Hindi Edition- Hands-on Statistics for Data Science

Name: Hindi Edition- Hands-on Statistics for Data Science
Rating: 5.0 (2 reviews)

Learn Statistics for Data Science & ML with Python in Hindi – From Basics to Advanced with Real-World Examples

Created byRPS Business Solutions

Last updated 3/2025

Hindi

What you'll learn

Master Core Statistics for Data Science – Learn descriptive and inferential statistics, probability distributions, and hypothesis testing
Hands-on Python for Statistical Analysis – Implement statistical concepts using Python libraries like NumPy, Pandas, and SciPy
Advanced Data Visualization & Insights – Use Matplotlib and Seaborn to explore and interpret data effectively
Statistical Thinking for Machine Learning – Understand feature selection, data preprocessing, and the role of statistics in ML models
Real-World Data Science Case Studies – Solve industry-based problems using statistical techniques and Python
Complete Statistics for Data Science in Hindi – Learn Descriptive & Inferential Statistics, Probability, and Hypothesis Testing

Course content

1 section • 70 lectures • 21h 25m total length

Introduction of Statistics20:11
Understanding Qualitative & Qualitative Variables13:14
Absolute & Relative Frequencies - Demo15:54
Central Tendency of Data12:57
Python Practical - Central Tendencies I21:22
Python Practical - Central Tendencies II18:43
Python Practical - Central Tendencies III20:51
Python Practical - Central Tendencies IV20:34
Python Practical - Central Tendencies V13:29
Understanding skewness in Data21:33
Python Practical - Skewness in Data22:49
Demo - Geometric Mean20:19
Demo - Harmonic Mean2:17
Understanding Quartiles20:00
Python Practical - Quartiles Part 120:14
Python Practical - Quartiles Part 218:20
Measures of Variation AKA Dispersion16:25
Understanding Range in details17:06
Understanding Inter Quartile Range in details15:20
Demo - Mean Absolute Deviation17:41
Demo - Mean Absolute Error Part 119:52
Demo - Mean Absolute Error Part 219:48
In-depth understanding of Standard Deviation17:14
Project - Standard Deviation14:50
Project - Central Tendencies Part 123:03
Project - Central Tendencies Part 216:43
Project - Central Tendencies Part 313:03
Introduction to Probabilities22:28
Conditional Probabilities21:21
Law of Multiplication & Addition6:20
Types of Events in Probabilities21:02
Marginal & Joint Probabilities13:42
Demo - Probability Distribution - Binomial Distribution19:24
Demo - Multi Experiment Binomial Distributions23:02
Demo - Poisson Distribution20:27
Project - Poisson Distribution25:09
Project - Poisson Distribution14:28
Demo - Multi Experiment Poisson Distribution18:32
Introduction to Normal Distribution22:59
Demo - Normal Distribution Probability Density Function19:55
Hands-on : Standard Normal Distribution16:11
Hands-on : Normal Distribution Part I20:14
Hands-on : Normal Distribution Part II18:24
Hands-on : Normal Distribution Part III17:48
Project - Normal Distribution25:15
Project - Normal Distribution26:07
Demo - Empirical Rule Part I15:14
Demo - Empirical Rule Part II19:58
Introduction to Hypothesis Testing22:15
Hypothesis Testing Continue15:04
Practical - Hypothesis Testing25:09
Practical - Hypothesis test Two Independent Samples20:09
Practical - Hypothesis test for Paired Samples11:25
Practical - Hypothesis Test of Proportion21:48
Introduction to Student Distribution25:13
Practical - T-test8:07
Practical : Two Independent Samples T-test19:47
Practical : Paired Samples T-test13:56
Practical : T & Z tests12:27
Introduction to Chi-square test goodness of fit22:00
Practical - Chi-Square test Goodness of fit23:41
Practical - Chi-Square Test of Independence12:49
Introduction to ANOVA (Analysis of Variance)18:00
Practical : Analysis of Variance (ANOVA)18:19
Python Implementation - P-value using SciPy package20:55
More Examples on One & Two Tailed Z test17:52
Python Implementation - One Tailed T test using SciPy Package21:01
Python Implementation - Goodness of Fit test20:00
Python Implementation - Hypothesis testing Part I15:18
Python Implementation - Hypothesis testing Part II18:01

