CodeDotNet

This site is often under construction as it is used to expand my skills by exploring ideas and techniques for .NET, combining ASP.NET, Silverlight applications, web services, and a VB/C# desktop client to access selfsame web services.

Updates and Announcements

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

  • Supporting Material
  • Load Data: Format Data & Sort
  • Prep Data: Create Returns
  • Eigen Decomposition and Scree Plot
  • Create Principal Components
  • FVX using PCA versus Logistic Regression
  • Alternative Libraries: Psych for the Social Sciences

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
  • To provide a general outline of a statistically-oriented method to improve funding activities by mining your current donor database
  • To provide general techniques for analyzing data, as well as provide cautions against bad techniques
How the Process Can Improve Endowment Activities
  • Allows the organization to more accurately target quality prospects, either to increase participation rates, or to find major givers more inclined to donate
  • Allows the organization to reduce costs, or more effectively use limited resources, i.e., phone smaller sets of people, limit the size of mailings, while increasing donations
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
  • Assumes the donor data is extractable and randomized
  • Requires export from donor database, or access via SQL
  • Assumes additional software for statistics (DataDesk, SAS, SPSS)
Limitations
  • Requires IT staff, analytical staff, donor contacts, and management to coordinate efforts
  • Requires IT and analytical staff have adequate skills to implement
  • Judges variables of data by both its intrinsic value and based upon its inclusion in database
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