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same here. i work great in teams, masters in computer science, do really well in kaggle competitions, I understand when to use the algorithms, how they work, e

same here. i work great in teams, masters in computer science, do really well in kaggle competitions, I understand when to use the algorithms, how they work, etc. but in the very few ds interviews I had, I was tanked as soon I got asked questions like: whats the formula for a T test. I know what the test is, and when its not appropriate to apply it, but I don't memorize those kinds of formulas. the field is just too big to do that

crescentfresh 9y ago
Thanks for sharing your experience! Were you making it past the initial stage/screen interviews where every question is a buzz-word test? Were you ever interviewed by people who you'd actually work with and know what their team needs? What geographical area do you reside roughly, if I may ask?
- autokad 9y ago
  it was initial screenings, she was a data scientist (but really a statistician). I passed the first two screenings (code interview, database / sql interview). every question was about statistics, and probability. This position was in NYC.
em500 9y ago
I'm sorry but a standard t-test is like the most basic thing in statistics after means and variances. It's not like they want you prove the CLT or something. To me it's more like a statistics fizz-buzz.
- steve_gh 9y ago
  To which, of course, the correct answer is "Unless you have good reason to believe the data is normally distributed, you should be using Mann-Whitney", then explain that :-)
  - neuromantik8086 9y ago
    Excuse me, I think that at this juncture a Kruskal-Wallis would be more appropriate (assuming multiple distributions).
  - thousandautumns 9y ago
    Thats not really true though. The vast majority of t-tests are done using a sample average as a test statistic, and by the CLT, as the sample size goes up, the distribution of a sample average becomes approximately normal. So unless you have a really weak sample size, the t-test assumption holds even in the absence of normally distributed data.
- deleted 9y ago
  [deleted]
rockinghigh 9y ago
For many companies, data science means statistics. Statistics are used for experiments and to analyze data. If you don't know what a t-test is, you cannot be hired as a statistician. However, you may be hired as a machine learning engineer.
alergico 9y ago
Try looking for a company that does take-home assignments (these are mostly Kaggle-type problems). Then you don't need to learn formulas by rote, and the interview can focus on the assignment you did. Experimental design (statistics) is important, but data science is a team sport. The people specialized in statistics may not be able to code up a deep net, and vice versa. Computer scientists make great data scientists, especially if you know data infra. If I were to start a team from scratch, I'd focus on software engineers and physicists. Most are willing to pick up some ML to broaden their skills, and the ability to go end-to-end from conception to production is invaluable.

crescentfresh 9y ago

Thanks for sharing your experience! Were you making it past the initial stage/screen interviews where every question is a buzz-word test? Were you ever interviewed by people who you'd actually work with and know what their team needs? What geographical area do you reside roughly, if I may ask?

autokad 9y ago
it was initial screenings, she was a data scientist (but really a statistician). I passed the first two screenings (code interview, database / sql interview). every question was about statistics, and probability. This position was in NYC.

em500 9y ago

I'm sorry but a standard t-test is like the most basic thing in statistics after means and variances. It's not like they want you prove the CLT or something. To me it's more like a statistics fizz-buzz.

steve_gh 9y ago
To which, of course, the correct answer is "Unless you have good reason to believe the data is normally distributed, you should be using Mann-Whitney", then explain that :-)
- neuromantik8086 9y ago
  Excuse me, I think that at this juncture a Kruskal-Wallis would be more appropriate (assuming multiple distributions).
- thousandautumns 9y ago
  Thats not really true though. The vast majority of t-tests are done using a sample average as a test statistic, and by the CLT, as the sample size goes up, the distribution of a sample average becomes approximately normal. So unless you have a really weak sample size, the t-test assumption holds even in the absence of normally distributed data.
deleted 9y ago
[deleted]

rockinghigh 9y ago

For many companies, data science means statistics. Statistics are used for experiments and to analyze data. If you don't know what a t-test is, you cannot be hired as a statistician. However, you may be hired as a machine learning engineer.

alergico 9y ago

Try looking for a company that does take-home assignments (these are mostly Kaggle-type problems). Then you don't need to learn formulas by rote, and the interview can focus on the assignment you did. Experimental design (statistics) is important, but data science is a team sport. The people specialized in statistics may not be able to code up a deep net, and vice versa. Computer scientists make great data scientists, especially if you know data infra. If I were to start a team from scratch, I'd focus on software engineers and physicists. Most are willing to pick up some ML to broaden their skills, and the ability to go end-to-end from conception to production is invaluable.