Key stages in getting started with data analysis

1. Understanding the fundamentals of the field

Even before coding or visualising, it's essential to understand what data analysis is. It consists of collecting, cleaning, transforming, analysing and presenting data to guide business actions. Familiarising yourself with the different types of analysis (descriptive, diagnostic, predictive, prescriptive) and adopting pragmatic frameworks such as BADIR will help to anchor a results-oriented approach.

2. Acquire essential technical skills

A solid foundation in technical skills is essential. Start with Excel, with its advanced functions and pivot tables, which will still be in great demand in 2025. Next, learn SQL to query databases, a skill that is required for more than half of all analyst jobs. Also learn the basics of Python or R, via Pandas and Matplotlib, to automate analyses. Finally, explore modern visualisation tools such as Tableau, Power BI or Looker, which are essential for sharing your insights visually.

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3. Applying statistics and analytical rigour

Statistics are at the heart of data analysis. It is essential to master concepts such as descriptive statistics, probability, distributions, hypothesis testing and regression. They enable you to analyse data rigorously, avoid pitfalls in interpretation and provide a solid context for the results.

4. Carrying out concrete projects to stay in control

There's nothing like practice to progress. Work on complete projects using public data, or take part in Kaggle challenges. Each project must include the whole cycle: from cleaning through statistical analysis to visual presentation. These structured projects become compelling case studies in a portfolio, demonstrating your skills and methodology.

5. Developing data communication and narration

Analysis is useless if it isn't understood. Learn how to tell a story with data, adapt your discourse to non-technical audiences and create impactful visuals. This narrative skill enables you to convince decision-makers, defend your conclusions and generate concrete action.

6. Entering the market: applications and interviews

Prepare a clear, project-focused CV, optimised with the keywords that recruiters are looking for. Be prepared for technical interviews on SQL, visualisation tools and role-playing, as well as behavioural interviews to assess your logic, curiosity and communication skills. Don't hesitate to apply for "junior" or "analyst I" positions, even if you don't meet all the criteria.

Getting started in data analysis requires a combination of :

1. technical and statistical knowledge,
2. clear and rigorous methodology,
3. projects in a portfolio,
4. the ability to tell a story with impact,
5. community commitments or certifications,
6. effective preparation for interviews.

This structured career path will give you access to opportunities in a fast-growing field where analytical skills are more highly valued than ever.

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