Applied Data Science with Venture Applications
This highly-applied course surveys a variety of key of concepts and tools that are useful for designing and building applications that process data signals of information. The course introduces modern open source, computer programming tools, libraries, and code samples that can be used to implement data applications. The mathematical concepts highlighted in this course include filtering, prediction, classification, decision-making, Markov chains, LTI systems, spectral analysis, and frameworks for learning from data. Each math concept is linked to implementation using Python using libraries for math array functions (NumPy), manipulation of tables (Pandas), long term storage (SQL, JSON, CSV files), natural language (NLTK), and ML frameworks.
Topics
- Students will be able to design and build data sample application systems that can interpret and use data for a wide range of real life applications across many disciplines and industries; understand relevant mathematical concepts that are used in systems that process data; implement these concepts within applications with modern open source CS tools.
Official catalogue course objectives and student learning outcomes
Prerequisites & course information
{'preRequisiteCourse': '1245673', 'concurrentPrerequisit': False}; {'preRequisiteCourse': '1245674', 'concurrentPrerequisit': False}; {'preRequisiteCourse': '1245671', 'concurrentPrerequisit': False}; {'preRequisiteCourse': '1245672', 'concurrentPrerequisit': False}
Sources
- Course catalogue ↗2026-2027