###
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 short-sightedness 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 s much more that 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:

- Statistical Methods Should Enable Data to Answer Scientific Questions
- Signals Always Come with Noise
- Plan Ahead, Really Ahead
- Worry about Data Quality
- Statistical Analysis Is More Than a Set of Computations
- Keep it Simple
- Provide Assessments of Variability
- Check Your Assumptions
- When Possible, Replicate!
- Make Your Analysis Reproducible

###
Data Analytics Workouts: Recent Posts using R

Below are some recent posts regarding my work learning R, where I took prior work I had done in Excel, country correlations on human welfare, or F#, solving Project Euler problems, and re-executed them, and to some degree improved upon them.

Plotting Text Frequency and Distribution using R for Spinoza's A Theological-Political Treatise [Part I]
Inequality Kills: Correlation, with Graph and Least Square, of Gini Coefficient (Inequality) and Infant Death
Chi-Square in R on by State Politics (Red/Blue) and Income (Higher/Lower)
Logistic Regression in R on Politics and Income
Multiple Regression with R, on IQ for Gini and Linguistic Diversity
Linear Regression with R, on IQ for Gini and Linguistic Diversity
Mean Median, and Mode with R, using Country-level IQ Estimates
Correlations within with Hofstede's Cultural Values, Diversity, GINI, and IQ
ANOVA with Hofstede's Cultural Values and Economic Outcomes
Text Parser and Word Frequency using R
Project Euler: F# to R
###
Data Analytics Workouts: Recent Posts using Python

I have been exploring Python and R for manipulating data. My long terms goals for this exercise, that I document on the blog, are to develop skills in the aforementioned languages, as well as extend my abilities with F# and explore other technologies like NoSQL Db's. Things like Hadoop and Spark are likely much farther down the road, if at all.

Exercises: OESMN (Obtaining, Scrubbing, Exploring, Modeling, iNterpreting)
OESMN (Obtaining, Scrubbing, Exploring, Modeling, iNterpreting): Getting Data
Computational Statistics in Python: Exercises
Promising Power: functools and itertools of Python
Iterators, Generators, and Decorators in Python
Recursion in Python
Functions are first class objects: Higher Order Functions