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This is a very good read, but I have to say that I managed to learn all of this 12 years ago before the term "Data Science" existed. It was quite easy as I was

by n00b101 11y ago

This is a very good read, but I have to say that I managed to learn all of this 12 years ago before the term "Data Science" existed. It was quite easy as I was a statistics, comp sci and applied math "triple major." I don't remember ever hearing the terms "Data Science" or "Big Data," but I do recall taking Department of Statistics courses with titles like "Data Mining," "Statistical & Machine Learning," and "Statistical Computing." We even sort of worked with what is now called "Big Data," by learning how to run large calculations in parallel using R's cluster computing packages. As fancy and interesting as those courses were, I would only have a superficial understanding had I not also been exposed to more foundational/theoretical courses/topics like "Probability," "Measure Theory," "Mathematical Statistics," "Linear Regression," "Time Series Analysis," "Applied Stochastic Processes," etc. When it comes to real-life practical implementation of all these ideas, it is necessary to have a pretty steep background in computer science. It's not enough to be able to do a couple runs in R or Hadoop. What is really called for here is at least an undergraduate level of knowledge in all the traditional areas of computer science, like programming languages, databases, computer systems, algorithms & data structures, etc. Finally, the fourth ingredient is experience. The only way to really learn data analysis is through practice. It takes hundreds of hours of staring at data and code, struggling hard to find the relevant patterns in your data and improve the predictive performance of models. I guess this is the main advantage of pursuing a modern "Data Science" graduate degree, presumably you will spend a lot of time practicing data analysis on "real" data. What I think irks people about the Data Science trend is that there seem to be a lot of people out there saying that you don't really need to be educated in mathematics and statistics. It's like saying that a mechanical engineer just has to know how to use 3D modeling computer software and doesn't need to know physics or mathematics ... that would lead to disastrous outcomes.

vdnkh 11y ago
>It's like saying that a mechanical engineer just has to know how to use 3D modeling computer software and doesn't need to know physics or mathematics ... that would lead to disastrous outcomes. It's also like saying a Software Engineer just needs to know how to "code" and doesn't need to know math or physics. The cornerstone of any Engineering profession is deep knowledge of underlying science and mathematics and their applications to your specific discipline. A paper similar to this offering commentary on the current trend of conflating programming with Software Engineering is sorely needed.
thinkmoore 11y ago
I think you've nailed it precisely. What is irksome (scary?) isn't the sudden upsurge in interest in data analysis (great! lets for science!), but the complete lack of acknowledgement of the importance of statistical theory and knowledge. As the paper mentions, almost all of the new "Data Science" academic programs being created have very little communication with, let alone integration with, real statisticians. Garbage in-garbage out, no matter how much data or how fancy a program.
- east2west 11y ago
  I submit computer science departments have enough expertise to operate a graduate data science program. Maybe not every CS department, but enough. It is interesting that the actual stats departments do not encompass all statistical knowledge and expertise. More specialized experts in econ, finance, many fields of social science, not to speak of all engineering fields and computer science and applied math. Data science predates modern statistics; Laplace used least-square to ignore "the God hypothesis;" the function-approximation view taken in many engineering fields is just as powerful as the statistical approach.

by n00b101 11y ago

vdnkh 11y ago

>It's like saying that a mechanical engineer just has to know how to use 3D modeling computer software and doesn't need to know physics or mathematics ... that would lead to disastrous outcomes. It's also like saying a Software Engineer just needs to know how to "code" and doesn't need to know math or physics. The cornerstone of any Engineering profession is deep knowledge of underlying science and mathematics and their applications to your specific discipline. A paper similar to this offering commentary on the current trend of conflating programming with Software Engineering is sorely needed.

thinkmoore 11y ago

I think you've nailed it precisely. What is irksome (scary?) isn't the sudden upsurge in interest in data analysis (great! lets for science!), but the complete lack of acknowledgement of the importance of statistical theory and knowledge. As the paper mentions, almost all of the new "Data Science" academic programs being created have very little communication with, let alone integration with, real statisticians. Garbage in-garbage out, no matter how much data or how fancy a program.

east2west 11y ago
I submit computer science departments have enough expertise to operate a graduate data science program. Maybe not every CS department, but enough. It is interesting that the actual stats departments do not encompass all statistical knowledge and expertise. More specialized experts in econ, finance, many fields of social science, not to speak of all engineering fields and computer science and applied math. Data science predates modern statistics; Laplace used least-square to ignore "the God hypothesis;" the function-approximation view taken in many engineering fields is just as powerful as the statistical approach.