Data Science vs Data Analytics: Which Career Should You Pick?

"Data scientist" and "data analyst" sound similar, but the daily work, salaries and required skills are very different. Pick the wrong one and you'll waste months on courses you don't need.

What a data analyst actually does

• Pulls data with SQL from a company warehouse
• Builds dashboards in Tableau, Power BI or Looker
• Answers business questions: "Why did sales drop in Germany last week?"
• Cleans messy spreadsheets in Excel or Python

Required math: basic statistics. You do not need calculus or linear algebra.

What a data scientist actually does

• Builds predictive models (forecasting, churn, recommendations)
• Trains and evaluates machine learning models in Python
• Runs A/B tests and explains results to product teams
• Sometimes ships ML models to production with engineers

Required math: statistics, linear algebra, calculus basics, probability.

Salaries (US, 2026 averages)

• Data analyst: $70k–$110k
• Data scientist: $120k–$190k
• ML engineer: $150k–$230k

In Europe and MENA, scale roughly down by 30–50%.

Time to first job

• Analyst: 4–8 months of focused study
• Scientist: 12–18 months
• ML engineer: 18+ months, usually after analyst or scientist experience

Free learning path — Analyst

1. SQL — SQLBolt + Mode SQL tutorial
2. Excel — Microsoft's free Excel training
3. Tableau Public — free desktop tool + free training videos
4. Statistics — Khan Academy "Statistics and Probability"
5. Portfolio: 3 dashboards on Tableau Public using real Kaggle datasets

Free learning path — Scientist

1. Python — see our free Python list
2. Math — 3Blue1Brown's "Essence of Linear Algebra" and "Calculus"
3. ML — Andrew Ng's Machine Learning Specialization (audit free on Coursera)
4. Deep Learning — fast.ai's Practical Deep Learning
5. Portfolio: 3 end-to-end notebooks on GitHub with clean READMEs

Both paths get a huge boost from Udemy coupon drops. Browse the Data category on CoursesPack for live deals.