Learn data science.
A starting guide to statistics, programming and practical analysis. Begin with the question you want to answer.
Data work begins before choosing a model. You need a clear question, suitable data and a way to check what the result means.
A useful analysis starts by making uncertainty explicit . Define the question, examine the data and separate what you observed from what you inferred.
These topics can help you choose a first project or strengthen an area you already use.
01 · FOUNDATIONS
Topics to build on
Statistics, programming and knowledge of the subject work together. You can develop them through small analyses rather than waiting to learn every tool first.
1. Finding and preparing data
Learn to inspect tables and combine relevant records. SQL (Structured Query Language) is useful for querying many databases. Start with filters and joins, then add more complex queries as needed.
2. Statistics and probability
Explore variation, sampling, uncertainty and the difference between association and causation. Learn to check assumptions before drawing conclusions from a test or model.
3. Programming for analysis
Python libraries such as pandas, NumPy and scikit-learn are useful options. Practise handling missing values, checking data types and keeping transformations understandable.
4. Explaining a result
Show the question, method, evidence and limitations. A clear table, chart or short explanation can help another person judge the result and decide what to do next.
02 · PRACTICE
Go beyond a clean example
Prepared datasets make learning easier. Gradually introduce realistic complications so you can practise decisions about data quality and interpretation.
Inspect the data first
Look for missing values, duplicate records, inconsistent units and unexpected dates. Decide how to handle them and explain the effect of those choices.
Start with a useful baseline
Compare a simple approach with a more complex model. Choose based on the task, evidence and practical constraints rather than novelty.
03 · PRACTICAL WORK
A project to learn from
Take a small question that can be answered with data you have permission to use. Define success before choosing a method.
Example task: A small service has fewer returning users this month. What changed, what data would you check, and what can you conclude?
Define what counts as a returning user, check whether data collection changed, compare suitable periods and describe uncertainty. A model may help, but it is not automatically the first step.
Good analysis makes the reasoning and its limits visible. The balance of preparation, modeling and communication varies with the project.
Mentrast can help you learn statistics, SQL, analysis and communication in a sequence shaped around your goal. Try exercises and discuss your work with the tutor. Use an external notebook or data tool for larger analyses, then discuss the method and results.
Start learning