The Bureau of Labor Statistics projects 36% growth in data science roles through 2031 — yet thousands of qualified candidates never get past the screening stage because they apply for the wrong jobs or undersell the right experience. If you're targeting data science entry level jobs, the gap between "job seeker" and "hired" is almost always a skills packaging problem, not a talent problem.
This guide breaks down exactly which entry-level roles exist, what each one actually requires, and the fastest path to a first offer — including which courses move the needle fastest.
What Data Science Entry Level Jobs Actually Exist
Most job seekers search "data science" and get overwhelmed by senior postings. Entry-level data science jobs tend to cluster under four titles:
Data Analyst
The most accessible on-ramp. Data analysts answer business questions using SQL, Excel, and visualization tools like Tableau or Power BI. No machine learning required. Median US salary: $67,000–$85,000. These roles dominate job boards because nearly every company with a database needs one.
Junior Data Scientist
Requires Python or R, some statistics, and usually a portfolio project or two. You'll typically work alongside senior data scientists rather than owning models end-to-end. Median US salary: $90,000–$115,000. Harder to break into, but the jump from analyst to junior data scientist often happens within 12–18 months.
Business Intelligence (BI) Analyst
Heavy SQL focus with dashboard-building as the primary output. More business-facing than data analyst roles. Great for people coming from a non-technical background who want a structured entry into the data world.
Data Engineer (Entry Level)
Pipeline-focused — moving, cleaning, and structuring data so analysts and scientists can use it. Requires stronger programming skills than analyst roles but pays accordingly: $95,000–$120,000 entry-level at many companies.
For most people, data analyst is the fastest path into data science entry level jobs. It has the lowest barrier, the most open positions, and is the natural predecessor to a data scientist title.
Skills That Actually Get You Hired at Entry Level
Job descriptions lie. They ask for 3 years of experience for "entry level" positions and list 15 tools. Here's what hiring managers actually care about:
SQL — Non-Negotiable
Every data interview will test SQL. Not advanced window functions on day one, but you need to write joins, aggregations, and subqueries confidently. Employers screen out candidates who can't do this in under 30 minutes.
Python or R — Pick One
Python is the industry standard for data science. R remains strong in academic and biostatistics settings. Learn Python first unless you have a specific reason otherwise. Focus on pandas, NumPy, and matplotlib before touching machine learning libraries.
Data Visualization
The ability to tell a story with data is underrated at entry level and over-valued by employers. If you can turn messy data into a clear chart with an obvious takeaway, you'll outperform candidates with better technical skills but worse communication.
Statistics Fundamentals
You don't need a PhD in statistics. You need to understand distributions, hypothesis testing, correlation vs. causation, and when to use which summary statistic. Interviewers test this regularly.
A Portfolio — Not Just Certificates
Certificates are table stakes. A GitHub with 2–3 projects that answer real questions beats a resume full of course completions. Pick datasets that interest you (sports, music, finance) and build projects you can explain passionately.
Top Courses for Data Science Entry Level Jobs
These aren't ranked by prestige — they're ranked by how directly they map to what employers test in entry-level data science interviews.
Introduction to Data Analytics Course
The cleanest starting point for complete beginners — covers data lifecycle, analysis thinking, and foundational tools without overwhelming technical depth. Directly mirrors what a data analyst does on day one.
Database Design and Basic SQL in PostgreSQL
SQL fluency is the single highest-ROI skill for landing data science entry level jobs, and this course builds it from scratch on PostgreSQL — the most common database in technical interviews.
Introduction to Data Analysis using Microsoft Excel
Don't dismiss Excel — 80% of business analyst and BI analyst job postings still list it, and Excel proficiency often determines who gets the offer at entry level when other candidates are equally matched on Python.
Applied Plotting, Charting & Data Representation in Python
Most Python courses teach you to analyze data but not to present it. This course fixes that gap — critical for interviews where you'll be asked to explain findings to non-technical stakeholders.
COVID19 Data Analysis Using Python
A real-world dataset project that walks through the full analysis workflow in Python. The output is a portfolio piece you can show in interviews — far more useful than theoretical coursework.
Executive Data Science Specialization Course
Counterintuitively useful for entry-level candidates: understanding how data science decisions get made at the leadership level helps you communicate your work more effectively and interview better for roles that require business context.
