Data science is an interdisciplinary field focused on extracting meaningful insights and knowledge from raw, structured, or unstructured data by combining expertise in statistics, computer science, mathematics, and domain knowledge. It differs from traditional data analysis by its ability to handle large-scale data (big data), automate analyses using advanced algorithms, and generate predictive or prescriptive models. The process typically involves data collection, cleaning, exploration, modeling, and interpretation, often in conjunction with artificial intelligence and machine learning.
Use cases and examples
Data science is widely used for detecting banking fraud, personalizing recommendations (such as on streaming platforms or e-commerce), optimizing industrial processes (predictive maintenance, supply chain management), sentiment analysis on social media, and personalized medicine. It also helps anticipate market trends or optimize marketing campaigns through behavioral analysis.
Main software tools, libraries, and frameworks
Key tools include programming languages like Python and R, and libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch. Platforms like Apache Spark, Hadoop, Databricks, as well as visualization tools like Tableau and Power BI, are also widely used. Jupyter Notebook is a common environment for prototyping and documenting analyses.
Latest developments, evolutions, and trends
Data science is rapidly evolving with the rise of generative artificial intelligence, increased automation of workflows (AutoML), and the integration of deep learning for unstructured data analysis (images, text, video). Governance and ethical issues are gaining importance, as are data quality and data sovereignty. Cloud computing also facilitates scalability and collaboration in data science projects.