#1 India's Top IT Training Institute
New Launches Project Management PG Programs Counselling Session Placement Report Download Certificate

Career Guide · Data Science

How to Start a Career in Data Science from Scratch

A step-by-step roadmap to launch your data science career with no prior experience. Learn the skills, build projects, and land your first job.

Tracks
Career Roadmap · Live Interactive
Stage
—
What to focus on
Key Skills
—
What to learn
Goal
—
End result
Learn → Build → Showcase → Get Hired
Click a tab to explore each stage of starting a data science career.

Home / Tutorials / Career Guides / How to Start a Career in Data Science from Scratch

Career Guide · Data Science

How to Start a Career in Data Science from Scratch

AK Ankit Kaushik · Lead Trainer, Data Engineering & AI | Updated 28 August 2026 | 17 min read Free · Career Guide
LEARN BUILD SHOWCASE HIRED Fundamentals Python, SQL, Stats 1-2 months Foundation Portfolio Projects ML, Analytics, EDA 2-3 months Credibility Job Applications Targeted roles Networking Interviews Career Launched First DS role Growth path Success
A step-by-step roadmap to start your data science career from scratch.

Quick summary — how to start a data science career from scratch

Starting a data science career from zero is absolutely possible. In this guide, we give you a step-by-step roadmap — from learning the basics to landing your first job.

You will learn:

  1. The foundations — what to learn first (Python, SQL, statistics).
  2. Building skills — machine learning, data visualization, and tools.
  3. Creating a portfolio — projects that impress employers.
  4. Job search strategy — where to apply and how to stand out.
  5. Interview preparation — how to crack data science interviews.

SECTION 01The foundations: Python, SQL, and statistics

Every data science career starts with these three pillars:

  • Python: Learn pandas, numpy, matplotlib, and seaborn. Practice on LeetCode and HackerRank.
  • SQL: Master joins, subqueries, window functions, and aggregations. Practice on StrataScratch.
  • Statistics: Understand mean, median, standard deviation, probability, hypothesis testing, and regression.
Key insight: Spend 1-2 months building a strong foundation. This is the most important investment you'll make.

SECTION 02Building skills: ML, visualization, and tools

Once you have the foundations, move to these areas:

  • Machine Learning: Supervised and unsupervised learning, scikit-learn, model evaluation.
  • Data Visualization: Tableau, Power BI, or advanced matplotlib/seaborn.
  • Tools: Jupyter Notebooks, Git, and basic cloud (AWS/GCP/Azure).
  • Big Data: Spark basics (optional but recommended).
Pro tip: Focus on one tool at a time. Start with Python for ML, then add visualization tools later.

SECTION 03Creating a portfolio

Your portfolio is your proof of skills. Build projects that showcase your abilities:

  • EDA + Visualization: Analyze a dataset (e.g., Airbnb, COVID-19) and create a dashboard.
  • ML Project: Build a predictive model (e.g., house price prediction, customer churn).
  • End-to-end project: From data collection to deployment using Flask or Streamlit.
  • Kaggle competition: Participate and share your solution.
Key insight: 2-3 high-quality projects with clear README files are better than 10 average ones.

SECTION 04Job search strategy

Here's how to find and apply for data science roles:

  • Target roles: Data Analyst, Junior Data Scientist, Business Analyst, ML Engineer (entry-level).
  • Platforms: LinkedIn, Naukri, Indeed, AngelList (startups).
  • Networking: Connect with data professionals, attend meetups, and join Slack/Discord communities.
  • Tailor applications: Customize your resume and cover letter for each role.
Pro tip: Apply to 5-10 roles per week. Quality applications are more effective than mass applications.

SECTION 05Interview preparation

Prepare for these types of interview questions:

  • Coding: Python and SQL problems (LeetCode, HackerRank, StrataScratch).
  • Machine Learning: Model selection, evaluation metrics, overfitting, feature engineering.
  • Statistics: Probability, hypothesis testing, confidence intervals.
  • Behavioral: Tell me about a project, how you handled a challenge, teamwork.
  • Case study: How would you solve a business problem using data?
Key insight: Practice mock interviews with peers or platforms like Pramp. This is the fastest way to improve.

SECTION 06Interview Q&A

Q1How long does it take to become a data scientist?

With consistent effort (3-4 hours/day), you can be job-ready in 6-12 months.

Q2Do I need a degree in data science?

No — many data scientists are self-taught. A strong portfolio and skills matter more than a degree.

Q3What's the easiest way to get my first data science job?

Start with internships, freelance projects, or data analyst roles — they provide experience and lead to DS roles.

Q4Which projects are best for beginners?

EDA on a public dataset, a regression model, and a classification model are great starting projects.

Q5How important is networking?

Very important — 70% of jobs are found through networking. Start building your network early.

SECTION 07Test yourself — Data science career essentials

Five questions. No sign-up.

0 / 5

Pick an answer to see why it is right or wrong.

SECTION 08Frequently asked questions

Can I become a data scientist with no math background?

Yes — you need to learn statistics and linear algebra, but you don't need to be a math expert. Focus on practical applications.

What's the difference between data analyst and data scientist?

Data analysts focus on reporting and visualization. Data scientists build predictive models and solve complex problems.

How many projects do I need for my portfolio?

2-3 high-quality projects that demonstrate different skills are sufficient to start applying.

What's the salary for entry-level data science in India?

Entry-level data scientists typically earn ₹6-10 LPA, with top companies offering ₹12-15 LPA.

What I would do in the first 30 daysLayoff · 14 min Why sending 500 resumes won't workJob Search · 14 min Rebuild your resume for the AI job marketResume · 15 min Which skills to learn before applyingSkills · 14 min 90-day career recovery roadmapRecovery · 16 min How AI can help you prepare for interviewsAI · 11 min

Classroom & online · Noida

Start your data science career.

Our Data Science Training Course covers everything from Python to ML — with portfolio projects and interview preparation.

₹15,500 · full programme ₹24,000
Explore the course Download the syllabus
  • Master Python, SQL, and ML
  • Build portfolio projects
  • Mock interviews
  • Weekday & weekend batches