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 the Data science is OSEMN module for Obtaining Data I worked through the exercises. Example Code # Exercises """http://people.duke.edu/~ccc14/sta-663/DataProcessingSolutions.html#exercises""" """1. Write the following sentences to a file “hello.txt” using open and write. There should be 3 lines in the resulting file. Hello, world. Goodbye, cruel world. The world is your oyster.""" str = 'Data\Test.txt' f = open(str, 'w') f.write('Hello, world.\r') f.write('Goodbye, cruel world.\r') f.write('The world is your oyster.') # Writes same thing, only in one statement f.write('\rHello, world.\rGoodbye, cruel world.\rThe world is your oyster.') f.close() with open(str, 'r') as f: content = f.read() print(content) """2. Using a for loop and open, print o...

OESMN is an acronym, for the elements of data science: Obtaining data Scrubbing data Exploring data Modeling data iNterpreting data As part of the Data science is OSEMN module  for Obtaining Data I performed the examples myself, as well as with variants. Example Code # Data science is OSEMN """http://people.duke.edu/~ccc14/sta-663/DataProcessingSolutions.html#data-science-is-osemn""" # Acquiring data """Plain text files We can open plain text files with the open function. This is a common and very flexible format, but because no structure is involved, custom processing methods to extract the information needed may be necessary. Example 1: Suppose we want to find out how often the words alice and drink occur in the same sentence in Alice in Wonderland. This example uses Gutenberg: http://www.gutenberg.org/wiki/Main_Page, a good source for files""" # the data file is linked below...

I was working through a tutorial in Python, but found this one, Computational Statistics in Python from Duke , much better. It has exercises, which increase retention, and the material is well-presented.