No experience? No problem. Learn how to land entry level data science jobs even if you’re just starting out. This step-by-step guide will show you how to build skills, create projects, and stand out—even without a tech background.
Why Entry Level Data Science Jobs Are Within Your Reach
Many people think you need years of experience to get into data science. That’s not true.
While top roles need experience, there are entry level data science jobs that welcome beginners. Companies often hire juniors or freshers who show potential—even without formal work experience.
All you need is the right mix of skills, projects, and persistence.
What Do Entry Level Data Science Jobs Involve?
If you land one of these roles, here’s what you might do:
- Clean and organize messy data
- Use tools like Python or SQL to find patterns
- Create charts or dashboards to show insights
- Help with machine learning models or reports
Even basic tasks like pulling data from a database or formatting spreadsheets are valuable starting points.
💡 Tip: Don’t underestimate small tasks—they help you learn the ropes.
Infographic: What Employers Look for in Beginners
how to Get Entry Level Data Science Jobs Without Experience
Let’s break the process down into easy, doable steps.
1. Start With the Basics (Python + SQL)
Don’t overthink it. Start with Python, the most common language in data science. Learn:
- Variables, loops, functions
- Data structures like lists and dictionaries
- Basic libraries like pandas, numpy, and matplotlib
Then move to SQL. It helps you pull and filter data from databases. Most data jobs need SQL, even if it’s just reading queries.
Example Tools to Use:
- Google Colab for Python practice
- W3Schools SQL for basic SQL queries
2. Build a Few Simple Projects
You won’t get hired with just online courses. You need to show your work.
Build small projects using free datasets from sites like Kaggle or Data.gov.
Good beginner projects:
- Analyze daily stock prices
- Visualize global COVID-19 cases
- Track top movies by IMDB rating
- Predict housing prices with regression
Explain your thought process. Add code comments. Post it on GitHub with a short writeup.
3. Create a Simple Portfolio
This is where you shine without experience.
Make a one-page portfolio showcasing:
- Your bio: “Aspiring Data Analyst learning Python & SQL”
- 2–3 projects: link to GitHub or Jupyter notebooks
- Skills: Python, SQL, Excel, matplotlib, etc.
You can even host it for free using GitHub Pages or use a simple site builder like Carrd.
4. Get on LinkedIn and Start Talking
If your resume is a quiet whisper, LinkedIn is your microphone.
Update your LinkedIn profile with:
- A headline like “Entry Level Data Science Enthusiast | Python | SQL”
- A short summary of your learning journey
- Your projects (link them!)
- Skills and courses you’ve taken
Then start posting once a week. Share what you’re learning, project updates, or even a Python tip.
✅ Real Example:
“Just finished my first project using pandas to analyze Airbnb listings in NYC! Learned a ton about data cleaning and visualization.”
5. Apply to Jobs That Don’t Demand Experience
Many roles say “1–2 years experience preferred.” Apply anyway.
Focus on:
- Internships or fresher roles
- Data Analyst or Junior Data Scientist titles
- Remote positions from startups
Use platforms like:
- LinkedIn Jobs
- AngelList
- Internshala (for India-based applicants)
- Indeed
Apply in batches. Track your progress in a spreadsheet.
6. Tailor Every Resume You Send
A generic resume won’t work. Make small changes for each job.
Highlight:
- Tools listed in the job description
- Similar projects (if the role is in finance, use a finance project)
- Your ability to learn quickly and work with data
Keep it to one page. Avoid long blocks of text.
Bonus Tip: Use action verbs like “analyzed,” “built,” “created,” “designed.”
7. Prep for Common Interview Questions
You don’t need to memorize formulas. Just understand the basics.
You might be asked:
- What’s the difference between supervised and unsupervised learning?
- How would you clean a messy dataset?
- What’s the use of groupby in pandas?
- Walk me through a project you’ve done.
Be clear, confident, and honest. If you don’t know something, say you’re learning it.
8. Stay Consistent and Keep Learning
Getting your first job takes time. It’s normal to get rejections.
Keep applying. Keep building. Keep posting.
If you do 1% better every week, you’ll be job-ready in 3–6 months.
Real Story: How Ravi Got His First Data Job Without a Degree
Ravi, 24, from Delhi:
“I studied civil engineering but got curious about data. I spent 4 months learning Python, did 3 projects, and started posting on LinkedIn. After 40 applications, I finally got hired by a local startup as a junior data analyst.”
FAQ: Entry Level Data Science Jobs With No Prior Experience
1. Can I really get a data science job without prior experience?
Yes, you absolutely can. Many companies hire freshers or career-switchers for entry-level roles. If you can show that you’ve learned the right tools (like Python and SQL) and built a few projects, you’re already ahead of most beginners.
2. What are the key skills I should focus on first?
Start with:
- Python for data analysis
- SQL to work with databases
- Pandas/NumPy for handling data
- Matplotlib/Seaborn for visuals
- Basic statistics to understand data patterns
Once you’re comfortable, move to beginner-level machine learning or visualization tools like Tableau.
3. Is a data science degree necessary?
No. A degree can help, but it’s not required. Many people land entry level data science jobs by learning through online courses, bootcamps, or self-study. What matters most is your ability to solve real problems using data.
4. How many projects should I have in my portfolio?
Aim for 2 to 4 solid projects. They should:
- Solve real-world problems
- Use relevant tools like Python, SQL, or Excel
- Include visualizations and insights
- Be well-documented (via GitHub or blog)
Even small projects count—quality matters more than quantity.
5. What are good places to find beginner-level jobs?
Some top platforms include:
- LinkedIn Jobs
- AngelList (now Wellfound) for startup roles
- Indeed and Glassdoor
- Kaggle Jobs Board
- Internshala (India-focused)
Set up alerts for terms like “entry-level data analyst”, “junior data scientist”, or “data science internship.”
6. How do I stand out if I don’t have a job history?
Use your portfolio, GitHub, and LinkedIn to showcase:
- What you’ve learned
- Projects you’ve done
- How you think and solve problems
Post regularly about your progress. It shows motivation and consistency—things hiring managers love.
7. Do certifications help?
They can help if they’re relevant and from trusted platforms like:
- Coursera (e.g., IBM Data Science)
- Google Data Analytics (on Coursera)
- edX or DataCamp
But don’t rely only on certificates. Projects and skills are more valuable.
8. What should I say in interviews if I’ve never had a data job?
Talk about your projects as if they were real job tasks.
Explain what problem you solved, how you did it, what tools you used, and what you learned. Show that you’re capable of doing similar work in a professional setting.
9. How long will it take to land a job if I start now?
It varies, but if you stay consistent, many people land jobs in 3 to 6 months. Focus on daily learning, building, and applying. The more active you are, the faster it’ll happen.
10. Can I get a remote entry level data science job?
Yes! Many startups and tech companies now hire remote analysts and data scientists, especially for junior roles. Search for “remote data analyst” or “remote data science internship” to find openings.
Roadmap: 90-Day Plan for Entry Level Data Science Jobs
Final Tips to Break In Without Experience
- Don’t wait to feel “ready”—start small
- Make your learning visible (GitHub + LinkedIn)
- Use rejection as feedback, not failure
- Join online communities like Reddit’s r/datascience or Kaggle forums
- Volunteer for NGOs or college projects to gain experience