Data science is an interdisciplinary field that combines statistics, programming, and domain expertise to extract meaningful insights from data. A data scientist typically draws on tools from mathematics, computer science, and the application area in question, using code to turn raw observations into decisions, predictions, or scientific findings. The work is rarely a single step; rather, it follows a well-known lifecycle that begins with defining the problem, moves through data collection, cleaning, exploration, modeling, and evaluation, and ends with deploying the result so it can be used by stakeholders. This lifecycle is intentionally iterative: findings in evaluation often send the practitioner back to cleaning or modeling.
To make this lifecycle repeatable, teams often adopt a structured methodology such as CRISP-DM, the Cross-Industry Standard Process for Data Mining. CRISP-DM formalizes the stages above and emphasizes that understanding the business problem and the data come before any algorithm is chosen. At the heart of any project sits the dataset: a collection of related data usually arranged as rows (observations) and columns (variables). Each column used as input is called a feature, a measurable property such as age, price, or pixel intensity, while the column the model tries to predict is called the target variable. Distinguishing features from the target is the first move in translating a real-world question into a machine learning problem.
Different problems call for different kinds of learning. Supervised learning trains on labeled examples to predict outcomes; unsupervised learning seeks patterns such as groups or low-dimensional summaries in unlabeled data; reinforcement learning has an agent learn by taking actions and receiving rewards or penalties; and semi-supervised learning blends a small amount of labeled data with a larger pool of unlabeled data. Within supervised learning the target's data type determines the task: regression predicts a continuous numeric output, classification predicts a categorical label, and clustering (an unsupervised counterpart) groups similar points without predefined labels. Dimensionality reduction, meanwhile, compresses many features into fewer while preserving information, often as a preprocessing step or for visualization.
The people doing this work have distinct roles. A data scientist focuses on analysis and modeling, framing questions and choosing methods. A data engineer builds and maintains the pipelines, warehouses, and infrastructure that move and shape data at scale. A data analyst typically sits closer to reporting and business intelligence, querying data with SQL and building dashboards. These roles overlap, but recognizing them helps clarify who owns which part of the pipeline, from raw ingestion to communicated insight.