Python is the language of data science. Master the basics, then focus on NumPy, Pandas, and Matplotlib — the three libraries you'll use every single day.
By the end, you'll be able to
Mini-project
Analyze the IPL cricket dataset: batting averages, team performance trends, and player comparisons. Present findings in a Jupyter notebook with visualizations.
Data science without statistics is just guessing. Learn descriptive stats, probability distributions, hypothesis testing, confidence intervals, and A/B testing.
By the end, you'll be able to
Mini-project
Analyze an e-commerce A/B test dataset: determine if a new checkout flow increases conversion rate. Calculate statistical significance and present your recommendation.
Most data lives in databases. Master SQL: complex joins, window functions, CTEs, and subqueries. This is the most-tested skill in data science interviews.
By the end, you'll be able to
Mini-project
Solve 30 SQL problems on LeetCode/HackerRank. Then write 10 business queries on a sample e-commerce database.
Before any model, you explore. Learn how to clean messy data, handle missing values, detect outliers, engineer features, and tell stories with data.
By the end, you'll be able to
Mini-project
Do a full EDA on the Titanic or House Prices dataset from Kaggle. Document every step in a clean Jupyter notebook.
Learn the core ML algorithms: linear regression, logistic regression, decision trees, random forests, SVMs, and k-means. Understand bias-variance tradeoff and cross-validation.
By the end, you'll be able to
Mini-project
Build a loan default prediction model using a real bank dataset from Kaggle. Compare 5 models, tune hyperparameters, and write a report.
The difference between a good model and a great model is features. Learn encoding, scaling, feature creation, and how to systematically select the best model.
By the end, you'll be able to
Mini-project
Compete in a Kaggle competition. Focus on feature engineering to move up the leaderboard rather than trying exotic models.
Learn neural networks: perceptrons, backpropagation, CNNs for images, and RNNs/LSTMs for sequences. Use TensorFlow or PyTorch.
By the end, you'll be able to
Mini-project
Build an image classifier that detects whether food is Indian or Western cuisine. Train on a custom dataset, deploy as a simple web app.
The best data scientists are great communicators. Learn to build dashboards, write clear reports, and present findings to non-technical stakeholders.
By the end, you'll be able to
Mini-project
Create a dashboard analyzing Zomato restaurant data across Indian cities: ratings, cuisine trends, pricing. Present it to a friend as if they were a business stakeholder.
Build 2-3 complete data science projects: problem definition → data collection → EDA → modeling → deployment → presentation. These are your interview tickets.
By the end, you'll be able to
Mini-project
Build a movie recommendation system, deploy it as a Streamlit app, and write a Medium article explaining your approach.
Data science interviews test: Python/SQL coding, statistics, ML theory, case studies, and a take-home assignment. Prepare across all these areas.
By the end, you'll be able to
Mini-project
Do 3 mock interviews, solve 30 SQL + 30 Python problems, and practice explaining your projects in 3 minutes.
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