Compare · Updated 16 September 2026
Data Analyst vs Data Scientist: which pays more and which is faster?
Same sourced data as the career pages, side by side.
Data Scientist pays more at mid-career: a median of $140,000 against $88,000 for Data Analyst, about 59% higher. Data Analyst is faster to enter: the quickest verified route takes about 6 months versus 12 for Data Scientist. Job growth favours Data Scientist (35% projected over ten years, BLS 2025-35, versus 12%).
| Data Analyst | Data Scientist | |
|---|---|---|
| Entry median | $68,000 | $110,000 |
| Mid-career median | $88,000 | $140,000 |
| Senior median | $115,000 | $180,000 |
| Top end | $154,571 | $199,130 |
| Roadmap hours | 630 | 890 |
| Fastest way in | Analytics bootcamp (6 mo) | Domain expert converting (12 mo) |
| Cheapest way in | $0 | $0 |
| Time to first job | 9–18 months | 18–36 months |
| Degree | No degree is legally required to work as a Data Analyst and a portfolio genuinely substitutes for one at small and mid-size employers, but most large-company postings still list a bachelor's degree as a preference. | No degree is legally required, but a quantitative master's or PhD is a hard filter in pharma, biostatistics, clinical research and quantitative finance; product analytics teams at technology companies and most mid-size employers are where demonstrated skill substitutes for the credential. |
| 10-year growth | 12% | 35% |
| Openings per year | 7,500 | 24,800 |
| Automation exposure | medium | medium |
| Key certification | Google Data Analytics Professional Certificate | IBM Data Science Professional Certificate |
| Tools | SQL (PostgreSQL, Snowflake, BigQuery, SQL Server), Excel / Google Sheets, Power BI, Tableau, Looker / Looker Studio | Python, SQL (Snowflake, BigQuery, Databricks, Redshift), Jupyter / VS Code, scikit-learn, XGBoost / LightGBM |
Salary figures checked September 2026 (Data Analyst) and September 2026 (Data Scientist). Sources are listed on each career page.
What a Data Analyst does
A Data Analyst is a person who turns a company's raw database into answers a manager can act on, using SQL, spreadsheets and a dashboard tool such as Power BI or Tableau.
A data analyst turns a messy company database into answers. The core loop is: a stakeholder asks something vague ("why did signups drop in July?"), you translate it into a query, pull the data with SQL, clean and check it, build a chart or a Power BI/Tableau dashboard, and write two paragraphs that say what happened and what to do. Most of the job is SQL and communication; the modelling is usually descriptive statistics, cohorts, funnels and the occasional A/B test readout. Titles vary a lot: business analyst, BI analyst, marketing analyst, product analyst, revenue operations analyst and healthcare data analyst are all the same skill set pointed at a different domain.
- Lowest barrier to entry of any six-figure-track data role: no degree requirement, no calculus, and a portfolio genuinely substitutes for credentials.
- Skills transfer across every industry, so you can follow the domain you find interesting or the one that pays.
- Mostly predictable hours - 40-45 a week outside month-end close and board-deck season.
What a Data Scientist does
A Data Scientist is a specialist who estimates what would happen rather than reporting what already did, by designing and reading out A/B tests, building forecasting and propensity models, and using causal inference when a clean experiment is impossible.
A data scientist sits between the analyst and the engineer. Where an analyst reports what happened, a data scientist estimates what would happen: designs and reads out A/B tests, builds forecasting and propensity models, does causal inference when a clean experiment is impossible, and builds the metric frameworks the rest of the company argues over. In practice the job splits into two archetypes. Product / decision science - heavy on SQL, experimentation, causal methods and stakeholder influence, common at consumer tech, marketplaces and fintech. Modelling / applied science - heavy on Python, scikit-learn, feature engineering and model evaluation, common in insurance, credit risk, healthcare, pricing and demand forecasting. Read the job description carefully, because the interview loops for the two are almost entirely different.
- BLS median of $120,230 with 35% projected growth to 2035 - one of the strongest wage-and-growth combinations in the US labour market.
- Genuine intellectual variety: the same week can involve causal inference, forecasting and a metric argument.
- Portable across industries; the statistics travels even when the domain does not.
How to choose between Data Analyst and Data Scientist
- Pick Data Analyst if internal transfer from a non-analyst job at your current employer produces more career-changer hires than cold applications do, because it converts domain knowledge you already have into the thing employers are actually short of; the certificate-plus-portfolio route works too, but it is the slowest of the five, at about 14 months and 150 to 300 applications.
- Pick Data Scientist if the route that produces the most career-changer hires is analyst first and Data Scientist second: 18 to 30 months of production experience substitutes for a graduate degree in a way no certificate does, and you are paid throughout. Going direct from zero means competing with master's graduates for the same junior slot.
The natural next moves are analytics engineer, which pays more for deeper SQL, dbt and pipeline ownership, and Data Scientist, where the US Bureau of Labor Statistics median is $120,230 against $88,940 for the operations research analyst code that BLS uses for analyst work, and where a quantitative master's degree is a common filter. The natural next moves are machine learning engineer and AI engineer, which have absorbed most of the production modelling work and pay more, plus analytics and data science management. Both engineering moves raise the software bar (testing, services, deployment) rather than the statistics bar, and neither requires a graduate degree.
Data Analyst vs Data Scientist FAQ
Which pays more, Data Analyst or Data Scientist?
At mid-career the median is $88,000 for a Data Analyst and $140,000 for a Data Scientist; at senior level $115,000 versus $180,000. Entry medians are $68,000 and $110,000. Figures are US base plus typical bonus where reported, checked September 2026.
Is it faster to become a Data Analyst or a Data Scientist?
The quickest verified route into Data Analyst is Analytics bootcamp at about 6 months; for Data Scientist it is Domain expert converting at about 12 months. Our full roadmaps run 630 and 890 study hours respectively.
Which is harder to automate, Data Analyst or Data Scientist?
We rate automation exposure medium for Data Analyst and medium for Data Scientist. Large language models now write competent SQL and first-draft charts, which compresses the ticket-taking half of Data Analyst work and is one reason Indeed's Hiring Lab counted entry-level postings down 7.5 percent year over year in May 2026. Owning metric definitions, experiments and the stakeholder relationship is what does not automate. Large language models already do exploratory analysis, boilerplate feature engineering and first-draft modelling code well, which erodes the junior end of Data Scientist work; the US Bureau of Labor Statistics cites the integration of AI into business workflows as a reason the occupation is projected to grow 35 percent to 2035. Experiment design, causal inference, spotting an artefact and persuading an organisation to act are not close to automated.
Do I need a certification for Data Analyst or Data Scientist?
No certification is required to work as a Data Analyst. The one with genuine screening value is Microsoft's PL-300 (Power BI Data Analyst Associate) at $165 for the exam in the United States and 40 to 90 hours of study, and it is worth having if you are targeting Microsoft-shop employers in corporate, healthcare, insurance or government. The Google Data Analytics Professional Certificate costs about $147 to $294 over three to six months and is worth taking as a curriculum, but it carries little weight as a credential on its own: there is no proctored exam and 3.8 million people have enrolled. No certification is required or expected for a Data Scientist job, and none of them substitutes for a degree or production experience. The most useful item on the list is the Machine Learning Specialization from Stanford Online and DeepLearning.AI at $49 a month for about two months and 95 hours, which is a curriculum rather than a credential. The Databricks Certified Machine Learning Associate at $200 is the only proctored exam here and it only matters if your target employers run Databricks; the DataCamp certifications carry little weight with hiring managers.