UC Irvine, Math 10, Fall 2021¶
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Original source¶
I originally duplicated the University of Washington’s Visualization Curriculum. I thank the authors for making this material available not just to use, but to edit via their GitHub source code.
Current content¶
As of October 27th, 2021, the content from Weeks 1 and 2 is disorganized (it’s mostly just the notebooks from lecture), but the content from Week 3 and beyond is more readable.
Course-level Learning Outcomes¶
The goal of this course is to introduce programming in Python, with an emphasis on some of the tools that are most relevant to data science. The primary learning outcomes for Math 10 are that students will be able to:
select appropriate data types (both built-in Python types as well as types defined in external libraries) when performing computations;
manipulate structured data using NumPy and pandas;
display significant aspects of datasets using Altair;
run machine learning algorithms using scikit-learn;
improve the performance of various machine learning algorithms by adjusting parameters;
evaluate simple neural networks by hand, and complex neural networks using Keras;
apply tools from unfamiliar Python libraries using some combination of documentation, error messages, and code examples written by experienced programmers;
create an interactive, data-focused web app using Streamlit.