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Resume & References:

Jennifer E. Yoon resume - PDF

Jennifer E. Yoon resume - WORD

Recommendation Letter from

Mr. Brandon Becker, former SEC Director.

Recommendations from

Crescat Capital LLC.

 

 

Bio

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-VBA with 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.

My Journey as of May 2019, at Learning Data Science and Python

I feel that I have made a huge progress at learning data science in the past year, from May 1, 2018, when I re-dedicated my time, to May 14, 2019, the latest Data Science & Machine Learning Meetup event. My decision to attend SciPy Conference in Austin, Texas in July 2018 seems to have been a very good decision. It kick started my motivation, and seems to have pushed my knowledge one level higher. My decision to attend the GARP Conference in New York in February 2019 also seems to have been a good one. It gave me another kick to my motivation. That was interesting because that conference was for Risk Managers in the financial industry and not for Python developers. I saw that people in the financial industry were starting to use machine learning tools and especially the Python language. This was a huge validation that my skills in machine learning with Python will be valuable in later job search in the financial industry.

Data Science Portfolio Website

I am adding machine learning and data manipulation/cleaning examples to my data science porfolio website. I welcome any feedback on the site content or style. If any section is unclear or could use improvement, please email feedback directly to "datasciY.info" at gmail.com :-D.

Data Science learning bookshelf in May 2019

Books for Data Science in 2019.

Updated bookshelf, from left to right in most useful now. The main books I am using in May 2019 to finish up machine learning are James et al. ISLR () and Vanderplas () along with a Udemy DSML class () and ISLR Youtube channel (). (Will add direct links later.) In 2018, I finished studying ISLR book with a local Meetup group. The same Meetup group is starting Stanford CS231n together (), Deep Learning for Visual Image Recognition. I am using Shukla Tensorflow, Chollet Deep Learning and Goodfellow Deep Learning for the CS231n class.

This is my reading bookshelf in 2017

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.

Books on a shelf.

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 --