Complete Free Course · Hindi + English
Data Science in 60 Days – Job-Ready, Hindi + English
A complete self-study path from your first line of Python to a deployed machine-learning project. Every day has a real business scenario, runnable code with real outputs and charts, practice tasks with solutions, a quiz and interview questions – built on Indian datasets like e-commerce sales, Delhi air quality, house prices, loans and telecom churn.
- 1Week 1: Python
- 2Week 2–3: NumPy + pandas
- 3Week 4: Visualisation
- 4Week 5: Statistics
- 5Week 6: SQL
- 6Week 7–8: Machine learning
- 7Week 9: Career toolkit
- 8Days 56–60: Major project
🛒
Sales EDA (Day 19)Analyse 2,000 orders and write a management summary🎓
Student performance (Day 25)Visual report and evidence-based advice🗄️
SQL warehouse (Day 35)Star schema, CTEs and window functions📞
Churn prediction (Days 56–60)Model, ₹ impact, Streamlit app and APIWhat you will learn
- Python for data: variables, loops, functions, files
- NumPy arrays, broadcasting and simulation
- pandas: select, filter, groupby, pivot, merge
- Data cleaning, dates, text and feature creation
- Exploratory data analysis with a repeatable checklist
- Matplotlib, Seaborn and interactive Plotly charts
- Data storytelling and dashboards
- Descriptive statistics, probability and distributions
- Confidence intervals, hypothesis tests and A/B testing
- SQL: joins, GROUP BY, CTEs and window functions
- Regression, classification, trees, forests and boosting
- Metrics, pipelines, cross-validation and tuning
- Clustering, PCA, NLP and time-series forecasting
- Neural networks and deep-learning basics
- Deploy models with joblib, Flask and Streamlit
- Excel, Power BI, Git/GitHub, resume and interviews
- End-to-end churn project with business impact
🐍 Week 1 – Python for Data Science
7 Days
01
Day 1: What is Data Science? Jobs & Roadmap
02
Day 2: Setup: Python, Jupyter & Google Colab
03
Day 3: Python Basics: Variables, Types, Operators
04
Day 4: Conditions, Loops & Functions
05
Day 5: Lists, Tuples, Dictionaries & Sets
06
Day 6: Files, CSV, JSON, Errors & Modules
07
Day 7: Mini Project: Expense Analyzer
🔢 Week 2 – NumPy & Pandas
7 Days🧹 Week 3 – Data Cleaning & EDA
6 Days📊 Week 4 – Visualisation & Storytelling
5 Days📐 Week 5 – Statistics & Probability
6 Days🛢️ Week 6 – SQL for Data Science
4 Days🤖 Week 7 – Machine Learning Core
8 Days
36
Day 36: What is Machine Learning?
37
Day 37: scikit-learn & Train/Test Split
38
Day 38: Linear Regression: House Prices
39
Day 39: Multiple Regression, Ridge & Lasso
40
Day 40: Logistic Regression: Loan Approval
41
Day 41: Confusion Matrix, Precision, Recall, ROC
42
Day 42: KNN & Decision Trees
43
Day 43: Random Forest & Gradient Boosting
⚙️ Week 8 – Advanced ML
7 Days
44
Day 44: Feature Engineering & Scaling
45
Day 45: Pipelines, Cross-Validation & Tuning
46
Day 46: K-Means Clustering: Customer Segments
47
Day 47: PCA & Dimensionality Reduction
48
Day 48: NLP: Text Cleaning & Sentiment Model
49
Day 49: Time Series & Forecasting
50
Day 50: Save & Deploy Models (Streamlit, Flask)
🚀 Week 9 – Deep Learning, Deploy & Career
5 Days🏆 Major Project – Churn Prediction
5 DaysFrequently Asked Questions
Do I need any programming background?
No. Day 1 starts from zero and Days 2–7 teach all the Python you need. You only need a laptop or Google Colab in a browser.
Is this course really job-ready?
It covers the skills Indian data-analyst and junior data-scientist roles ask for – Python, pandas, SQL, statistics, ML, Excel and Power BI – plus four portfolio projects, a resume guide and 35+ interview questions with answers.
Are the outputs and charts real?
Yes. Every program was run with Python 3, pandas 2 and scikit-learn 1.x on the course datasets; the outputs and chart images on each page are the real results.
How much time does it take per day?
About 1.5 to 3 hours per day, including practice. You can go faster or slower – it is 100% self-paced.
Where do I get the datasets?
Log in and use the “Download Datasets + All Code” button. The kit contains every CSV and each day’s code as ready-to-run files.
