4 ms·

Here's the problem with stuff like this. If you actually learn all the stuff that is listed it will take you at least 3 years if not more. All the subjects list

Here's the problem with stuff like this. If you actually learn all the stuff that is listed it will take you at least 3 years if not more. All the subjects listed require prolonged study if you want them to become part of your intellectual repertoire and then it takes some more time until you are comfortable applying that knowledge in non-trivial settings. On top of all that you have to stay motivated to consistently study and make progress alongside whatever else you are doing for your day job. I program every day and I'm been studying parsers, virtual machines, and compilers for some time now and it still requires hard work to not fall back on bad habits. It takes even more effort to consistently apply techniques I learned while studying those subjects in day to day programming. There is no royal road.

datashaman 12y ago
Doing something is more productive than doing nothing, regardless of how easy or hard it is.
- dkarapetyan 12y ago
  I'm not discouraging you from taking action. My point was the current document is not beginner friendly if that is the intent because it is much too exhaustive for a beginner and is more likely to discourage than encourage them to continue as soon as they hit the first roadblock.
  - clarecorthell 12y ago
    Deterrent roadblocks are a huge problem. In fact, it's the primary reason education can't happen solely through a book or video lecture series. Students need mentorship to gain motivation and remove roadblocks. Teachers, TAs, and cohorts fill this need in the university setting. It makes sense that when you learn something new, you face more unknown unknowns than known unknowns. It's nearly impossible to ask questions about the former because you don't know how to begin formulating a question, while the latter is googleable and likely solvable. The programmers' standard roadblock remover? google -> "stackoverflow" + problem
clarecorthell 12y ago
It's worthwhile to explain why many people find the OSDSM useful. The topics listed do indeed require depth to acquire an adequate proficiency to understand and command them. The OSDSM is most useful for people who don't know where to start, which is often the hardest part. The hardest things are often the most trivial in hindsight. Typical interaction: Q: I want to be a Data Scientist. What do you think I should study first? A: Build a basic proficiency in linear algebra, programming in python, and statistics. Then take cursory classes in the subjects in the OSDSM that interest you. You'll figure out what depth is most meaningful to you from there. Q: What does a Data Scientist actually need to know? A: Totally and completely depends on what you want to do. There are people who crunch click logs all day, people who comb the tendrils of search algorithms, and yet others who seek terrorists and criminals in statistical signs among the bits and bytes. See my last answer for relevant guidance. e.g. https://medium.com/@clarecorthell/the-brief-multi-tweet-enumeration-of-job-titles-encompassed-by-data-science-8d48a988e3a1 https://medium.com/@clarecorthell/the-brief-multi-tweet-enum...
- dkarapetyan 12y ago
  In that case I have some feedback. As a beginner I still don't know where to start after reading your post. There are 101* bullet points in your post distributed among IDEs, books, online courses, libraries (the programming kind not the book kind), programming languages, pure math, applied math, database theory, machine learning, natural language processing, visualization, etc. and there is no obvious ordering on how hard or easy things are because the very first link in the math section is another list of bullets tackling some heavy-duty mathematics and I say this as someone who studied mathematics at a graduate school level. Expanding mailshanx's answer will be much more helpful to beginners than the current 101 bullet points. *I just counted the bullet points with document.querySelectorAll('li') so there are some false positives.
  - clarecorthell 12y ago
    Start here: http://bit.ly/uwintrodatascience http://bit.ly/uwintrodatascience. It's the first bullet.

datashaman 12y ago

Doing something is more productive than doing nothing, regardless of how easy or hard it is.

dkarapetyan 12y ago
I'm not discouraging you from taking action. My point was the current document is not beginner friendly if that is the intent because it is much too exhaustive for a beginner and is more likely to discourage than encourage them to continue as soon as they hit the first roadblock.
- clarecorthell 12y ago
  Deterrent roadblocks are a huge problem. In fact, it's the primary reason education can't happen solely through a book or video lecture series. Students need mentorship to gain motivation and remove roadblocks. Teachers, TAs, and cohorts fill this need in the university setting. It makes sense that when you learn something new, you face more unknown unknowns than known unknowns. It's nearly impossible to ask questions about the former because you don't know how to begin formulating a question, while the latter is googleable and likely solvable. The programmers' standard roadblock remover? google -> "stackoverflow" + problem

clarecorthell 12y ago

It's worthwhile to explain why many people find the OSDSM useful. The topics listed do indeed require depth to acquire an adequate proficiency to understand and command them. The OSDSM is most useful for people who don't know where to start, which is often the hardest part. The hardest things are often the most trivial in hindsight. Typical interaction: Q: I want to be a Data Scientist. What do you think I should study first? A: Build a basic proficiency in linear algebra, programming in python, and statistics. Then take cursory classes in the subjects in the OSDSM that interest you. You'll figure out what depth is most meaningful to you from there. Q: What does a Data Scientist actually need to know? A: Totally and completely depends on what you want to do. There are people who crunch click logs all day, people who comb the tendrils of search algorithms, and yet others who seek terrorists and criminals in statistical signs among the bits and bytes. See my last answer for relevant guidance. e.g. https://medium.com/@clarecorthell/the-brief-multi-tweet-enumeration-of-job-titles-encompassed-by-data-science-8d48a988e3a1 https://medium.com/@clarecorthell/the-brief-multi-tweet-enum...

dkarapetyan 12y ago
In that case I have some feedback. As a beginner I still don't know where to start after reading your post. There are 101* bullet points in your post distributed among IDEs, books, online courses, libraries (the programming kind not the book kind), programming languages, pure math, applied math, database theory, machine learning, natural language processing, visualization, etc. and there is no obvious ordering on how hard or easy things are because the very first link in the math section is another list of bullets tackling some heavy-duty mathematics and I say this as someone who studied mathematics at a graduate school level. Expanding mailshanx's answer will be much more helpful to beginners than the current 101 bullet points. *I just counted the bullet points with document.querySelectorAll('li') so there are some false positives.
- clarecorthell 12y ago
  Start here: http://bit.ly/uwintrodatascience http://bit.ly/uwintrodatascience. It's the first bullet.