DataScience

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It blends statistics, computer science, and domain expertise to turn data into actionable outcomes. The term gained prominence in the 2000s, with proponents like DJ Patil and Jeff Hammerbache helping raise its profile.

The typical data science workflow includes data collection and cleaning, exploratory data analysis, feature engineering, model building, evaluation, and deployment. Data scientists often work with data engineers to prepare data pipelines and with analysts and domain experts to interpret results. Methods span statistics, machine learning, data mining, and visualization, applied to problems in prediction, classification, clustering, and causal inference.

Common tools include programming languages such as Python and R, databases and SQL, and big data platforms like Hadoop and Apache Spark. Visualization and reporting tools like Tableau or Power BI are used for communication. Roles include data scientist, data engineer, data analyst, and machine learning engineer, with education typically in computer science, statistics, mathematics, or a related field; many also pursue specialized bootcamps or master's programs.

Applications are broad, including business analytics, finance, healthcare, public policy, and scientific research. Data science supports decision-making, automation, and innovation, but also raises concerns about privacy, bias, and reproducibility.