Build the core skills first — Python, SQL, statistics and basic machine learning — then create two or three portfolio projects on real, publicly available datasets and share them on GitHub. From there, apply for internships, data analyst roles or entry-level data science positions. Most data science careers for freshers actually begin in analyst roles, because they let you work with real business data while you keep learning.

A typical data science career roadmap starts with a data analyst or junior data scientist role, then moves to data scientist, senior data scientist, and finally lead or manager tracks such as ML engineering. In India, many people enter through analytics or business analyst roles and transition into data science within 1–2 years once their SQL and modelling skills mature.

The most common roles are data analyst, data scientist, data engineer, machine learning engineer, BI analyst and product analyst. Beyond tech companies, data science careers in healthcare, fintech, e-commerce and banking are expanding quickly, so you can specialise in a domain that genuinely interests you.

Yes, especially if you enjoy working with data and solving business problems. Demand still outpaces supply for professionals who combine SQL, Python and strong communication. Entry-level roles are competitive, so candidates with real projects and interview-ready SQL stand out — and salaries grow sharply once you have 2–3 years of hands-on experience.

Fresher packages typically range from around ₹4–10 LPA, with service-based companies at the lower end and product companies, well-funded startups and strongly prepared candidates at the higher end. The biggest jumps usually come with your second and third roles, so focus early on skills and proof-of-work rather than the first offer alone.

Begin with Excel and SQL, since most analytics work in India runs on them, then add a visualisation tool like Power BI or Tableau and basic statistics. Practise with real datasets, build a dashboard around an actual business problem such as sales or operations, and apply for analyst or MIS reporting roles. You don't need a heavy coding background — comfort with numbers and clear communication matter more.

Start with a browser-based SQL editor so you don't have to install anything, and learn in this order: SELECT and WHERE, then GROUP BY and aggregations, then JOINs, and finally window functions. Solve a few problems daily on realistic datasets and attempt every query without hints first. Consistency of 30–45 minutes a day beats long weekend sessions.

LeetCode's SQL section, HackerRank, SQLZoo and StrataScratch are the most widely used options, and all offer free tiers. Pick one that has interview-style questions sorted by difficulty, and stick with it until you have finished its medium-level problems instead of hopping between multiple sites.

LeetCode, StrataScratch and HackerRank host large banks of interview-style SQL practice questions, and most data analyst interview guides cover recurring patterns like joins, aggregations and window functions. Choose sets that include SQL practice exercises with solutions, so you can compare your query against an optimised answer and learn from the difference.

Because nearly every company's data sits in databases, and SQL is how you retrieve it. In India, SQL rounds appear in almost every data analyst and data scientist interview, and on the job you will use it daily to pull, clean and join data before any analysis or modelling happens. It is the single most tested skill for entry-level data roles.

Start with the fundamentals — activation, retention, funnels, cohorts and event tracking — then learn one tool such as Amplitude, Mixpanel or GA4 along with SQL for querying event data. Reinforce it with hands-on product analytics projects using sample event datasets, for example analysing a mock e-commerce app's funnel or churn. This mix of metrics thinking and tool practice is exactly what interviews test.

Product analytics tools track how users behave inside an app or website — events, funnels, retention and segmentation. The popular ones are Amplitude, Mixpanel, Google Analytics 4, Heap and Pendo. If you are starting out, GA4 is free and widely used, while Amplitude and Mixpanel are the tools most commonly discussed in product analyst interviews.

Not strictly — many people transition from data or business analyst roles by learning metrics, SQL and a tool on their own. That said, a recognised product analytics certification helps your resume get shortlisted when you lack direct experience. What matters most in interviews is showing you can define the right metrics and translate them into a product decision.

SQL is non-negotiable, along with Excel and at least one product analytics tool such as Amplitude, Mixpanel or GA4. Add basic statistics, A/B testing concepts, and the ability to convert data into clear product recommendations — hiring managers test this through case studies. Strong communication often decides the final offer, since product analysts present insights to non-technical stakeholders.