Data & AI
Statistics for Data Science in Hindi (IIT Madras BS Degree)
Sikhami InstituteHindi
Free
Join for free- 64 video lectures (30h 51m)
- Certificate of completion
- Ask the teacher your doubts
- Watch on the web and in the Android app
About this course
The Hindi edition of Statistics for Data Science I from IIT Madras's online BS degree programme, taught week by week from types of data to probability distributions. Who it's for: students starting data science who prefer to learn in Hindi. Taught in Hindi. These are the free, official videos of the "Statistics for Data Science - Hindi Course" playlist from the IIT Madras - B.S. Degree Programme YouTube channel (youtube.com/@iitmadrasbsdegreeprogramme). All videos belong to IIT Madras (BS Degree Programme); Sikhami is not affiliated with IIT Madras (BS Degree Programme) and simply arranges the public playlist into a course, so you can learn one lesson at a time and keep track of your progress.
What you will learn
• Types of data and scales of measurement • Describing categorical and numerical data • Association between two variables • Permutations and combinations • Probability, conditional probability and independent events • Random variables, expectation and variance • Binomial, uniform, exponential and normal distributions
Course content
1 section · 64 lectures · 30h 51m
1. Statistics for Data Science - Hindi Course
- W1_L1_Introduction and types of Data - Basic definitions21m
- W1_L2_Introduction and types of Data - Understanding data41m
- W1_L3_Introduction and types of Data - Classification of data21m
- W1_L4_Introduction and types of Data - Scales of measurement27m
- W2_L1_Describing Categorical Data - Frequency distributions32m
- W2_L2_Describing Categorical Data - Charts of categorical data37m
- W2_L3_Describing Categorical Data - Best practices while graphing data - Part 118m
- W2_L4_Describing Categorical Data - Best practices while graphing data - Part 219m
- W2_L5_Describing Categorical Data - Mode and Median33m
- W3_L1_Describing Numerical Data - Frequency Tables for numerical data39m
- W3_L2_Describing Numerical Data - Mean35m
- W3_L3_Describing Numerical Data - Median and Mode25m
- W3_L4_Describing Numerical Data - Measures of dispersion- Range, variance and standard deviation48m
- W3_L5_Describing Numerical Data - Percentiles, Quartiles, and Interquartile range28m
- W4_L1_Association between two variables - Review of course5m
- W4_L2_Association between two categorical variables - Introduction26m
- W4_L3_Association between two categorical variables - Relative frequencies41m
- W4_L4_Association between two numerical variables - Scatterplot16m
- W4_L5_Association between two numerical variables - Describing association12m
- W4_L6_Association between two numerical variables - Covariance38m
- W4_L7_Association between two numerical variables - Correlation34m
- W4_L8_Association between two numerical variables - Fitting a line16m
- W4_L9_Association between categorical and numerical variables23m
- W5_L1_Permutations and Combinations - Basic Principles of counting31m
- W5_L2_Permutations and Combinations - Factorials19m
- W5_L3_Permutations and Combinations - Permutations: Distinct objects1h 0m
- W5_L4_Permutations and Combinations - Permutations: Objects not distinct and Circular permutations43m
- W6_L1_Permutation and Combinations - Combinations42m
- W6_L2_Permutation and Combinations - Applications15m
- W7_L1_Probability - Basic Definitions32m
- W7_L2_Probability - Events38m
- W7_L3_Probability - Venn diagrams27m
- W7_L4_Probability - Properties of probability46m
- W7_L5_Probability - Applications18m
- W7_L6_Probability - Equally likely outcomes27m
- W8_L1_Conditional Probability - Contingency tables21m
- W8_L2_Conditional Probability - Conditional probability formula16m
- W8_L3_Conditional Probability - Multiplication rule24m
- W8_L4_Conditional Probability - Independent events10m
- W8_L5_Conditional Probability - Independent events: examples21m
- W8_L6_Conditional Probability - Independent events: properties23m
- W8_L7_Conditional Probability - Bayes' rule31m
- W9_L1_Random Variables - Introduction38m
- W9_L2_Random variables - Application14m
- W9_L3_Random variables - Discrete and continuous random variable16m
- W9_L4_Discrete random variables - Probability mass function properties28m
- W9_L5_Discrete random variables - Graph of probability mass function19m
- W9_L6_Discrete random variables - Cumulative distribution function14m
- W10_L1_Discrete random variable - Application25m
- W10_L2_Expectation of a random variable35m
- W10_L3_Expectation of a random variable - Properties of expectation37m
- W10_L4_Variance of a random variable36m
- W10_L5_Variance of a random variable - Properties of variance29m
- W10_L6_Standard deviation of a random variable26m
- W11_L1_Binomial distribution - Bernoulli distribution23m
- W11_L2:Binomial distribution - IID Bernoulli trials43m
- W11_L3_Binomial distribution - Distribution of Binomial random variable27m
- W12_L1_Continuous random variable - Uniform distribution24m
- W12_L2_Continuous random variable - Uniform distribution: applications21m
- W12_L7_Standardizing a normal random variable52m
- W12_L6_Standard Normal Distribution43m
- W12_L5_Normal Distribution47m
- W12_L3_Continuous random variable - Non uniform and triangular distribution21m
- W12_L4_Continuous random variable - Exponential distribution24m