Review: Text Analysis with R for Students of Literature (Quantitative Methods in the Humanities and Social Sciences)

Text Analysis with R for Students of Literature (Quantitative Methods in the Humanities and Social Sciences)Text Analysis with R for Students of Literature by Matthew L. Jockers
My rating: 5 of 5 stars

Engaging writing, with code samples and practices. As for programming, I thought the code quality was somewhat low or sloppy, but Jockers is not a software developer by trade. While reading, I did have a few ideas to solve some text-matching issues across systems, and generally, I found the lack of discipline in the author's approach conducive to flexible thinking about using techniques with R.

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Cultures and Organizations: Software of the Mind, Third Edition

Although this is not a technical book, it is highly relevant to management, project and otherwise, particularly if you work in technology. Our teams are often composed of internationally originating, and internationally located members, and understanding how such difference might affect team dynamics is essential.

Cultures and Organizations: Software of the Mind, Third Edition Cultures and Organizations: Software of the Mind, Third Edition by Geert Hofstede
My rating: 4 of 5 stars

A detailed and fascinating review of Hofstede's dimensions, by the researcher himself, showing broad high-level insights into history and culture, although a bit tedious, as it often describes in detail relationships many of us implicitly understand.

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Data Analytics Workouts: Recent Post using R

I have dabbled with statistical analysis since college, eventually wanting to publish academically while I was in B-School, and recent growth in big data and analytics motivated me to update some of my prior analyses with new technologies. This is not a prior analysis, but I was struck by the lackluster reporting of the correlation between obesity and indulgence, and wanted to delve further, to look at the compound relationship between both indulgence and LTO, e.g., does shortsightedness and indulgence lead to obesity.

Hofstede's Long-term Orientation and Individuality: Obesity Relationships (using R)

Transitioning to Project Management

As I transition back to project management work, as well as pick up more business analysis skill, I have found it useful to work through a few Pluralsight videos. First, I was quite surprised how much I got from learning more about MS Project. It is much more than just Gantt charting! As for project management, some elements are native to anyone that plans, like a work breakdown structure or the timeline, but there are very important aspects of issue control and communication that need to be part of one's PM toolkit.

Ten Simple Rules for Effective Statistical Practice

A interesting article by six statisticians, Ten Simple Rules for Effective Statistical Practice, and their aim:
To this point, Meng notes "sound statistical practices require a bit of science, engineering, and arts, and hence some general guidelines for helping practitioners to develop statistical insights and acumen are in order. No rules, simple or not, can be 100% applicable or foolproof, but that's the very essence that I find this is a useful exercise. It reminds practitioners that good statistical practices require far more than running software or an algorithm."
The 10 rules are:

  1. Statistical Methods Should Enable Data to Answer Scientific Questions
  2. Signals Always Come with Noise
  3. Plan Ahead, Really Ahead
  4. Worry about Data Quality
  5. Statistical Analysis Is More Than a Set of Computations
  6. Keep it Simple
  7. Provide Assessments of Variability
  8. Check Your Assumptions
  9. When Possible, Replicate!
  10. Make Your Analysis Reproducible
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