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Data Scientist

Data scientists use statistics, programming and machine learning to answer questions and make predictions from data. The work goes from getting and cleaning data with SQL and Python, through analysis and modelling, to explaining results to decision makers.

What the job involves

  • Frame business questions as problems data can answer.
  • Query, clean and explore datasets.
  • Build and evaluate statistical and machine learning models.
  • Communicate findings and their uncertainty clearly.

Core skills

  • Python
  • SQL
  • Statistics and probability
  • pandas and NumPy
  • Machine learning basics
  • Data visualization and communication

Your learning path

Work through the steps in order. Each one links to the lessons and practice to use.

  1. Step 1: Python programming

    Python is the main language of data science. Become comfortable with its data structures and functions.

  2. Step 2: SQL for getting data

    Most company data lives in databases. Learn to extract and aggregate it yourself.

  3. Step 3: Working with data files and sources

    Read CSV and JSON files, call APIs and connect to databases from Python.

  4. Step 4: Statistics and visualization

    Statistics tells you whether a pattern is real. Charts make it understandable.

  5. Step 5: Machine learning

    Learn how models are trained, evaluated and compared, and when a simple model is enough.

  6. Step 6: Prepare for interviews

    Expect SQL and Python coding, statistics questions, and a discussion of a past analysis or model.

Projects to build

Projects prove your skills and give you something to show.

  • Explore a public dataset and write a short report with three evidence-backed findings.
  • Predict a numeric value (such as house prices) with regression and explain the errors.
  • Design and analyse a simulated A/B test.

Interview preparation

This path is a suggested order. Requirements differ between companies, so also read job descriptions for the roles you are applying to.

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