Requirements

Basic Understanding of Mathematics – A fundamental knowledge of numbers, algebra, and basic calculations will be helpful
Familiarity with Python (Optional but Recommended) – Knowing Python basics like variables, loops, and functions will make it easier to implement statistical concepts.
No Prior Knowledge of Statistics Needed – This course covers everything from scratch, making it beginner-friendly
A Laptop/PC with Internet Access – You’ll need a computer to practice Python coding and follow along with hands-on exercises
Interest in Data Science & Machine Learning – If you’re curious about how statistics powers AI, ML, and Data Science, this course is perfect for you

Description

Master Statistics, Probability & Python for Data Science and Machine Learning – in Hindi!

Why Learn Statistics for Data Science?

Statistics is the foundation of Data Science, Machine Learning, and AI. Without a strong grasp of statistics, it’s impossible to make data-driven decisions, analyze datasets, or build accurate ML models.

This course is specially designed for Hindi-speaking learners who want to master Statistics for Data Science using Python. Whether you are an aspiring Data Scientist, Machine Learning Engineer, or Business Analyst, this course will help you build a strong foundation in statistics and apply it practically with Python.

What Will You Learn?

Descriptive & Inferential Statistics – Mean, Median, Mode, Variance, Standard Deviation, Skewness, Kurtosis
Probability & Distributions – Binomial, Normal, Poisson, and Uniform Distributions
Hypothesis Testing & Statistical Inference – p-value, t-tests, Chi-square tests, ANOVA
Python for Statistics – NumPy, Pandas, SciPy, Matplotlib, and Seaborn
Real-World Data Science Case Studies – Solve practical business problems with statistics
Statistics for Machine Learning – Feature selection, correlation, and data preprocessing

Who Should Take This Course?

Aspiring Data Scientists & Analysts – Build a solid foundation in statistics for Data Science & ML
Python Programmers & Developers – Learn how to apply statistics for data analysis & AI
Machine Learning & AI Enthusiasts – Understand how statistics powers ML models
MIS Executives & Business Analysts – Improve data-driven decision-making
Students & Career Switchers – Learn statistics from scratch in Hindi, with hands-on Python practice

Why Take This Course?

Course in Hindi – Best for Hindi-speaking learners who prefer learning in their native language
Hands-on Python Implementation – Learn practical coding with NumPy, Pandas, Matplotlib & SciPy
Real-World Case Studies & Projects – Apply statistics to real business & ML problems
Beginner-Friendly – No prior statistics knowledge required!
High-Quality Content – 25+ hours of step-by-step video lectures covering everything from basics to advanced

Prerequisites

Basic understanding of Mathematics (Algebra, Numbers)
Python Basics (Variables, Loops, Functions) – Not mandatory, but helpful
A Laptop/PC with Internet to follow along with coding exercises

Enroll Now & Start Your Data Science Journey!

Master Statistics & Probability with Python and become a Data Science Expert – in Hindi!

Who this course is for:

Aspiring Data Scientists & Analysts – If you want to build a strong foundation in statistics for data science, this course is for you
Machine Learning & AI Enthusiasts – Learn the statistical concepts behind ML algorithms and AI applications
Python Developers & Programmers – Improve your data analysis skills using Python libraries like NumPy, Pandas, and SciPy.
MIS Executives & Business Analysts – Master data-driven decision-making with real-world statistical applications
Students & Professionals Looking to Switch Careers – If you're from a non-technical background but want to enter Data Science or Analytics, this beginner-friendly course will help