A second and final post on Alex Edmans’ May Contain Lies, looks at some advice he gives and highlights some of his useful stories. The key thing the general reader might want to take from the book is that claims such as ‘science shows’ are nonsense. Remember, research shows not to believe claims that science…
Category: Data Science
Getting Beyond Text In Unstructured Data
Unstructured data has massive potential in marketing that is already somewhat being fulfilled. Text analysis has proved a fruitful resource. Using text mining, marketers can seek to uncover messages hidden in massive amounts of words. A typical usage would be attempting to uncover the themes in online reviews written about a product. Yet, unstructured data…
Explaining Clustering Simply Has Real Value
I was impressed by Annalyn Ng’s and Kenneth Soo’s short book Numsense. I have already discussed it in a prior post, see here. Today I will note how they discuss clustering. This is central to a lot of marketing analyses. Numsense Covers A Lot Of Basic Data Science The subtitle Data Science for the Layman…
Comparing Text Classification Methods
Marketing research, especially academic research, now assesses a lot of unstructured text data. (Unstructured data is that which does not come in neat database/spreadsheet form of rows and columns). Classifying such text is a task that computers excel at. So, how do we go about comparing text classification methods to find which one best fits…
In Defense Of Robots And Their Judgments
A second post on the book, Noise, by Kahneman, Sibony, and Sunstein. For the first see here. The authors are experts in human judgment and they have a few useful comments in defense of robots and their judgments. Arguments Against, And For, Robots Noise is a book about the problems of variability in human judgment….
Understanding Data Analytics, And ‘Competitive Advantage’
Anil Maheshwari’s Data Analytics Made Accessible is a helpful book. Schools use it as a textbook and it has that feel. There is a lot of information there in a somewhat ‘just the facts’ sort of format. It should help with understanding data analytics. Useful Information To Aid In Understanding Data Analytics The book is…
Advice On Data Science, It Isn’t Too Hard to Understand
Marketers should understand the data science models that are increasingly being used in the discipline. We really need good advice on data science. As such it is helpful to find simple explanations of data science models. Numsence by Annalyn Ng and Kenneth Soo is an admirable attempt to clarify basic models used in data science….
Creating Stories With Data Visualizations
Cole Nussbaumer Knaflic has a useful book — Storytelling with Data. This contains lots of good advice on Creating Stories With Data Visualizations and generally improving data visualization. She tries to ensure the reader does not lazily follow the first thing a software (e.g., Excel) recommends. This is important, she gives many examples in the…
Showing A Problem Does Not Equal Demonstrating A Worsening Problem
Cathy O’Neil has a great book on big data, Weapons of Math Destruction, but one with a fundamental flaw. The flawed claim is made in the book’s subtitle and permeates throughout the book. The subtitle is: “How Big Data Increases Inequality and Threatens Democracy”. I could find no significant evidence of big data increasing inequality in the book. She shows…
How Sexy is Working With Big Data?
I think that academics should share their opinions widely. Some academics may believe that they have no opinions, they just relate what the data says. This is might be true for extremely empirical scholars, those who typically see themselves working with big data. Such scholars are kidding themselves. We must be willing to change with data but our experience helps…