How to Get Experience When You Have None
The "need experience to get experience" trap is real but solvable. Here's how candidates actually break through:
Kaggle Competitions
Free datasets, community notebooks, and a leaderboard you can cite. You don't need to win — participating in 3–5 competitions gives you structured projects and exposes you to how other data scientists approach problems.
Volunteer Data Work
Nonprofits and local organizations often have data they've never analyzed. Offer 10 hours of analysis work in exchange for a line on your resume and a reference. DataKind and Catchafire both match volunteers with nonprofits needing data help.
Internal Transfers
If you're currently employed in any role, look for data-adjacent projects on your team. Analysts who built dashboards in marketing or ops often transition to formal data roles faster than external candidates because they have a reference and demonstrated impact.
Freelance on Upwork/Fiverr
Small businesses need data cleaned, analyzed, and visualized constantly. A $50 Fiverr project that you document well is a portfolio piece. Three of them and you have a work history section.
Salary Expectations for Data Science Entry Level Jobs
Salary varies significantly by role, location, and company size. Realistic ranges for US-based entry-level positions in 2026:
- Data Analyst: $58,000–$85,000
- BI Analyst: $65,000–$90,000
- Junior Data Scientist: $88,000–$120,000
- Entry Data Engineer: $90,000–$125,000
Remote roles skew these numbers upward — a remote data analyst position at a tech company often pays $80,000–$95,000 regardless of your location. Non-profit and government data roles typically pay 20–30% less than private sector equivalents.
Industries with the most entry-level openings: tech, finance, healthcare, e-commerce, and consulting. Industries with the fastest hiring cycles: startups and mid-size tech companies (3–4 week processes vs. 8–12 weeks at enterprise).
FAQ
Do I need a degree to get data science entry level jobs?
No — but it helps. A significant portion of entry-level data analysts and even junior data scientists are self-taught or come from bootcamps. What matters more: a portfolio with working code, SQL proficiency, and the ability to communicate analysis clearly. Many employers have removed degree requirements over the last three years, especially in tech.
How long does it take to get an entry-level data science job from scratch?
Most self-starters land their first data analyst role in 6–12 months of focused learning. Junior data scientist roles typically take 12–18 months from zero. The timeline shortens significantly if you have adjacent skills (finance, engineering, biology) that you can apply data skills to immediately.
Is Python or SQL more important for entry-level data jobs?
SQL. Every data role requires SQL; not every entry-level role requires Python. If you have limited time, get SQL to an intermediate level first. Most companies screen on SQL in the first technical interview and only test Python in later rounds.
What's the best entry-level data science job title to target?
Data Analyst is the best first target for most people. It has the most open positions, the clearest skill requirements, and is the natural progression path toward data scientist titles. Junior Data Scientist roles are fewer in number and tend to require project portfolios that take additional months to build.
Do certifications help when applying for data science entry level jobs?
They help when paired with portfolio work — they don't help much on their own. A certificate signals you completed structured learning; a portfolio project signals you can actually apply it. Employers who see both take you more seriously than candidates with certificates and no applied work.
Which companies hire the most entry-level data professionals?
Consulting firms (Deloitte, Accenture, PwC) hire large cohorts of entry-level analysts annually. Tech companies (Amazon, Google, Meta) hire fewer but pay more. For the highest odds of landing a first role, target mid-size companies with 200–2,000 employees — they hire constantly and have faster processes than enterprise.
Bottom Line
Data science entry level jobs are genuinely accessible — but only if you target the right role, build the right skills, and present real project work alongside certificates.
Start with SQL and data analysis fundamentals. Build two or three portfolio projects on real datasets. Apply first to data analyst and BI analyst roles, which have the most openings and the shortest hiring cycles. Then use that first role to develop Python and ML skills on the job.
The Introduction to Data Analytics Course is the cleanest starting point for most people, and the SQL in PostgreSQL course is the single highest-ROI investment for getting past the first technical screen. Both are online, self-paced, and directly aligned with what entry-level employers test.
The market for entry-level data professionals remains strong in 2026 — the candidates who struggle are those who over-index on learning and under-invest in building visible, shareable work. Start a project before you feel ready.