The tools most commonly used in Data Science and AI in 2024

Python remains the most popular programming language in Data Science and AI. Its simplicity, combined with a vast library of modules and packages dedicated to data manipulation and machine learning, makes it a preferred choice. In 2024, libraries such as Pandas, NumPy, scikit-learn and TensorFlow are still in the lead for data processing and analysis, as well as for the development of machine learning models. Python is particularly appreciated for its flexibility, enabling data scientists to move easily from a data cleansing stage to the construction and optimisation of advanced models.

SQL remains a mainstay for database management and interrogation. In 2024, its use remains central to the data pipeline, enabling Data Scientists and Data Engineers to extract specific information and structure raw data for analysis. New versions of SQL, integrated into cloud platforms such as Google's BigQuery or Azure SQL Database, enable large-scale data processing and integration with other data science tools, improving the fluidity of workflows.

In 2024, Jupyter Notebooks remained one of the most widely used tools for exploring data and sharing analyses. They allow data scientists to code, visualise results and document their work in the same environment, facilitating communication between teams. Notebooks also offer interactive visualisation options, making them particularly useful for illustrating trends and presenting insights to non-technical teams. Google Colab or Kaggle notebooks, cloud-based alternatives that are highly prized for their free or low-cost computing power.

In the field of deep learning, TensorFlow and PyTorch still dominate in 2024. TensorFlow, developed by Google, is widely adopted for its advanced modelling capabilities and its adaptability for production. PyTorch, appreciated for its ease of parameterisation, is widely used in research and production. These two frameworks are essential for complex Deep Learning projects involving image analysis, natural language processing (NLP) and recommendation systems.

Visualisation tools such as Tableau and Power BI enable data to be transformed into visual insights that can be understood by everyone. In 2024, they were strengthened with functionalities incorporating AI to automate the creation of graphs and reports. They offer interactive visualisations that facilitate decision-making, and their connections to databases and cloud services make them indispensable for real-time collaborative working.

For processing large amounts of data, Apache Spark is a preferred choice in 2024. Spark is a distributed engine capable of processing large volumes of data quickly and efficiently. Often associated with Hadoop, it is particularly well suited to real-time analysis. Its compatibility with Python (via PySpark) makes this tool accessible and powerful for projects requiring substantial computing resources.

Cloud platforms such as Microsoft Azure, AWS, and Google Cloud Platform will dominate in 2024. These cloud services enable large-scale machine learning models to be designed, deployed and managed. They offer a scalable infrastructure, simplified workflow management, and increased accessibility to advanced machine learning tools. Native integrations with other tools facilitate every stage, from exploratory analysis to putting models into production.

Application containerisation has become a standard for deploying models in production. Docker and Kubernetes enable models in the form of APIs or Web applications to be put into production and deployed in a flexible and scalable way. In 2024, Docker is widely used to create reproducible environments, and Kubernetes has become the standard for large-scale container orchestration, particularly in machine learning applications. These tools guarantee reliability and make it easier to manage models in production.

In 2024, Data Science and AI tools continue to evolve to meet the data analysis and processing needs of businesses. From database management to machine learning modelling and results visualisation, each tool plays an essential role in the data workflow. Mastering these technologies is essential for Data Science and AI professionals, as they enable data to be transformed into added value and meet the demands of an increasingly data-driven market.