Data Analytics Workouts

This is a repost of a Goodreads' review I made in 2013, for a book I read in 2005, which seems relevant now, as the industry is adding a data-driven focus. Plus, the world is now being transformed by advances in artificial intelligence and machine learning (AI/ML), particularly deep learning, and the large data sets and complexity of donor actions should greatly benefit from analysis. Note, the tax changes for 2018 and beyond will increase the importance of major donors, attenuating the benefits of AI/ML, as data for high-net-worth individuals is sparse. 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 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 ...

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: Requires data frame of stock returns, factor returns, and stock weights Expose a method to calculate and return a single VaR number for different variance types Expose a method to calculate and return an array of VaR values by confidence level Expose a method to calculate and plot an array of VaR values by confidence level Still to do: Dynamic factor usage Note...

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: Load and clean Data Calculate returns Calculate historical variance Calculate systemic, idiosyncratic, and total variance Develop a range of stress variants, e.g. scenario-based possibilities Calculate VaR as the worst case loss in a period for a particular probability The modules: In R: Financial Risk - Calculating Value At Risk (VaR) with R In Python: Financial Risk - Calculating Value At Risk (VaR) with Python

As part of understanding neural networks I was reading Make Your Own Neural Network by Tariq Rashid. A review is below: Make Your Own Neural Network by Tariq Rashid My rating: 4 of 5 stars The book itself can be painful to work through, as it is written for a novice, not just in algorithms and data analysis, but also in programming. For the neural network aspect, it jumped between overly simplistic and complicated, while providing neither in enough detail. That said, by the end I found it a worthwhile dive into neural networks, since once it got to the programming structure, it all made sense, but only because I stuck with it. View all my reviews

Introduction As part of understanding neural networks I was reading Make Your Own Neural Network by Tariq Rashid. The book itself can be painful to work through, as it is written for a novice, not just in algorithms and data analysis, but also in programming. Although the code is a verbatim transcription from the text (see Source section), I published it to better understand how neural networks are designed, made easy by the use of a Jupyter Notebook, not to present this as my own work, although I do hope that this helps others develop their talents with data analytics.  Github Source:  AzureNotebooks/Basic Three Layer Neural Network in Python.ipynb at master · JamesIgoe/AzureNotebooks (github.com) Overview The code itself develops as follows: Constructor set number of nodes in each input, hidden, output layer link weight matrices, wih and who weights inside the arrays are w_i_j, where link is from node i to node j in the next layer set learning rate activat...

Overview What follows is an exploration of clustering, via hierarchies and K-Means, using the Hofstede patterns data, available from my Public folder . For a deeper understanding of clustering and the various related techniques I suggest the following: Cluster analysis (Wikipedia) An Introduction to Clustering and different methods of clustering Load Data # load data Hofstede.df.preclean <- read.csv("HofstedePatterns.csv", na.strings = c("", "NA")) #nrow(Hofstede.df.preclean) # remove NULLs Hofstede.df.preclean <- na.omit(Hofstede.df.preclean) #nrow(Hofstede.df.preclean) Hofstede.df <- Hofstede.df.preclean Hierarchical Clustering Run hclust, Generate Dendrogram The first attempt is the simplest analysis using the dist() and hclust() functions to generate a hierarchy of grouped data. The cluster size is derived from a reading of the dendrogram, although there are automated ways of selecting the cluster ...

I have not worked in F# for over two (2) years, but am enthused that Microsoft has added it to it languages for Data Science Workloads, along with R and Python. To that end, I hope to repost some of my existing F# code, as well as explore Data Science Workloads utilizing all three languages. Prior work in F# is available from learning F# , and some solutions will be republished on this site. Data Science Workloads Build Intelligent Apps Faster with Visual Studio and the Data Science Workload Published Work in F# James Igoe MS Azure Notebooks that utilizes MS's implementation of Jupyter Notesbooks . Mathematical Library, a basic mathematical NuGet package, with the source hosted on GitHub . Basic Statistical Functions: Very basic F# class for performing standard deviation and variance calculations. Various Number Functions: A collection of basic mathematical functions written in F# as part of my learning via Project Euler, functions for creating arrays or calc...