JENNIFER YOON'S WEBSITE RUMINATIONS
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I am a Data Scientist working on data science projects relevant to the financial industry. I am also a certified Financial Risk Manager (FRM), and received an MBA from the University of Chicago, Booth School of Business. I am interested in projects using Python, SQL, and Excel-VBAwith data modeling tools, such as Machine Learning, Principal Component Analysis (PCA), k-Means Clustering, Linear and Logistic Regressions, Decision Trees and Random Forests, and Bayesian Classifiers. I am also interested in clear and beautiful presentation of data in charts, interactive graphs, business-case diagrams, and reproducible research.
Data Science Portfolio
I am working on a data science portfolio website. A working draft website is available at datasciY.com
This is my reading bookshelf.
In 2017, I am focusing on Python as my primary programming language. In previous years, my project interests included Interactive Python, Bloomberg-Excel DAPI, Excel-VBA, R, SQL database development, and financial modeling (options, swaps, fixed income) in Excel-VBA and MATLAB. I also studied and took classes in mathematical statistics and stochastic calculus to supplement my university training.
July 26, 2017
A close friend of mine has a super-cool Python book out. I think Juan's goal is to help scientific data users write more elegant Python code. It's not for the beginner, but may help you up your game. Visuals are beautiful! Please check it out!
February 26, 2017
Just discovered a free Python executable cloud-based service from Microsoft, the Azure Machine Learning Web Service using the Jupyter Notebook environment. Python is one of the executable web kernels. It's free for now while it is in the preview mode. Seems to be the best option right now for putting Python executable codes online. Being able to go live with a few clicks is definitely a winning point. Codeskulptor still works for simple python codes, but has limits on libraries that can be loaded. (http://py3.codeskulptor.org and notebooks.azure.com)
I am also going through the new Python Data Science Handbook, by Jake VanderPlas, copyright 2017. Amazon link: Python Data Science Handbook Very much enjoying it so far. In my opinion, the writing is cleaner, there are no obvious errors in code or code output, and it's more up to date than the previous standard, Python for Data Analysis by Wes McKinney, copyright 2013. However, Wes did write Pandas from scratch, and he is still a core maintainer. So if you wish to support Pandas, an extremely useful library for data munging, you may consider buying his book or sending him an Amazon gift certificate. :-) Amazon link: Python for Data Analysis book
I am working on examples for the financial industry that uses Python and Python data science tools. I plan to post some here when ready, so please check in again later.
-- Jennifer Yoon --
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