Long perceived as an expert in dashboards and numerical reports, the data analyst today sees their role evolving profoundly. With the rise of machine learning, it is no longer just about understanding what happened, but about anticipating what will occur. A transformation that is gradually redefining the skills expected, including for entry-level profiles.
Understanding yesterday, predicting tomorrow
Historically, the data analyst analyzes past data to inform decisions. Identifying sales, understanding user behavior, or measuring the performance of a marketing campaign are among their main tasks.
But this approach, called descriptive, now shows its limits. Companies are now looking to anticipate trends, detect anomalies, or recommend actions. This is where machine learning comes into play. This branch of artificial intelligence allows systems to learn from data to generate predictions.
In practice, a data analyst can today build a model to forecast the demand for a product, detect fraud, or anticipate customer churn. These uses are becoming widespread across many sectors, from e-commerce to finance.
An evolution driven by data growth
This transformation occurs in a context of strong data growth. According to an IDC study, the global volume of data is expected to reach 175 zettabytes by 2025, compared with 33 in 2018. At the same time, analyses from Forrester show that many companies still struggle to fully exploit this data.
The challenge is therefore no longer just to produce dashboards, but to make data directly useful for decision-making. Companies are now looking for profiles capable of going beyond descriptive analysis, by incorporating predictive logic.
This evolution is reflected in job postings, where skills in Python, modeling, and machine learning are appearing more and more, including for junior roles.
Skills that are evolving, without becoming inaccessible
However, the profession does not become inaccessible. The machine learning used by data analysts often remains applied. It is not about developing complex algorithms, but about using existing tools to meet concrete needs.
Fundamentals remain essential. SQL, data visualization and cleaning remain at the heart of the job. But these skills are gradually broadening with Python or certain machine learning libraries.
This skill progression is achieved mainly through practice. Building a recommendation model or a forecasting tool makes it possible to quickly understand the issues without entering excessive complexity.
A pedagogy that adapts to the needs of the market
Faced with these changes, the way of learning data is also evolving. Approaches that are too theoretical show their limits, especially for people retraining. Understanding concepts is no longer enough; one must be able to apply them.
More and more training programs therefore include concrete projects based on real cases. This immersive approach allows learners to work with data, build models, and better understand expectations on the ground. It also aligns with recruiters' expectations, who favor operational profiles.
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Some recent training programs, such as the data analyst training offered by La Capsule, illustrate this evolution. They combine learning the fundamentals, an introduction to machine learning, and projects inspired by real-world situations. They also include elements of data engineering, enabling learners to understand how to collect, structure, and automate data flows using tools employed in industry.
The goal is not to train artificial intelligence specialists, but profiles able to master the entire data lifecycle, from extraction to analysis, and to draw useful insights in a professional context.
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