IBM Data Science Professional Certificate
Beginners building Python, SQL, machine-learning and portfolio evidence
DATA CAREER GUIDE
Data Scientist
Turn data into models, forecasts, and business decisions.
BLS 2025 to 2035 · May 2025 pay · US BLS source ↗US OUTLOOK+35%
US MEDIAN PAY$120,230
10TH TO 90TH PERCENTILE$67,240 to $199,130+
YOUR ROUTE
From foundation to job evidence.
- 01FoundationPython · SQL · Statistics
- 02AnalysisCleaning · Visualization · Experimentation
- 03ModellingMachine learning · Evaluation
- 04Proof of skill2 to 3 independent portfolio projects
- 05Job readinessGitHub · Case studies · Interviews
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← Back to Data Scientist resultsWhat to check before applying
Collect a small sample of current vacancies for this role in your target US location. Separate required skills, preferred skills and experience requirements. This is an exercise you can perform, not a claim that we audited those vacancies for you.
- Choose a repeated task you can demonstrate instead of attempting every listed tool.
- Build an independent example with a README, source information and clear limitations.
- Explain one decision, an alternative you rejected and how you checked the result.
- Consider adjacent roles if vacancies consistently require experience you do not yet have.
For a mock interview, ask a reviewer to challenge your assumptions and explain your project without a prepared script. Improve the evidence as well as the presentation.
Do you want to explain a business result or build a prediction?
If you want to investigate sales, operations or customer behaviour, start with an analyst-style project. If you want to build predictions, first make sure you can evaluate a model against a simple baseline. A course with data science in its name is not automatically the right starting point.
An independent project brief
Use a public sales dataset to investigate why revenue changed. Check missing values and duplicate rows, define revenue consistently, write SQL queries and explain one business decision the analysis supports.
- A README explaining the business question, data source and limits.
- Reproducible SQL or a notebook with checks before calculations.
- A small chart set and a one-page recommendation, including an alternative explanation.
Self-review: Can another person reproduce your totals? Can you explain the difference between a finding in this dataset and a claim about the wider business?
This is a proposed practice exercise, not first-hand testing evidence or a hiring guarantee.
Build work you can show
Choose a practice brief, deliverables and a self-review checklist. These exercises complement a course; they are not proof of hiring readiness.
Data careersDo you want to explain a business result or build a prediction?CybersecurityCan you explain what happened, not just name a security tool?Project managementWhat have you already coordinated that you can explain clearly?Page updated September 17, 2026. Source-review dates elsewhere on this page are separate. Added practical decision checks and topic resources; no new checkout verification or first-hand course testing is implied.