, research, finance, or strategy work. Learning statistics early in your career also builds long-term analytical confidence and supports smoother transitions into data-driven roles later.

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Disclaimer

  1. The above statistics depend on various factors and individual results may vary. Past performance is no guarantee of future results.

  2. The student assumes full responsibility for all expenses associated with visas, travel, & related costs. upGrad does not .