4 ms·

It depends what you mean by "do data science". If you've read and mastered what's in the book, you'll have a good chance at writing a nice PhD in machine learni

It depends what you mean by "do data science". If you've read and mastered what's in the book, you'll have a good chance at writing a nice PhD in machine learning. But there's a lot of academic fluff in there that isn't too useful outside of university. Most data scientists are consumers of algorithms, not producers of algorithms. The rules are a bit different if you're at a bigco, but most data scientists don't do active research. It's nice to have a solid theoretical understanding machine learning, but most data data scientists' day-to-day consists of chaining together libraries and building nice dashboards.

crimsonalucard 7y ago
By do data science, I mean the day to day stuff. So actually it's not that hard then if all they are doing is chaining together libraries. Is there anything that this book would be missing the day to day stuff?
- Rainymood 7y ago
  Communication skills, business insights, politics ... those for starters ...
  - crimsonalucard 7y ago
    I mean on the technical side. The stuff you describe are required for any job.
- soVeryTired 7y ago
  I think what's missing is how to assess whether what you've build actually does what it's meant to do. Too many textbooks (actually almost all of academia) boil evaluation down to the calculation of a handful of narrow metrics. But really understanding the failure modes of what you've made is much harder than that. Unfortunately I don't have a magic bullet or even a reference for that side of things. It's just something I've learned on the job. To paraphrase the Anna Karenina principle [0]: All good models are alike; each bad model is bad in its own way. [0] https://en.wikipedia.org/wiki/Anna_Karenina_principle https://en.wikipedia.org/wiki/Anna_Karenina_principle
- joker3 7y ago
  > By do data science, I mean the day to day stuff. So actually it's not that hard then if all they are doing is chaining together libraries. Anyone who believes this is dangerous and shouldn't be allowed anywhere near a data science project. The programming is easy, but working with data and making good inferences is very hard.
  - tekkk 7y ago
    Sounds a bit of gate keeping to me. I mean it isn't magic, it's still 0s and 1s which yield results that are of some use to someone. Sure, it would be nice if everything were masters of DS but in reality, people make mistakes and sadly, or luckily depending how you look at it, you still can get useful results even when some of your assumptions about the data are wrong. But I understand what you mean, and completely agree that poseur amateurs who treat DS as just easy "number fluff" and expect fancy ML frameworks to solve all their problems are cancerous and ruin the reputation of DS as a field. Yet the more I have worked with regular people at work, the more I have moved from the camp of always doing the "right thing" to hoping that people would just "do something". I can't fix every problem so maybe I'll just deal with the reality as it is and try to make best out of it. I won't start quizzing people at work about DS know-how but maybe then silently guide them towards understanding what they are doing instead of driving them out of the room and keeping them located somewhere far away from the data science team.
lordgrenville 7y ago
>but most data data scientists' day-to-day consists of chaining together libraries and building nice dashboards. This - though I'd add that data science is also about statistical intuition, knowing what questions to ask of the data and how to get sensible results. It's not essential to understand that PCA involves finding the eigenvectors of the covariance matrix, but it is essential to know when PCA would be useful, and common gotchas that might make it irrelevant.

crimsonalucard 7y ago

By do data science, I mean the day to day stuff. So actually it's not that hard then if all they are doing is chaining together libraries. Is there anything that this book would be missing the day to day stuff?

Rainymood 7y ago
Communication skills, business insights, politics ... those for starters ...
- crimsonalucard 7y ago
  I mean on the technical side. The stuff you describe are required for any job.
soVeryTired 7y ago
I think what's missing is how to assess whether what you've build actually does what it's meant to do. Too many textbooks (actually almost all of academia) boil evaluation down to the calculation of a handful of narrow metrics. But really understanding the failure modes of what you've made is much harder than that. Unfortunately I don't have a magic bullet or even a reference for that side of things. It's just something I've learned on the job. To paraphrase the Anna Karenina principle [0]: All good models are alike; each bad model is bad in its own way. [0] https://en.wikipedia.org/wiki/Anna_Karenina_principle https://en.wikipedia.org/wiki/Anna_Karenina_principle
joker3 7y ago
> By do data science, I mean the day to day stuff. So actually it's not that hard then if all they are doing is chaining together libraries. Anyone who believes this is dangerous and shouldn't be allowed anywhere near a data science project. The programming is easy, but working with data and making good inferences is very hard.
- tekkk 7y ago
  Sounds a bit of gate keeping to me. I mean it isn't magic, it's still 0s and 1s which yield results that are of some use to someone. Sure, it would be nice if everything were masters of DS but in reality, people make mistakes and sadly, or luckily depending how you look at it, you still can get useful results even when some of your assumptions about the data are wrong. But I understand what you mean, and completely agree that poseur amateurs who treat DS as just easy "number fluff" and expect fancy ML frameworks to solve all their problems are cancerous and ruin the reputation of DS as a field. Yet the more I have worked with regular people at work, the more I have moved from the camp of always doing the "right thing" to hoping that people would just "do something". I can't fix every problem so maybe I'll just deal with the reality as it is and try to make best out of it. I won't start quizzing people at work about DS know-how but maybe then silently guide them towards understanding what they are doing instead of driving them out of the room and keeping them located somewhere far away from the data science team.

lordgrenville 7y ago

>but most data data scientists' day-to-day consists of chaining together libraries and building nice dashboards. This - though I'd add that data science is also about statistical intuition, knowing what questions to ask of the data and how to get sensible results. It's not essential to understand that PCA involves finding the eigenvectors of the covariance matrix, but it is essential to know when PCA would be useful, and common gotchas that might make it irrelevant.