Array and matrix operations, functions and function handles, control flow, plotting and image manipulation, cell arrays and structures, and the Symbolic Mathematics toolbox.Ĭatalog Description: Self-paced course in the C programming language for students who already know how to program.
Sampling and introduction to inference.Ĭatalog Description: Introduction to the constructs in the Matlab programming language, aimed at students who already know how to program. Relationship between numerical functions and graphs. Introduction to statistical concepts including averages and distributions, predicting one variable from another, association and causality, probability and probabilistic simulation. Visualizing univariate and bivariate data with bar charts, histograms, plots, and maps. Programming practices, abstraction, and iteration. Expressions, data types, collections, and tables in Python. Emphasizes the use of computation to gain insight about quantitative problems with real data. Introduction to Computational Thinking with DataĬatalog Description: An introduction to computational thinking and quantitative reasoning, preparing students for further coursework, especially Foundations of Data Science (CS/Info/Stat C8). It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. Catalog Description: Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance.
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