INFO-H 501 Introduction to Data Science Programming
3 credits
- Prerequisite(s): Prior programming experience
- Delivery: Online
Description
This course covers how to develop and test object-oriented programs for data science applications, including classification and approximation. Students learn to design simulations, determine algorithm complexity, test hypotheses, and evaluate reliability and validity. Topics include clustering, decision trees, graph optimization, histograms, linear regression, plotting, probability, and sampling.
Learning Outcomes
- Design, implement, test and debug extensible and modular programs involving control structures variables, expressions assignments, I/O functions, parameter passing, data structures, regular expressions, and file handling.
- Analyze complexity in algorithm development.
- Investigate research questions and design by loading, extracting, transforming, and analyzing data from various sources.
- Test hypotheses and evaluate reliability and validity.
- Implement histograms, classifiers, decision trees, sampling, linear regression and projectiles in a scripting language.
- Decompose and simulate systems to process data using randomness.
- Employ supervised and unsupervised machine learning for functional approximation and categorization.
Policies and Procedures
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