These articles were controversial in the sense that they highlighted the differences between data science and other disciplines, at a time when many believed that data science was just old stuff being re-branded, or being practiced by people knowing nothing about statistics. Ironically, some of the old stuff actually re-branded itself as data science, not the other way around.
Analytics practitioners and users grew by a factor 5 over the last three years, faster than they can be properly trained, despite the numerous programs available for free, including ours (for self-learners only). Thus many are not equipped with the proper training. This created an opportunity to develop efficient, simple methods that could be understood and properly used by the layman, and even by robots, to process modern, big data. Unfortunately, this aspect of data science is considered by many, even today, to not be part of the core data science framework: it has created much of the controversy, mostly around the concept of automated data science, automated machine learning, or automated statistical science, including the introduction of new powerful algorithms such as automated indexation – a very fast clustering algorithm for big, unstructured text data – to create large taxonomies, by companies such as Amazon or Google.
Here is my selection of controversial articles:
- 10 types of regressions. Which one to use?
- 10 Modern Statistical Concepts Discovered by Data Scientists
- The 8 worst predictive modeling techniques
- Data scientist paid $500k can barely code!
- Data science without statistics is possible, even desirable
- Foundations of Statistical Theory Being Questioned
- Vertical vs. Horizontal Data Scientists
- Data Science: The End of Statistics?
- The death of the statistician
- Three myths about data scientists and big data
- High versus low-level data science
- Data Science Has Been Using Rebel Statistics for a Long Time
- Example of Bad Data Science: Test of Hypothesis
- Biased vs Unbiased: Debunking Statistical Myths
- The Death of the Statistical Tests of Hypotheses
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Additional Reading
- What statisticians think about data scientists
- Data Science Compared to 16 Analytic Disciplines
- 10 types of data scientists
- 91 job interview questions for data scientists
- 50 Questions to Test True Data Science Knowledge
- 24 Uses of Statistical Modeling
- 21 data science systems used by Amazon to operate its business
- Top 20 Big Data Experts to Follow (Includes Scoring Algorithm)
- 5 Data Science Leaders Share their Predictions for 2016 and Beyond
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 22 tips for better data science
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- High versus low-level data science
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