Here's the frustrating reality of data science entry level jobs: the median posting labeled "entry level" on LinkedIn still asks for 2+ years of experience and proficiency in Python, SQL, and machine learning. A 2024 Burning Glass analysis found that 62% of "entry level" data roles require prior experience. That's not a bug — it's the gap you need to close strategically.
The good news: data science entry level jobs are genuinely available, and companies are hiring candidates without degrees or traditional backgrounds — if those candidates can demonstrate they can do the work. This guide breaks down what these roles actually look like, what they pay, and the fastest path to getting one.
What Data Science Entry Level Jobs Actually Look Like
The term "data science entry level" covers a wider range of roles than most people expect. You're unlikely to walk into a full data scientist position at Google with zero experience, but there are several adjacent roles that function as real entry points into the field — and that pay well.
Data Analyst
The most common true entry point. Data analysts clean data, run SQL queries, build dashboards in Tableau or Power BI, and answer business questions. Median salary: $67,000–$85,000. Requires: SQL, Excel or Google Sheets, basic statistics, and ideally one visualization tool. Python is a plus, not always required.
Junior Data Scientist
Found mostly at mid-sized tech companies and startups. You'll run experiments, build simple predictive models, and work closely with a senior data scientist. Median salary: $85,000–$105,000. Requires: Python or R, pandas, scikit-learn, and at least one portfolio project that shows end-to-end modeling.
Business Intelligence Analyst
Heavy overlap with data analyst, but more focused on reporting and KPIs. Common in finance, retail, and SaaS. Median salary: $62,000–$80,000. Requires: SQL, strong Excel, and a BI tool. Less coding, more stakeholder communication.
Data Science Associate / Rotational Programs
Large companies like Amazon, Capital One, and Target run dedicated data science rotational programs for new grads. Competitive, but they explicitly recruit people without experience. Worth applying to even if you think you're underqualified.
Data Science Entry Level Job Salaries by City and Industry
Location and industry move the needle more than most people realize for data science entry level jobs.
By city (data analyst, entry level):
- San Francisco / Bay Area: $95,000–$115,000
- New York City: $82,000–$100,000
- Seattle: $80,000–$98,000
- Austin / Denver: $68,000–$85,000
- Remote (US): $70,000–$90,000
- Chicago / Atlanta: $65,000–$80,000
By industry (entry level, national median):
- Finance / Fintech: $88,000
- Healthcare / Pharma: $78,000
- Tech: $92,000
- Retail / E-commerce: $72,000
- Government / Nonprofit: $62,000
Finance and tech consistently pay the most for data science entry level jobs. If you're flexible on industry, both are worth targeting early — the skills transfer easily later.
What Skills Are Actually Required (vs. What's Just Noise)
Job postings for data science entry level jobs notoriously over-specify requirements. Here's what actually matters when you sit down in an interview:
Non-negotiable skills
- SQL: Every data role. You will write SQL on day one. Practice joins, window functions, aggregations, and CTEs. Sites like Mode Analytics SQL Tutorial and StrataScratch give you real practice problems.
- Python (basics): For analyst roles, knowing pandas for data manipulation is enough. For junior data scientist roles, add scikit-learn and matplotlib.
- Statistics fundamentals: Hypothesis testing, p-values, confidence intervals, and regression. You don't need graduate-level theory — you need to explain a t-test to a non-technical stakeholder without flinching.
- Data cleaning: 60–80% of a data scientist's actual job. Knowing how to handle nulls, outliers, and messy real-world datasets is underrated in course curricula but heavily screened in interviews.
Skills that help but aren't blockers
- Tableau or Power BI (useful, learnable in 2–3 weeks)
- Machine learning (required for junior data scientist, optional for analyst)
- Cloud platforms (AWS, GCP — increasingly asked for, but usually on-the-job training is fine)
- Excel (yes, still asked about in many interviews)
Skills you can safely deprioritize early
- Deep learning / neural networks (required for ML engineer roles, not entry level DS)
- Spark / big data tools (shows up in mid-level roles, rarely entry)
- A/B testing infrastructure (know the concept, not the engineering)
How to Build a Portfolio That Gets You Interviews
Hiring managers for data science entry level jobs see dozens of "I analyzed the Titanic dataset" projects. They're not impressed. A portfolio that stands out follows a simple principle: pick a dataset that has a real question behind it, and answer the question in a way that would matter to a business.
Three portfolio projects is enough. They should show:
- SQL + analysis: Query a real-world dataset (NYC taxi trips, Stack Overflow survey data, a public company's financials). Write a short summary of what you found and why it matters.
- End-to-end Python project: Load data, clean it, build a simple model or analysis, visualize results, write conclusions. Host it on GitHub with a clear README.
- Domain-specific project: If you're targeting finance, analyze stock data. Healthcare? CMS open data. Retail? Kaggle has dozens of transaction datasets. Domain relevance signals genuine interest and makes your portfolio memorable.
Put everything on GitHub. Write READMEs that non-technical people can skim. Link your GitHub in your resume header. Recruiters look.
