2017-10-12

Principal Component Analysis (PCA) on Stock Returns in R

Principal Component Analysis is a statistical process that distills measurement variation into vectors with greater ability to predict outcomes utilizing a process of scaling, covariance, and eigendecomposition.

MS Azure Notebook

The work for this is done in the following notebook, Principal Component Analysis (PCA) on Stock Returns in R, with detailed code, output, and charts. An outline of the notebook contents are below.

Overview of Demonstration

2017-08-18

Exercises in Programming Style by Cristina Videira Lopes

Exercises in Programming Style Exercises in Programming Style by Cristina Videira Lopes
My rating: 5 of 5 stars

An easily consumed, enjoyable read, and excellent review of the history of programming style, from older days of constrained memory and monolithic styles, through pipelining and object-oriented variants, to more recent patterns like model-view-controller (MVC), mapreduce, and representational state transfer (ReST). Along the way, each variant is described, along with its constraints, its history, and its context in systems design.

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2017-07-31

Data Mining for Fund Raisers

This is a repost of a Goodreads' review I did a little over 4.5 years ago, for a book I read twelve (12) years ago, which seemed relevant, as the industry seems to be picking up a data-driven focus. Plus, the world is now being transformed by advances in machine learning, particulary deep learning, and the large data sets and complexity of donor actions should greatly benefit from analysis.

Data Mining for Fund Raisers: How to Use Simple Statistics to Find the Gold in Your Donor Database Even If You Hate Statistics: A Starter GuideData Mining for Fund Raisers: How to Use Simple Statistics to Find the Gold in Your Donor Database Even If You Hate Statistics: A Starter Guide by Peter B. Wylie

My rating: 4 of 5 stars

My spouse, at times a development researcher of high-net worth individuals, was given this book because she was the 'numbers' person in the office. Since my undergraduate was focused on lab-design, including analysis of results using statistics, I was intrigued and decided to read it. Considering my background, I found some of the material obvious, while other aspects were good refreshers on thinking in terms of statistics.

Below is the synopsis I wrote at the time:

Purpose of Book
How the Process Can Improve Endowment Activities
Outline of Method (Non-Technical)
  1. Export sample of donor database
  2. Split sample into smaller components
  3. Find relationships between donor features and giving
  4. Select the significant variables
  5. Develop scoring system
  6. Validate findings
  7. Test finding on limited appeals and compare results
Assumptions
Limitations
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2017-07-20

Tips for Staying Employed as an Older Developer

A response to an article Tips for Staying Employed as an Older Developer:

A bit about myself, older and working as a developer, team lead and project manager, writing here to add to the options for staying relevant, and how to let the world know about it.

Some Tips
2017-07-11

Value-at-Risk (VaR) Calculator Class in Python

As part of my self-development, I wanted to rework a script, which are typically one-offs, and turn it into a reusable component, although there are existing packages for VaR. As such, this is currently a work in progress. This code is a Python-based class for VaR calculations, and for those unfamiliar with VaR, it is an acronym for value at risk, the worst case loss in a period for a particular probability. It is a reworking of prior work with scripted VaR calculations, implementing various high-level good practices, e.g., hiding/encapsulation, do-not-repeat-yourself (DRY), dependency injection, etc.

Features:
Still to do:
Note: Data to validate this class is available from my Google Drive Public folder.
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