Most "data science entry level jobs" postings list 2–3 years of experience as a requirement. That contradiction is actually useful information: it tells you which roles you're competing for, where the real on-ramps are, and why a targeted course can move the needle faster than a second degree.
This guide covers what entry-level data science roles actually look like in 2024 — job titles, realistic salaries, the skills that dominate job listings, and how to position yourself whether you're starting from scratch or switching fields.
What Entry Level Really Means for Data Science Jobs
The term gets used loosely. In practice, three distinct categories hide under the "entry level" label:
- True entry level — roles like data analyst or junior analyst that accept candidates with 0–1 years of professional experience. Most common at mid-size companies and startups.
- Experienced entry level — "junior data scientist" roles that expect a portfolio, internship experience, or a relevant master's degree. These are the postings asking for 2–3 years.
- Mislabeled roles — full data scientist positions at companies with poor calibration on their own job levels. Worth identifying and skipping early in a search.
For most career-changers and recent graduates, the realistic path runs through data analyst first, not data scientist. Analyst roles have lower barriers, faster hiring cycles, and teach you how data actually gets used inside a business — which makes you a better scientist later.
Common Entry-Level Data Science Job Titles to Target
Here are the titles worth searching for, roughly ordered by accessibility to someone new to the field:
Data Analyst
The most populated entry-level rung. Median salary in the US: $65,000–$80,000. Tools expected: SQL, Excel, at least one BI tool (Tableau, Power BI, Looker). Python is a plus but often not required. Most companies will hire analysts with a portfolio of three to five clean SQL projects and a demonstrated ability to communicate findings to non-technical stakeholders.
Business Intelligence Analyst
Overlaps heavily with data analyst but tends to sit closer to product or operations teams — more dashboard work, less modeling. Slightly higher floor on salary ($70,000–$90,000) at larger companies. SQL fluency and a BI tool are non-negotiable.
Junior Data Scientist
Typically requires some exposure to machine learning: scikit-learn, basic model training, evaluation metrics. Median salaries run $85,000–$110,000 at tech companies. More competitive, but accessible with a strong GitHub portfolio showing end-to-end ML projects, not just Jupyter notebooks from course exercises.
Data Engineer (Entry Level)
Often overlooked by people chasing "data scientist" titles. Entry-level data engineering roles pay $90,000–$120,000 and are in high demand. The focus is pipelines, SQL at scale, and cloud platforms (AWS, GCP, Azure). If you have a software background, this is frequently the faster path into data with a higher starting salary than analyst work.
Analytics Engineer
A relatively new title — popularized by dbt and the modern data stack — sitting between analyst and engineer. Writes transformation logic in SQL/dbt, maintains data models, works with analysts on data quality. Pay is comparable to junior data scientist roles and demand is rising fast.
What Hiring Managers Actually Look For
SQL Before Python
Nearly every data role — analyst, scientist, engineer — screens on SQL. Python gets you further in data science and engineering, but SQL is the baseline. If you can write window functions, CTEs, and subqueries without looking anything up, you pass the majority of technical screens. Many candidates who list Python on their CV can't write a clean GROUP BY under pressure.
A Portfolio Over Certificates
Certificates from major platforms are table stakes, not differentiators. What moves the needle is two to three projects on GitHub where you've taken a real dataset, asked a real question, and communicated the answer clearly. The analysis does not need to be impressive — the communication does. A hiring manager can't promote you to senior stakeholders if you can't explain what you found in plain language.
Domain Knowledge
A candidate who completed a generic data science course versus a candidate who completed the same course and applied it to healthcare, finance, or retail data in their portfolio will win the interview almost every time. Companies hire people who can work with their specific data, not abstract toy datasets.
Statistics Fundamentals
You don't need graduate-level probability theory for analyst roles. But you need to explain the difference between correlation and causation, know what a p-value means (and its limits), and understand basic distributions. Interviewers still catch candidates who treat machine learning as a black box with no statistical grounding.
How Courses Bridge the Gap to Entry-Level Data Science Jobs
Courses alone don't get you hired. What they do is compress the time between zero knowledge and portfolio-ready. A structured curriculum forces you through fundamentals in sequence — something self-teaching with YouTube videos rarely achieves — and the better ones include hands-on projects you can put on GitHub immediately.
For career-changers, the fastest path is usually: one foundational course covering Python or SQL basics → one applied course covering data analysis or ML → two to three portfolio projects using a domain you already know from your previous career.
The domain knowledge from your old job is an asset. An ex-nurse who learns Python and analyzes healthcare datasets is more hireable for a health-tech data analyst role than a fresh computer science grad with no clinical context. Lead with that.