Top Courses to Build Job-Ready Data Science Skills
These courses are specifically useful for landing data science entry level jobs — not just for learning theory.
Introduction to Data Analytics Course
A practical starting point that covers the full analytics workflow — from asking business questions to cleaning data to communicating results. Ideal if you're building toward an analyst role and want to understand how data work actually fits into organizations before going deeper on technical skills.
Executive Data Science Specialization
Unusually useful for entry level candidates: it teaches you how data science projects are scoped, managed, and communicated to non-technical stakeholders. Understanding this process early makes you sound much more experienced in interviews than candidates who only know the coding side.
Introduction to Data Analysis using Microsoft Excel
Don't sleep on Excel. A significant portion of analyst interviews still include Excel components, and many real analyst jobs live in spreadsheets. This course gets you to a professional standard quickly — and it's a fast confidence builder before moving to Python.
Database Design and Basic SQL in PostgreSQL
SQL is the single highest-ROI skill for data science entry level jobs. This course teaches you not just how to query but how databases are structured — which means you'll write better queries and debug them faster than candidates who learned SQL from cheat sheets.
Applied Plotting, Charting & Data Representation in Python
Visualization is underemphasized in most data science curricula but critical for getting hired — your portfolio projects need charts that clearly communicate findings. This course teaches matplotlib and data storytelling in Python, which directly feeds into portfolio quality.
COVID19 Data Analysis Using Python
A hands-on project-based course that walks through a real-world dataset with genuine complexity. Completing this gives you a portfolio project you can actually talk about in interviews — one that shows data cleaning, analysis, and visualization together.
The Job Search Strategy That Actually Works
Applying to data science entry level jobs on LinkedIn and Indeed cold is a low-percentage play. The candidates who get hired faster use a different approach:
Target smaller companies first. A 20-person startup with a data problem is much more likely to hire a self-taught analyst than Google. Early career roles at smaller companies also give you more scope and faster skill development. You can move to a larger company in 2–3 years with real experience.
Use your domain as an edge. If you've worked in healthcare, finance, education, or any specific industry, you have context that a pure CS grad doesn't. Apply to data roles in that industry first — you'll be more credible than a generalist candidate.
Network directly, not generically. Message data analysts and junior data scientists on LinkedIn with a specific question about their work — not "can you refer me?" One genuine conversation is worth 50 cold applications. Most people are willing to talk for 20 minutes if you're respectful of their time.
Take the analyst role, then grow. Many working data scientists started as data analysts. The analyst-to-scientist transition is well-worn and well-understood by hiring managers. Getting your foot in the door as an analyst is a legitimate strategy, not a compromise.
FAQ
What degree do I need for data science entry level jobs?
A formal degree helps but is not required by most employers. Many companies have removed degree requirements entirely. What matters is demonstrated ability: a portfolio of projects, proficiency in SQL and Python, and the ability to explain your work. Bootcamp graduates and self-taught candidates are regularly hired for analyst and junior data scientist roles.
How long does it take to be qualified for entry level data science jobs?
With focused effort, 6–12 months of learning is enough to be competitive for data analyst roles. Junior data scientist roles typically require more depth — 12–18 months — especially if you're building machine learning skills from scratch. The portfolio matters as much as the learning time.
Is data science a good career in 2026?
Yes. The Bureau of Labor Statistics projects 35% growth in data science roles through 2032, far above average. Salaries remain strong. The market has normalized from the 2021 peak, but demand for people who can actually work with data — clean it, analyze it, and communicate findings — remains consistent across industries.
What's the difference between a data analyst and a data scientist for entry level hiring?
Data analyst roles focus on querying data, building reports and dashboards, and answering defined business questions. Data scientist roles involve more modeling, statistical inference, and building predictive systems. Analyst roles are more accessible for entry level candidates and more abundant. Most companies have 3–5x more analyst openings than data scientist openings.
Do I need a master's degree to get a data science job?
Not for most roles. A master's in statistics, computer science, or data science can accelerate your career and open doors at research-heavy companies. But for industry data science and analytics roles — which represent the majority of job openings — a strong portfolio and relevant skills outperform a degree without them.
What programming language should I learn first for data science entry level jobs?
SQL first, always. It's required in nearly every data role and the fastest to learn at a functional level. Then Python — it's the dominant language for data science work and has the strongest ecosystem of libraries (pandas, scikit-learn, matplotlib). R is valuable in statistics-heavy fields like biostatistics and academia but less commonly required in industry entry level roles.
Bottom Line
Data science entry level jobs pay well, are genuinely available to people without traditional backgrounds, and provide a career trajectory that compounds over time. The friction is real — "entry level" often isn't — but it's navigable with the right approach.
Start with SQL and Python. Build three portfolio projects that answer real questions. Target data analyst roles at smaller companies in industries where you have existing context. Take the Introduction to Data Analytics course to get your bearings on how the work actually fits into organizations, then layer on technical depth with Database Design and Basic SQL in PostgreSQL.
The candidates who land data science entry level jobs aren't necessarily the ones who've studied the longest — they're the ones who can show their work, communicate clearly, and demonstrate they understand what the role actually requires on day one.