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BSMA1002 - Statistics for Data Science I
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BSMA1002 - Statistics for Data Science I
524 words
3 min read
| Field | Value |
|---|---|
| Course Code | BSMA1002 |
| Level | Foundational Level Course |
| Credits | 4 |
| Type | Foundational |
| Pre-requisites | None |
| Videos | YouTube Playlist |
📖 Description
The students will be introduced to large datasets. Using this data, the students will be introduced to various insights one can glean from the data. Basic concepts of probability also will be introduced during the course leading to a discussion on Random variables.
🗓️ Weekly Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction and type of data, Types of data, Descriptive and Inferential |
| statistics, Scales of measurement | |
| Week 2 | Describing categorical data |
| Frequency distribution of | |
| categorical data, Best practices for | |
| graphing categorical data, Mode and median for | |
| categorical | |
| Week 3 | Describing numerical data |
| Frequency tables for numerical data, Measures of central tendency - Mean, median and mode, Quartiles and percentiles, Measur | |
| Week 4 | Association between two variables - |
| Association between two categorical variables - Using relative frequencies in contingency tables, Association bet | |
| Week 5 | Basic principles of counting and factorial concepts - |
| Addition rule of counting, Multiplication rule of | |
| counting, Factorials | |
| Week 6 | Permutations and combinations |
| Week 7 | Probability |
| Basic definitions of | |
| probability, Events, Properties of probability | |
| Week 8 | Conditional probability - |
| Multiplication rule, Independence, Law of total probability, Bayes’ theorem | |
| Week 9 | Random Variables - |
| Random experiment, sample space and random variable, Discrete and continuous random variable, Probability mass function, Cumulativ | |
| Week 10 | Expectation and Variance - |
| Expectation of a discrete random variable, Variance and standard deviation of a discrete random variable | |
| Week 11 | Binomial and poisson random variables - |
| Bernoulli trials, Independent and identically distributed random variable, Binomial random variable, Expecta | |
| Week 12 | Introduction to continous random variables - |
| Area under the curve, Properties of pdf, Uniform distribution, Exponential distribution |
📎 Reference Documents
📚 Books & Resources
Reference Documents / Books
Descriptive Statistics (VOL 1) DOWNLOAD Probability and Probability Distributions (VOL 2) DOWNLOAD Prescribed Books The following are the suggested books for the course: Introductory Statistics (10th Edition) - ISBN 9780321989178, by Neil A. Weiss published by Pearson Introductory Statistics (4th Edition) - by Sheldon M. Ross
📝 About the Instructors
Usha Mohan
Professor,
Department of Management Studies,
IIT Madras
Usha Mohan holds a Ph.D. from Indian Statistical Institute. She has worked as a researcher in ISB Hyderabad and Lecturer at University of Hyderabad prior to joining IIT Madras. She offers courses in Data analytics, Operations research, and Supply chain management to under graduate, post graduate and doctoral students. In addition, she conducts training in Optimization methods and Data Analytics for industry professionals. Her research interests include developing quantitative models in operations management and combinatorial optimization.
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