Knowra Data science Data science Data science combines statistical reasoning, computing, and domain knowledge to extract insights and build models from data. It spans the full process from collecting and preparing data to communicating results.
Data wrangling : The process of cleaning, transforming, and organizing data for analysis. It makes inconsistent source data usable for later analysis.
Probability theory : The mathematical study of random events and uncertainty. It gives a formal language for uncertainty in data and predictions.
Recommender system : A system that predicts which items or content a person may prefer. Recommendation is a familiar deployment of data-driven prediction.
Data analytics : The examination of data to answer questions, identify patterns, and support decisions. Analytics often emphasizes interpreting existing data, while data science also builds predictive systems.
Exploratory data analysis : The use of summaries and visualizations to investigate a dataset before formal modeling. It reveals patterns, anomalies, and assumptions worth testing.
Linear algebra : The study of vectors, matrices, and linear transformations. Many data representations and machine-learning algorithms use vectors and matrices.
Credit scoring : The assessment of a borrower’s likelihood of repaying credit using financial and other relevant information. It illustrates data science applied to consequential risk decisions.
Business intelligence : Technologies and practices for reporting and analyzing organizational data to support business decisions. It commonly centers on recurring reports and metrics rather than model development.
Statistical inference : Methods for drawing conclusions about populations or processes from observed data. It distinguishes evidence about a broader process from patterns in a sample.
Algorithms : Finite, well-defined procedures for solving problems or performing computations. Data science relies on algorithms to transform data and fit models.
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