Introduction to the most commonly used data analysis tools

Excel: a solid basis for getting started

Excel, developed by Microsoft, remains one of the most widely used tools in the world, despite the emergence of more sophisticated tools. Its simplicity, combined with powerful features such as pivot tables, advanced formulas and macros, makes it the preferred choice for small analyses and individual projects. For example, a marketing team might use Excel to analyse data from an advertising campaign, compiling customer feedback and performance.

However, Excel shows its limitations when it comes to processing large amounts of data or collaborating with several people in real time. Despite this, it remains an essential introductory tool for those new to data analysis.

Python: the language of data experts

Python has established itself as a pillar of data analysis thanks to its versatility and the richness of its ecosystem. This open-source language is appreciated for its simple syntax and its ability to manage complex projects. Let's take the example of a company that wants to analyse its customers' buying behaviour: with libraries like Pandas for data manipulation, Matplotlib and Seaborn for visualisation, Python makes it possible to discover hidden trends in large datasets.

Python also excels in the field of machine learning, with libraries such as TensorFlow and scikit-learn, enabling companies to predict future behaviour or automate processes. It is an essential tool for data scientists looking for a powerful and flexible solution.

SQL: the foundation of database management

Relational databases remain the backbone of many organisations. SQL, or Structured Query Language, is the standard tool for managing and querying these databases. Its strength lies in its syntactic simplicity and its ability to execute complex queries. For example, an e-commerce company might use SQL to identify the best-selling products, peak times or customer preferences.

By combining SQL with other tools such as Python or Tableau, analysts can build robust data pipelines, from extracting raw data to generating ready-to-use reports.

Tableau: powerful visualisations for decision-making

There's more to data analysis than just manipulating it. Decision-makers need clear visualisations to quickly understand trends and act accordingly. What sets Tableau apart is its ability to transform complex data into interactive, visual dashboards. For example, a sales team can create a dashboard to monitor its performance in real time, identify missed opportunities and adjust its strategies.

Thanks to its intuitive interface, Tableau is accessible even to non-technical users, making it an invaluable tool for democratising access to data within an organisation.

Use cases for analysis tools

These tools are not just technologies; they are practical solutions to real challenges. For example, a company looking to reduce its operating costs might use Python to identify inefficiencies, SQL to extract the relevant data and Tableau to visualise the results. In another scenario, a start-up might start with Excel for simple analyses before moving on to more complex tools as its needs evolve.

The choice of tool always depends on the context and objectives. Excel is perfect for beginners or small analyses, Python excels in complex, automated projects, SQL is indispensable for databases, and Tableau shines in visualisation. These tools are not mutually exclusive; quite often, professionals combine their strengths to achieve the best results.

At the School of Data, we offer practical training courses to help you master these essential tools. Discover our programmes here and learn how to turn your data into strategic levers.

Others news in this theme