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 I read it:

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
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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

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.

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

Calculating Value at Risk (VaR) with Python or R

The following modules linked below are based on a Pluralsight course, Understanding and Applying Financial Risk Modeling Techniques, and while the code itself is nearly verbatim, this is mostly for my own development, working through the peculiarities of Value at Risk (VaR) in both R and Python, and adding commentary as needed.

The general outline of this process is as follows:
The modules:

Review: The Systems View of Life: A Unifying Vision

My rating: 5 of 5 stars

An excellent, incredibly insightful and informative book, somewhat marred by the tedium experienced in the authors' rehashing the ideas of organizations working for change. For most of this book, the writers masterfully tie together concepts in systems, mathematics, consciousness, the environment, society and biology, and for that, it is a brilliant read.

The Systems View of Life: A Unifying Vision The Systems View of Life: A Unifying Vision by Fritjof Capra

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