Yes — data science is a good career in India for people who enjoy statistics, coding, and business problem-solving. Demand spans IT services, e-commerce, banking, fintech, retail, and healthcare, and experienced professionals earn significantly more than average IT roles. Competition is highest at the entry level, so freshers who build strong SQL, Python, and machine learning fundamentals along with real projects stand out quickly.

Whether data science is worth it depends on how you make the switch. A structured transition — learning SQL, Python, statistics and ML, then building 2-3 end-to-end projects — usually leads to a meaningful role and salary upgrade, especially for people coming from IT support, testing, or general analytics. Doing only a certificate course without hands-on projects rarely converts into interview calls, so the return depends entirely on depth of preparation.

Focus on four things: SQL and Python fluency (most filtering happens here), core statistics and ML concepts explained with intuition, deep command over 2-3 of your own projects, and clear structured communication. Interviews in India also commonly include guesstimates and case questions, so practise those separately. Finally, do a few mock interviews — most candidates know the theory but lose offers to unstructured answers.

A typical data science interview in India has 4-5 rounds: an HR screening, one or two technical rounds on SQL, Python, statistics and machine learning, a deep-dive into your past or academic projects, and sometimes a case study or business problem round. Product companies may add DSA-style coding, while analyst-heavy roles focus more on SQL and dashboards. The project deep-dive is where most candidates get rejected, so prepare it end-to-end.

Work with a 6-8 week plan: two weeks of SQL and pandas practice, two weeks of statistics and ML fundamentals (regression, classification, overfitting, evaluation metrics), then project deep-dive prep where you can justify every choice in your resume projects. Add guesstimates and basic case practice in the final stretch, and close with mock interviews. Consistency over six weeks beats last-minute cramming.

Freshers are usually asked to explain the bias-variance tradeoff, overfitting, precision vs recall, p-values, SQL joins and window functions, and to walk through their final-year or internship project in detail. Scenario questions like "how would you handle missing data" or "how would you reduce customer churn" are also common. Prepare 2-3 projects deeply, because interviewers keep probing until they find the limit of your understanding.

Avoid generic lines like "data science is the hottest job" — interviewers have heard them thousands of times. A strong answer follows a simple arc: a genuine trigger (a project, course, or real problem you enjoyed solving), the concrete steps you took to learn, and why the role matches your strengths in statistics, coding, or business thinking. A specific, honest story always beats a rehearsed perfect answer.

Yes, as a practice resource — it is one of the more useful data science interview books because of its large question banks on SQL, statistics, probability and ML, built from real interview questions at product companies. It should not be your only resource though: pair it with hands-on Python and SQL practice, 2-3 solid projects, and mock interviews. Indian interviews also lean heavily on case studies and guesstimates, which the book covers only lightly.

Start with end-to-end problems rather than complex architectures: house price prediction (regression), customer churn prediction (classification), customer segmentation (clustering) and sales forecasting (time series). Use real datasets from Kaggle or open government data portals, keep the code clean, do proper exploratory analysis, and write a short summary of the business impact — that is what separates a serious beginner project from a tutorial copy.

Follow a full pipeline: pick one specific business problem, collect and clean the data, explore it, build a simple baseline model, then iterate with feature engineering and the right evaluation metric. Finish with a small deployment (a Streamlit or Flask app), a documented GitHub repo, and a README that explains the problem, approach and results. Two or three end-to-end projects like this carry far more weight than ten unfinished notebooks.

The strongest machine learning projects for a resume are domain-relevant and end-to-end — for example demand forecasting for retail, churn or fraud prediction for banking, or sentiment analysis and text classification for NLP roles. Describe each with the business problem, your approach, the tools used, and a measurable outcome such as accuracy, MAPE or cost saved. Add GitHub and deployment links, since recruiters rarely trust projects they cannot open.

GitHub and Kaggle are the best places — both host thousands of machine learning projects with source code, along with curated repository lists for different skill levels. Treat them as references, not submissions: interviewers probe copied projects until they fall apart. A better approach is to take a reference project, change the dataset or add a new feature, and rebuild it end-to-end so you can defend every decision.

Start with the statistics — trend, seasonality, stationarity and autocorrelation — then learn classical models like ARIMA, SARIMA and exponential smoothing before moving to ML or deep learning approaches. Practice time series forecasting in Python using statsmodels, Prophet and scikit-learn on datasets such as retail sales or electricity demand, and always validate with rolling backtests instead of a single train-test split.

Strong options include retail demand forecasting, electricity load forecasting, website traffic prediction and inventory planning — all of which map to real business decisions. A good time series forecasting project compares at least two approaches (for example ARIMA vs an ML model), evaluates them with MAPE or RMSE using walk-forward validation, and ends with a clear recommendation on how the forecast would drive a business decision.

Expect questions on stationarity and how to test it (ADF/KPSS), choosing p, d and q, the difference between ARIMA, SARIMA and exponential smoothing, handling trend and seasonality, why MAPE can be misleading, and how LSTM-based models differ from classical ones. Interviewers often ask you to compare time series forecasting models for a given business scenario and explain how you would validate and monitor the forecast after deployment.