Top Courses for Data Science Entry Level Jobs
Python for Data Science, AI & Development by IBM
IBM's Python course covers language fundamentals alongside pandas and NumPy in a way that maps directly to day-to-day analyst work. It's structured around applied exercises rather than theory, which makes it more useful for building portfolio pieces than courses that spend three modules on variable types before touching real data.
Introduction to Data Analytics
A well-sequenced intro that covers the full analyst workflow: data cleaning, exploratory analysis, and visualization. Good starting point if you're switching fields and want to understand what analysts actually do before committing to a longer specialization.
Analyze Data to Answer Questions
Part of Google's Data Analytics Certificate, this course focuses specifically on SQL and spreadsheet analysis for answering business questions — exactly the skill tested in analyst interviews. The framing around "answering questions" rather than "learning SQL" is intentional and produces more interview-ready candidates.
Process Data from Dirty to Clean
Data cleaning is the unglamorous 70% of the job that most courses skip over. This one addresses it directly. Being able to talk through how you handle missing values, outliers, and inconsistent formats in an interview shows real-world readiness that candidates who only studied clean toy datasets can't replicate.
Tools for Data Science
A practical survey of the tools working data scientists use: Jupyter, RStudio, Git, Watson Studio. Not deep on any single tool, but useful for understanding the ecosystem before you specialize. Pairs well with a Python or R fundamentals course to avoid getting lost in environment setup on day one of a new job.
Python Data Science (edX)
Goes deeper into statistical analysis and visualization than most intro options, making it a better fit for candidates targeting junior data scientist roles rather than pure analyst positions. The higher technical floor is worth it if ML roles are the goal from day one.
FAQ: Data Science Entry Level Jobs
How long does it realistically take to qualify for entry-level data science jobs?
For data analyst roles: most career-changers are job-ready in 6–12 months of focused study (15–20 hours per week) including portfolio building time. For junior data scientist roles: 12–18 months is more realistic, accounting for time to build ML projects and ideally complete one internship or freelance engagement. These timelines assume a quantitative background. A non-quantitative career-changer should add 3–6 months for statistics fundamentals.
Do I need a degree to get entry-level data science jobs?
For data analyst roles: no. A portfolio with two to three strong SQL or Python projects will clear the initial screen at the majority of mid-size and growth-stage companies. Larger enterprises — banks, insurance companies, Fortune 100 — still filter by degree more aggressively. For junior data scientist roles: a relevant degree (CS, statistics, math, engineering) or a master's is still expected at most companies. Bootcamp certificates with a strong portfolio are accepted at a minority of employers.
What salary should I expect at entry-level data science jobs?
US market estimates for 2024: data analyst ($60K–$85K), business intelligence analyst ($70K–$95K), junior data scientist ($85K–$115K), entry-level data engineer ($85K–$120K). Tech hubs (SF Bay Area, NYC, Seattle) run 20–30% above these figures. Remote roles have compressed the geographic premium somewhat, but top-of-range offers still cluster at companies in major metro markets.
What programming language should I learn first?
Python, by volume of job postings. R is still used in academic and biostatistics contexts, but Python is the default for analyst, scientist, and engineering roles across most industries. If you're targeting BI analyst roles specifically, SQL first is the faster path to employment — many BI positions never require Python at all.
Is a data science bootcamp worth it for entry-level jobs?
Depends on the bootcamp and your alternative. A well-structured bootcamp (12–24 weeks, project-heavy, career services with employer relationships) can accelerate your path if you lack the self-discipline to self-study. The credential itself doesn't carry much weight with hiring managers — it's the portfolio you build during the program that matters. A Coursera specialization plus self-built projects costs a fraction of a bootcamp and produces the same hiring signal, but requires more self-direction.
What's the practical difference between a data analyst and a data scientist at entry level?
Data analysts primarily work with existing data to answer defined business questions: dashboards, reporting, SQL queries, trend analysis. Data scientists build predictive models, run experiments (A/B tests), and work on less-defined problems. At entry level the distinction matters for where you apply, but the skills overlap significantly. Most working data scientists started as analysts. The reverse career path is unusual.
Bottom Line
"I want to be a data scientist" is too vague to execute on. "I want a data analyst role at a healthcare company within nine months, using my background in clinical operations" is actionable.
Start with SQL and Python fundamentals, pick a domain you already understand, build three projects that demonstrate you can take messy data and produce a clear answer to a real question, and target analyst roles as the first step rather than aiming directly at data scientist titles. The analyst role teaches you what the job actually involves — which is mostly data cleaning, stakeholder communication, and query optimization, not the Kaggle-competition machine learning most courses emphasize.
The courses above give you the technical baseline. The portfolio you build applying that knowledge to problems you actually understand is what gets you interviews.