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Top 10 BOB Blog Posts of 2015: Data Science, Statistics, Sentiment and Theresa

datascienceblogroleskillsI've counted the pageviews of each of my 2015 blog posts and present them here in my end-of-year summary. This year, I wrote a lot about my research in the areas of data science; this research focused on the meaning of data science and how organizations can best leverage their data. Additionally, I conducted and presented research on the development of a new way to measure customer sentiment that combined both a structured and unstructured approach; this research helps organizations simplify their annual customer relationship survey.

This year was difficult for me on a personal level. My wife passed away from breast cancer this summer, and I celebrated her life by writing about her spirit and love of life.

As always, thanks for reading. The top 10 B.O.B. blog posts for 2015 were:

  1. Investigating Data Scientists, Their Skills and Team Makeup: This post was, by far, the most popular post of the year. I studied the structure and practice of data science by asking data professionals about their team, what they do and what they know.
  2. statistical_distributionStatistics: Is this Big Data's Biggest Hurdle: Getting value from data not only requires data professionals to analyze the data, it also requires data users to correctly interpret and apply those those results. Collectively, we as a species would be better off if everybody learned the basics of statistics and statistical thinking.
  3. Data Scientists and the Practice of Data Science: I think of data science more like a discipline than a single profession. There are different types of data professionals, each bringing unique skills (i.e., Subject Matter Expertise, Technology/Programming and Math/Statistics) to a team that applies principles of data science to their data.
  4. The scientific method is a way to get insights from your data Getting Insights Using Data Science Skills and the Scientific Method: Science is a way of thinking more than it is a body of knowledge. The scientific method is a way of using data to solve a problem, answer a question, test a hypothesis. The scientific method minimizes human bias and leads to evidence-based conclusions to help you improve how you make decisions and optimize algorithms.
  5. TheresaCaboDinnerTheresa Allman: A Celebration of Life: I lost the love of my life this year. I have never loved a woman so deeply and easily. She inspired me. She inspired everybody she met. She'll inspire you.
  6. 6 Customer Experience Practices of Loyalty Leaders:  By comparing loyalty leaders and loyalty laggards, I identified six practices you need to adopt if you want an effective CEM program.
  7. Development of the Customer Sentiment Index: Measuring Customers' Attitudes: I present a new method of measuring customer attitudes in your annual customer survey without using rating scales.
  8. Development of the Customer Sentiment Index: Reliability, Validity and Usefulness: I present evidence that the Customer Sentiment Index is really measuring what we think it's measuring.
  9. Making Sense of our Big Data World: Statistics for the 99%: Data are everywhere. Everyone needs a grasp of basic statistics and statistical thinking to maneuver in this Big Data world. I started the process of providing material you can use to learn about statistics.
  10. Development of the Customer Sentiment Index: Lexical Differences: Developing a new measuring instrument is a scientific endeavor. In this post, I compare different sentiment lexicons to help develop the final algorithms we will use for the index.

In the upcoming year, I will continue my research in the area of data science and how organizations can best leverage their data. Also, I will continue studying and refining the Customer Sentiment Index for practical purposes.

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