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What exactly is Data Science? It seems like such an overused term and the value of the subject really gets diluted for me when I see charts in Tableau being off

What exactly is Data Science? It seems like such an overused term and the value of the subject really gets diluted for me when I see charts in Tableau being offered as examples of "data science". What's the difference between, say, a Master's program in Computer Science where one studies machine learning and a Master's program in Data Science? Am I wrong for thinking the Data Science program weaker?

itronitron 8y ago
The CS program should focus more on data structures and algorithms (and possibly UX and good ol' software dev as well) and the DS program should focus more on statistical/analytical methods and their particular nuances and limitations. If the DS program is done well, with a lot of stats classes, then it is not a weaker program.
- chibg10 8y ago
  Honestly, with the current state of stats teaching, they might be better off just avoiding the stats classes in most cases. I don't need yet another drone that tries to convince me I should make a business decision because of "statistical significance" but can't explain why I should care about statistical significance (p.s. I shouldn't).
gaius 8y ago
What exactly is Data Science? Data Science and DevOps are both just labels for things people have been doing under more mundane terms for 40-odd years. Even Machine Learning is just a trendy buzzword for what used to be called Predictive Statistics.
- nl 8y ago
  I did stats before data science was a thing, and then ran a data science team afterwards, and it's dramatically different. I've never seen any stats text book or course discuss techniques for dealing with large amounts of data to any significant level, but in data science that is a core part of what you do. I ran production systems before DevOps and after. Again, it's very different - prior to devops, there was no emphasis at all about using software engineering techniques to manage and deploy software. The most you'd get was some scripts maybe kept in source control if you were lucky. Now I run an AI company, and a key part of the ML we use involves generating structured text files from images. I guess predictive statistics is technically a correct label, but the tools and techniques are so dramatically different that that thinking of them as separate fields is more correct than incorrect.
  - gaius 8y ago
    I struggled for years to understand DevOps because I couldn’t see what was different from how I worked already... the answer was nothing :-)
    - fjsolwmv 8y ago
      DevOps gave a name to what your were doing, that other sysadmins and product devs did not. Is that bad?

itronitron 8y ago

The CS program should focus more on data structures and algorithms (and possibly UX and good ol' software dev as well) and the DS program should focus more on statistical/analytical methods and their particular nuances and limitations. If the DS program is done well, with a lot of stats classes, then it is not a weaker program.

chibg10 8y ago
Honestly, with the current state of stats teaching, they might be better off just avoiding the stats classes in most cases. I don't need yet another drone that tries to convince me I should make a business decision because of "statistical significance" but can't explain why I should care about statistical significance (p.s. I shouldn't).

gaius 8y ago

What exactly is Data Science? Data Science and DevOps are both just labels for things people have been doing under more mundane terms for 40-odd years. Even Machine Learning is just a trendy buzzword for what used to be called Predictive Statistics.

nl 8y ago
I did stats before data science was a thing, and then ran a data science team afterwards, and it's dramatically different. I've never seen any stats text book or course discuss techniques for dealing with large amounts of data to any significant level, but in data science that is a core part of what you do. I ran production systems before DevOps and after. Again, it's very different - prior to devops, there was no emphasis at all about using software engineering techniques to manage and deploy software. The most you'd get was some scripts maybe kept in source control if you were lucky. Now I run an AI company, and a key part of the ML we use involves generating structured text files from images. I guess predictive statistics is technically a correct label, but the tools and techniques are so dramatically different that that thinking of them as separate fields is more correct than incorrect.
- gaius 8y ago
  I struggled for years to understand DevOps because I couldn’t see what was different from how I worked already... the answer was nothing :-)
  - fjsolwmv 8y ago
    DevOps gave a name to what your were doing, that other sysadmins and product devs did not. Is that bad?