Learn Foundations of Python Natural Language Processing and Computer Vision with my Video Course: Applications of Statistical Learning with Python

I’m pleased to announce my fourth and final video course. The course has already been out for a couple months by now, but that doesn’t mean it’s too late for me to write about it!

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High Dimensional Data, MSRI, and San Francisco in 2018; Reflections

Last fall my adviser alerted me to the MSRI workshop on high-dimensional data and suggested I may be interested. I applied and was accepted to participate. Thus, from July 9th to July 20th I stayed in San Francisco (for the first time in my life), living in the dorms of UC Berkeley and attending the workshop. I got to experience San Francisco’s legendary weather (escaping Salt Lake City’s triple-digit heat) while learning mathematics. I enjoyed the experience and wanted to share it.

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Stock Data Analysis with Python (Second Edition)


This is a lecture for MATH 4100/CS 5160: Introduction to Data Science, offered at the University of Utah, introducing time series data analysis applied to finance. This is also an update to my earlier blog posts on the same topic (this one combining them together). I strongly advise referring to this blog post instead of the previous ones (which I am not altering for the sake of preserving a record). The code should work as of July 7th, 2018. (And sorry for some of the formatting; WordPress.com’s free version doesn’t play nice with code or tables.)

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Learn Basic Python and scikit-learn Machine Learning Hands-On with My Course: Training Your Systems with Python Statistical Modelling

This post is actually months late, but like with my last video course announcement, it’s better late than never. And besides, of my video courses, I had the most fun writing this one.

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An Introduction to Stock Market Data Analysis with Python (Part 1)

THIS POST IS OUT OF DATE: AN UPDATE OF THIS POST’S INFORMATION IS AT THIS LINK HERE! (Also I bet that WordPress.com just garbled the code in this post.)

I’m keeping this post up for the sake of preserving a record.

This post is the first in a two-part series on stock data analysis using Python, based on a lecture I gave on the subject for MATH 3900 (Data Science) at the University of Utah. In these posts, I will discuss basics such as obtaining the data from Yahoo! Finance using pandas, visualizing stock data, moving averages, developing a moving-average crossover strategy, backtesting, and benchmarking. The final post will include practice problems. This first post discusses topics up to introducing moving averages.

NOTE: The information in this post is of a general nature containing information and opinions from the author’s perspective. None of the content of this post should be considered financial advice. Furthermore, any code written here is provided without any form of guarantee. Individuals who choose to use it do so at their own risk.

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