Pre-MBA Statistics
Master essential statistical concepts and methods needed for MBA success with IIM Ahmedabad's comprehensive course.
Master essential statistical concepts and methods needed for MBA success with IIM Ahmedabad's comprehensive course.
This foundational course provides a thorough introduction to statistics for MBA aspirants. Students learn to analyze and interpret data through descriptive and inferential statistics, probability concepts, and sampling methods. The curriculum covers data types, probability theory, sampling techniques, estimation methods, and hypothesis testing. Through practical examples and hands-on exercises, learners develop the statistical reasoning skills necessary for business decision-making.
4.6
(241 ratings)
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Understand different types of data and their analysis
Master fundamental probability concepts
Learn various sampling techniques and their applications
Perform point and interval estimation
Conduct and interpret hypothesis tests
Apply statistical methods to business problems
Skills you'll gain
This course includes:
5.5 Hours PreRecorded video
14 assignments
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FullTime access
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There are 6 modules in this course
This comprehensive statistics course prepares students for MBA programs by covering fundamental statistical concepts and methods. The curriculum progresses from basic data types and descriptive statistics to advanced topics in probability, sampling, and statistical inference. Students learn through a combination of theoretical foundations and practical applications, with emphasis on business-relevant examples and real-world problem-solving.
Types of Data
Module 1 · 4 Hours to complete
Probability
Module 2 · 4 Hours to complete
Sampling
Module 3 · 4 Hours to complete
Point and Interval Estimation
Module 4 · 4 Hours to complete
Hypothesis Testing
Module 5 · 3 Hours to complete
Peer Review Assignment
Module 6 · 2 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
Professor
Sriram Sankaranarayanan is a professor of Computer Science at the University of Colorado Boulder, where he teaches a variety of courses on algorithms, theory of computation, mathematical optimization and programming languages. His research studies how computers can be used to verify and design other computer systems by combining ideas from mathematical logic, theory of computation and control theory. He uses these to analyze "safety-critical" systems ranging from autonomous vehicles to artificial pancreas devices for patients with type-1 diabetes. Sriram obtained a PhD in computer science from Stanford University. Subsequently he worked as a research staff member at NEC research labs in Princeton, NJ. He has been on the faculty at CU Boulder since 2009. Sriram has been the recipient of awards including the CAREER award from NSF, and the Provost's faculty achievement award at CU Boulder.
Distinguished Operations Research Scholar and Optimization Expert
Diptesh Ghosh serves as a Professor in the Production and Quantitative Methods Area at IIM Ahmedabad, where he has been contributing since December 2001. His academic journey includes a doctoral degree in Operations Research and Systems Analysis from IIM Calcutta, followed by significant roles including post-doctoral research at the University of Groningen's Faculty of Econometrics and Operations Research, and faculty position at IIM Lucknow. His practical experience includes working in the production department of a global automobile manufacturer, which adds real-world perspective to his academic expertise. His research focuses on optimization algorithms and heuristics for complex combinatorial optimization problems, particularly in network location and routing, with significant contributions to metaheuristic methods development. At IIM Ahmedabad, he teaches crucial courses in linear optimization, decision analysis, and network algorithms, while also serving as Dean (Programmes). His extensive publication record includes numerous research papers on topics ranging from genetic algorithms and facility layout problems to data correcting algorithms in combinatorial optimization, establishing him as a leading authority in operations research and quantitative methods
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4.6 course rating
241 ratings
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