Industry Guide

Data Science & Analytics Internships: Complete Guide 2025

Data roles are booming. Learn how to break into data science or analytics with the right skills, projects, and internship strategy.

Sproutern Career Team

December 27, 2025

18 min read

Data Careers in India 2025

₹15-50Kmonthly stipend range for data interns

30%YoY growth in data job openings

Pythonmost required skill

SQLsecond most required skill

Key Takeaways

Python + SQL + Excel are the foundational skills
Start with Data Analyst roles—easier entry point
Kaggle projects and competitions boost your profile
Visualization skills (Tableau/Power BI) are highly valued

1. Data Analyst vs Data Scientist

Data Analyst

• Analyze existing data for insights
• Create reports and dashboards
• SQL, Excel, Tableau/Power BI
• Easier entry point

Data Scientist

• Build predictive models
• Machine learning & statistics
• Python, ML libraries, math
• Requires more technical depth

Recommendation: Start with Data Analyst internships, then transition to Data Science as you build ML skills.

2. Required Skills

Must-Have

Python: Pandas, NumPy, Matplotlib, Seaborn
SQL: Joins, aggregations, window functions
Excel: Pivot tables, VLOOKUP, basic analysis
Statistics: Mean, median, distributions, hypothesis testing

For Data Science Roles

Machine learning (Scikit-learn)
Deep learning basics (TensorFlow/PyTorch)
Feature engineering
Linear algebra and calculus

3. Building Your Portfolio

Project Ideas

EDA Project: Analyze a public dataset (COVID, IPL, movies)
Dashboard: Build a Tableau/Power BI dashboard
ML Project: Predict house prices, churn, etc.
Kaggle: Compete and earn rankings

Where to Showcase

GitHub (Jupyter notebooks)
Kaggle profile
Personal portfolio website
LinkedIn posts about your projects

4. Top Companies

Product Companies

Flipkart, Swiggy, Zomato, Meesho, CRED, Razorpay, Ola, Uber

Analytics Firms

Mu Sigma, Fractal Analytics, Tiger Analytics, LatentView, Lemnisk

Tech Giants

Google, Microsoft, Amazon (more competitive)

5. Interview Process

SQL Test: Write queries on the spot
Case Study: Analyze a business problem
Technical: Stats, Python, ML concepts
Project Discussion: Walk through your portfolio

6. FAQs

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.

Data Roles: Deep Dive

Data Analyst

Analyze data to answer business questions. Create reports, dashboards, and insights that drive decisions.

Typical Stipend:₹15K-35K/month

Key Skills:SQL, Excel, Tableau/Power BI

Growth Path:Sr. Analyst → Lead → Manager

Business Analyst

Bridge between business and data teams. Translate business requirements into data solutions.

Typical Stipend:₹20K-40K/month

Key Skills:SQL, Communication, Domain

Growth Path:Sr. BA → Product Manager

Data Scientist

Build predictive models and machine learning solutions. More technical, requires programming and ML expertise.

Typical Stipend:₹30K-60K/month

Key Skills:Python, ML, Statistics

Growth Path:Sr. DS → Lead → Chief DS

ML Engineer

Deploy ML models to production. Focus on scalability, MLOps, and engineering best practices.

Typical Stipend:₹40K-80K/month

Key Skills:Python, MLOps, Cloud

Growth Path:Sr. MLE → Staff → Principal

Data Internship Stipends

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 Interview Questions

SQL is tested in almost every data interview. Here are common patterns:

Basic: Filter and Aggregate

"Find total sales by region for Q1 2025"

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

12-Week Learning Roadmap

1-2

Weeks 1-2: Python Basics

Variables, loops, functions, data structures. Practice on HackerRank or LeetCode (easy problems).

3-4

Weeks 3-4: Data Manipulation

Pandas, NumPy fundamentals. Work through tutorials with real datasets. Practice filtering, grouping, merging.

5-6

Weeks 5-6: SQL Mastery

Joins, aggregations, subqueries, window functions. Practice on SQLZoo, LeetCode, and StrataScratch.

7-8

Weeks 7-8: Visualization

Matplotlib, Seaborn, and Tableau or Power BI. Build your first interactive dashboard.

9-10

Weeks 9-10: Statistics & ML Basics

Probability, distributions, hypothesis testing. Intro to Scikit-learn: regression, classification.

11-12

Weeks 11-12: Portfolio Projects

Build 2-3 complete projects. EDA, ML model, and a business dashboard. Host on GitHub and Kaggle.

Success Stories

"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

Data Internship Readiness Checklist

Comfortable with Python (Pandas, NumPy)

Can write complex SQL queries

Understand basic statistics

Have 2+ portfolio projects on GitHub/Kaggle

Can create visualizations (Tableau/Power BI)

Know how to explain insights to non-technical people

Have practiced SQL interview questions

Resume highlights data projects with metrics

Best Learning Resources

Free Courses

• Google Data Analytics Certificate (Coursera)
• CS50's Introduction to Data Science (Harvard)
• Kaggle Courses (Python, Pandas, ML)
• freeCodeCamp Data Analysis with Python

Practice Platforms

• StrataScratch (SQL interview questions)
• Mode Analytics SQL Tutorial
• LeetCode Database (SQL)
• Kaggle (competitions and datasets)

Books

• Python for Data Analysis - Wes McKinney
• Naked Statistics - Charles Wheelan
• Hands-On Machine Learning - Aurélien Géron
• Storytelling with Data - Cole Nussbaumer

Common Interview Mistakes

❌

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.