Data Science and Machine Learning With Python

Overview

Data science in India—and globally—rewards people who can move from a messy spreadsheet to a defensible insight, then to a model someone trusts enough to act on. This comprehensive Hindi-language program is designed as one continuous road: you are not jumping between disconnected YouTube playlists wondering what to learn after Pandas.\n\nWe start where many Hindi-speaking learners ask to start: Python itself, explained clearly without assuming a computer science degree. From there the stack unfolds in the order professionals actually use it—NumPy for fast numeric work, Pandas for real tables, Matplotlib and Seaborn for charts that survive a client meeting, statistics for knowing when a trend is signal versus noise, and scikit-learn for building and evaluating models that generalize beyond one dataset.\n\nInstruction stays in Hindi so abstract terms land faster; you spend cognitive energy on concepts, not on translating every sentence. Projects mirror workplace deliverables: cleaned datasets, exploratory notebooks, visual summaries, and a final modeling exercise with metrics you can explain to a non-technical stakeholder. That is the difference between knowing library syntax and being hireable.

What you will learn

• Python fundamentals for data work: variables, control flow, functions, files, and virtual environments
• NumPy arrays, broadcasting, and vectorized operations that replace slow loops
• Pandas for importing, cleaning, merging, grouping, and exporting tabular data
• Matplotlib and Seaborn for charts that communicate findings clearly
• Statistics and probability foundations: distributions, correlation, and hypothesis thinking
• Scikit-learn workflows: splits, pipelines, model choice, metrics, and basic tuning
• End-to-end mini projects from raw CSV to presentation-ready output
• Professional habits: reproducible notebooks, naming conventions, and sanity checks on data

Syllabus outline

1. Module 1: Python for Data — setup, syntax, and scripting patterns analysts use daily
2. Module 2: NumPy Deep Dive — arrays, shapes, broadcasting, and linear algebra essentials
3. Module 3: Pandas Mastery — DataFrames, cleaning, joins, and group-by analytics
4. Module 4: Visualization — Matplotlib, Seaborn, and choosing honest chart types
5. Module 5: Statistics for Decisions — descriptive stats, inference intuition, and pitfalls
6. Module 6: Machine Learning with scikit-learn — supervised models, evaluation, and tuning
7. Module 7: Integrated Capstone — one dataset carried from ingest through model report

Who this course is for

Hindi-speaking beginners who want one structured program from zero to machine learning, career switchers targeting analyst or junior data scientist roles, and working professionals who learn best in their native language after hours. Students in BCA, MCA, or statistics programs who need practical Python skills will also benefit.

Prerequisites

No programming background is required. Basic comfort with computers, installing software, and high-school level math helps. English is not required for following lectures.

How to study this course

Follow modules in order—the later ones assume earlier habits. After each Hindi lecture, rewrite the notebook in your own words with comments in whichever language helps you think. Dedicate one hour to video and one hour to hands-on practice the same day. Download datasets locally so you are not dependent on streaming. Each week, produce one small output: a chart, a summary table, or a model metric screenshot. Share it with a peer or mentor; explaining in Hindi or English solidifies retention better than passive rewatching.

Why this guide exists on the site

Udemy carries the full Hindi video series; this page lays out the entire data-science arc in one readable guide—what each block covers and how long to spend before jumping to ML. Learners often ask whether to start with Pandas or NumPy; the syllabus here removes that guesswork. Treat it as your roadmap while videos handle demonstration.

How enrollment works

Sharad Khare hosts structured video lessons on Udemy for convenient playback, progress tracking, and certificates. Use the button above to open the official course page. Pricing and promotions are set by Udemy. Pair lessons with free note packs on the Notes page and related reading in Articles.