Data roles are booming. Learn how to break into data science or analytics with the right skills, projects, and internship strategy.
Data Analyst
Data Scientist
Flipkart, Swiggy, Zomato, Meesho, CRED, Razorpay, Ola, Uber
Mu Sigma, Fractal Analytics, Tiger Analytics, LatentView, Lemnisk
Google, Microsoft, Amazon (more competitive)
Do I need a statistics/math background?
Helpful but not required. Many successful data professionals come from non-math backgrounds but learned statistics along the way.
Should I get a certification?
Projects matter more than certificates, but Google Data Analytics or IBM certifications can help for entry-level roles.
Analyze data to answer business questions. Create reports, dashboards, and insights that drive decisions.
Bridge between business and data teams. Translate business requirements into data solutions.
Build predictive models and machine learning solutions. More technical, requires programming and ML expertise.
Deploy ML models to production. Focus on scalability, MLOps, and engineering best practices.
| Company Type | Stipend Range | Duration |
|---|---|---|
| FAANG/Top Tech | ₹50K-1L+/month | 2-3 months |
| Funded Startups | ₹25K-50K/month | 3-6 months |
| Analytics Firms | ₹20K-40K/month | 2-6 months |
| Product Companies | ₹25K-45K/month | 3-6 months |
| Early Startups | ₹10K-25K/month | Variable |
SQL is tested in almost every data interview. Here are common patterns:
Basic: Filter and Aggregate
"Find total sales by region for Q1"
SELECT region, SUM(sales) FROM orders WHERE order_date BETWEEN '2025-01-01' AND '2025-03-31' GROUP BY region
Intermediate: Joins
"Find customers who placed orders but never received delivery"
SELECT c.name FROM customers c JOIN orders o ON c.id = o.customer_id LEFT JOIN deliveries d ON o.id = d.order_id WHERE d.id IS NULL
Advanced: Window Functions
"Find month-over-month growth in revenue"
SELECT month, revenue, LAG(revenue) OVER (ORDER BY month) as prev, (revenue - LAG(revenue) OVER (ORDER BY month)) / LAG(revenue) OVER (ORDER BY month) * 100 as growth_pct FROM monthly_sales
Weeks 1-2: Python Basics
Variables, loops, functions, data structures. Practice on HackerRank or LeetCode (easy problems).
Weeks 3-4: Data Manipulation
Pandas, NumPy fundamentals. Work through tutorials with real datasets. Practice filtering, grouping, merging.
Weeks 5-6: SQL Mastery
Joins, aggregations, subqueries, window functions. Practice on SQLZoo, LeetCode, and StrataScratch.
Weeks 7-8: Visualization
Matplotlib, Seaborn, and Tableau or Power BI. Build your first interactive dashboard.
Weeks 9-10: Statistics & ML Basics
Probability, distributions, hypothesis testing. Intro to Scikit-learn: regression, classification.
Weeks 11-12: Portfolio Projects
Build 2-3 complete projects. EDA, ML model, and a business dashboard. Host on GitHub and Kaggle.
Python. It's more versatile, has better ML libraries, and is more commonly used in industry. R is mainly for academia and specific statistical roles.
Not essential for most data analyst/scientist roles. Focus on classical ML first. Deep learning is needed mainly for specialized roles (NLP, computer vision).
Yes! SWEs often make great data engineers and ML engineers. Learn SQL, statistics, and ML concepts. Your coding skills are a major advantage.
Product companies: work on internal problems, see business impact. Analytics firms: exposure to multiple clients and industries. Both are valuable.
Write clean, well-documented notebooks. Enter competitions and aim for top 20%. Publish datasets and kernels. Engage with the community.
Entry-level: ₹6-12 LPA. After 3-5 years: ₹18-35 LPA. Senior data scientists at top companies: ₹50L+. ML engineers often earn even more.
"From mechanical engineering to data science..."
"I was a mechanical engineering student with zero coding background. Spent 6 months learning Python and SQL on my own. Built 5 Kaggle projects. Got a data analyst internship at Flipkart." — Arjun, IIT Roorkee
"Kaggle competitions opened doors..."
"I'm from a tier-3 college. But I was a Kaggle Expert with 3 competition medals. That got me interviews at companies that wouldn't normally look at my college. Now at Tiger Analytics." — Priya, Pune
"Started as analyst, now ML Engineer..."
"I started as a data analyst intern at a small startup. Learned ML on the job and gradually transitioned. 3 years later, I'm an ML Engineer at a Series C startup earning 3x my first salary." — Rohit, Bengaluru
Not explaining your thought process
Interviewers want to see how you think, not just the final answer. Verbalize your approach.
Forgetting to ask clarifying questions
Data problems have many interpretations. Ask about data quality, definitions, and edge cases.
Overcomplicating solutions
Start simple. A basic working solution is better than an incomplete complex one. Iterate from there.
Not connecting to business impact
Always tie your analysis back to business outcomes. "So what?" is the key question to answer.
Team Standup
Share progress on current analysis, discuss blockers, and align on priorities for the day.
Query Writing & Data Exploration
Write SQL queries, explore data, understand patterns. This is the core of daily work.
Stakeholder Meeting
Present findings to product/business teams. Learn to communicate insights to non-technical audiences.
Dashboard Building
Create or update dashboards in Tableau/Power BI. Visualize data to make it actionable.
Documentation & Learning
Document your work, learn new techniques. Great time to take online courses or read documentation.
Data careers offer great growth potential. Start with the fundamentals, build projects, and don't stress about knowing everything—learn as you go.
The field is vast, but every expert started as a beginner. Pick one skill, master it, then move to the next. Consistency beats intensity.
The best time to start was yesterday. The second best time is now. 📊
Written by Sproutern Career Team
Based on data industry insights and successful placements.
Regularly updated
