Install Python and Jupyter Notebook. Learn syntax, variables, data types, loops, functions, and classes. Complete freeCodeCamp's Python course or CS50P (Harvard, free). Also, simultaneously start statistics — mean, median, variance, standard deviation, normal distribution, and basic probability. Use Khan Academy (completely free) for statistics. Furthermore, by end of Month 2, write your first Python scripts to load and explore a simple dataset from Kaggle.

Learn Pandas deeply — loading CSVs, cleaning missing data, filtering, groupby, merging datasets. Also, learn NumPy for array operations and numerical computing. Furthermore, start SQL on Mode Analytics, SQLZoo, or LeetCode (free). Practice SELECT, JOIN, GROUP BY, and window functions. Also, learn Matplotlib and Seaborn for data visualisation. By end of Month 4, complete your first mini-project: a full exploratory data analysis (EDA) of a real dataset — like the Titanic dataset on Kaggle — and post it on GitHub. Furthermore, take the Kaggle Python and Pandas micro-courses (both free) to solidify your skills.

This is the most important 2-month stretch. Learn supervised learning algorithms — linear regression, logistic regression, decision trees, random forests, and XGBoost. Also, understand model evaluation metrics and cross-validation thoroughly. Use Scikit-learn to implement models and understand how to tune hyperparameters using GridSearchCV. Furthermore, complete Andrew Ng's Machine Learning Specialisation on Coursera (available free to audit) — widely considered the best ML course in the world. Also, by end of Month 6, complete 2 Kaggle competitions using tabular data. Even a top 50% finish shows employers you can apply ML to real problems. Furthermore, publish all your work on GitHub with clean README files.

Now go deeper. Learn neural networks, backpropagation, and how deep learning works using TensorFlow or PyTorch. Also, pick one specialisation to go deep on — Natural Language Processing (NLP) for roles in fintech, e-commerce, and chatbots, or Computer Vision for roles in manufacturing, healthcare, and security. Furthermore, complete DeepLearning.AI's Deep Learning Specialisation (Andrew Ng — free to audit). Also, build 2 substantial projects — for example, a sentiment analysis tool using BERT, or an image classification system using CNNs. Furthermore, if you want to focus on the hottest skill in 2026, learn Generative AI — LangChain, RAG systems, and LLM fine-tuning. This single addition can push your starting salary from ₹8 LPA to ₹14–20 LPA at AI-focused companies.

Build 1–2 end-to-end capstone projects that demonstrate the full data science pipeline — data collection, cleaning, EDA, model building, evaluation, and deployment. Also, deploy at least one project as a web app using Streamlit or Flask on Heroku or AWS — this shows employers you can take a model beyond a Jupyter notebook. Furthermore, build a clean GitHub portfolio with 3–5 projects, each with a detailed README. Also, write 2–3 blog posts on Medium or LinkedIn documenting your learnings — this alone has helped many Indian freshers get interview calls from companies that found their content. Furthermore, apply to internships on Internshala, Naukri, and LinkedIn. Also, practice SQL interview questions on LeetCode and ML interview questions on Glassdoor. Your portfolio + LeetCode SQL performance determine 80% of your chances at a data science interview.