How to get started in Data Science: key stages and skills

1. Acquire the technical and mathematical fundamentals

The course begins with learning languages such as Python, R and SQL, which are essential for manipulating data, writing scripts, querying databases and building models. Python is particularly popular thanks to libraries such as NumPy and pandas, while SQL is the cornerstone of relational databases. At the same time, mathematics (descriptive and inferential statistics, probability, linear algebra and calculus) provides an indispensable background for understanding and developing reliable models.

2. Data quality: cleaning and visualisation

Before creating a model, you need to know how to clean and transform the data. The data wrangling stage is essential: correcting missing values, harmonising formats, detecting anomalies via Pandas or SQL. Then, visualising distributions, correlations and trends with Matplotlib, Seaborn or Tableau not only helps to present the information, but also to detect insights and improve data quality.

3. Taking Machine Learning one step further

Once the technical and mathematical foundations have been laid, it's time to study Machine Learning. The basic models (linear regression, logistic, decision trees, random forests) are well taught via scikit-learn. The recommended progression is to start with simple models before moving on to neural networks using TensorFlow or PyTorch . Learning is enriched by understanding the business context: why a model is justified or not, and how to interpret its results.

4. Carrying out practical projects and building a portfolio

There is no substitute for practice. Personal projects or voluntary assignments (open-source contributions, Kaggle mini-projects) allow you to apply the skills you have acquired and demonstrate the coherence of your reasoning. A good project becomes a structured case study: objective, method, results and learning. This portfolio is often the key to landing your first job.

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.

5. Developing complementary skills

In addition to technical skills, you need to cultivate complementary qualities: communication skills to make insights accessible to the general public, the ability to work in an interdisciplinary team and the curiosity to adapt to a specific business area. These soft skills distinguish the good profiles from those who really succeed in transforming data into value.

Getting started in Data Science requires a structured approach: mastering Python, SQL and mathematics, practising data cleaning, building projects, then progressing to advanced models and complex environments. Added to this are communication skills, technology watch and networking. By following these steps, aspiring data scientists can position themselves effectively in a fast-growing market and make a tangible contribution to creating value within their organisation.

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