5 ms·

The Open Source Data Science Masters

95 points by nns 10y ago

randcraw 10y ago
That's a nice overview of autodidact resources for DS. But I suggest that you tweak the name a little, like "The Open DS Masters Program" or "Toward OSDS Mastery". "OSDS Masters" sounds like a plural noun, like you're trying to say, "at this website you can find the great open source masters of data science" -- like Richard Stallmann or the authors of Weka. It's a bit confusing.
- cholantesh 10y ago
  I presumed the 'Masters' was akin to the usage in "master's degree'
  - j_s 10y ago
    If so then they should add the word 'Degree' everywhere.
    - dragonwriter 10y ago
      Or, just eliminate the subtitle and merge the one important word in the subtitle that isn't in the subtitle, and call it "The Open Source Data Science Master's Curriculum", which has the advantage that while it invokes the idea of a curriculum of the level of a Master's Degree, doesn't falsely present itself as an offering an actual degree, which it is not.
      - cholantesh 10y ago
        I don't think that's the intent, but I can see how it would give such an impression. That alone should be reason to change the title, though.
Rogerh91 10y ago
I really like this collection of resources--it's perfect for people really trying to get into the basics of data science.
jmde 10y ago
This seems like a nice compilation for introductory material in one place. I still can't get over the term "data science", though. Not only is it ridiculously meaningless - what sort of science doesn't involve data, and how often would data be useful to something that isn't scientific at some level - its meaninglessness derives from the hyped buzzword trendiness that drove its upswing. I say this as someone whose expertise is really sitting at the nexus of what would be considered data science. I feel as if I have been doing what might be considered data science for a long time, before there was a label for it, but watching its ascendance in demand and popularity has been troubling. I should be happy, but I feel like it's being driven by fashion rather than fundamentals, which makes me worried about the trajectory going forward, and disturbed by some communities being thrown under the bus.
- denzil_correa 10y ago
  > what sort of science doesn't involve data There are many like theoretical computer science which do not involve data. > I say this as someone whose expertise is really sitting at the nexus of what would be considered data science. I feel as if I have been doing what might be considered data science for a long time, before there was a label for it, but watching its ascendance in demand and popularity has been troubling. I should be happy, but I feel like it's being driven by fashion rather than fundamentals, which makes me worried about the trajectory going forward, and disturbed by some communities being thrown under the bus. There will be a time where things will consolidate. During this time, people who really do data science will be stuck with while people who just have it as a title for the sake of it would face problems.
  - cholantesh 10y ago
    >> what sort of science doesn't involve data >There are many like theoretical computer science which do not involve data. That computer science is a 'science' is also pretty contentious! :)
    - ende 10y ago
      Much of computer science is a science. The contention seems to comes from the fact that software engineers tend to come from computer science departments. Maybe more universities should create separate software engineering departments?
Notre1 10y ago
Clare Corthell, the creator of the Open Source Data Science Masters project, is interviewed in the 2016-07-30 episode of This Week in Machine Learning & AI (TWiML): https://twimlai.com/twiml-talk-1-clare-corthell-open-source-data-science-masters-hybrid-ai-algorithmic-ethics/ https://twimlai.com/twiml-talk-1-clare-corthell-open-source-...
neilsharma 10y ago
Good collection, but as someone who has been slowly learning data science over the past few years, I think it needs far fewer lectures and waaaay more projects. The biggest difficulty I have with learning data science is not how the algorithm or tools work, but the problem setup. Where is the data? How do I clean it? What insights can I draw from this? Which algorithms to use? What can I do with the algorithm assuming it works? Most MOOC projects decide all this for you by giving you a set of tasks to do in order and skeleton code to work off of. Your job is simply to implement a small part of whatever algorithm you learned that week and press run. This way lacks creative development, exploration, trial and error, and critical thinking skills necessary when you go out in the real world. Also, I think there should be more emphasis on publishing, even if your attempts are inaccurate. Push out a jupyter notebook to github of how you tested out a rudimentary monte carlo simulation on stock data. Or write a blog post with your attempt at determining how much silicon valley home prices will drop if 10K more family units magically existed in SF. Or try to code a random forest algorithm from scratch in a language of your choice. You don't have to be right, but publishing forces you to at least take a critical look at your work and think about the material deeply. MOOCs, at least from my experience, just encourage you to move on to the next topic the moment your code works, without diving too deeply.
- shanusmagnus 10y ago
  Agree. I'll add that while it's valuable to compile a giant list of topic areas, and references and resources for those topic areas, working through all the stuff on this list would take years to do in a non-trivial way. What I really prize are curricula that err in the other direction; something like: here are the handful of foundational topic areas you _have_ to know about, and the pieces of those areas that will give you the absolute minimum. But taken collectively, you will then be able to make a beginning; and be able to engage with other, more advanced, resources, as the need arises, and as the sophistication of your projects require. It's hard (at least for me) to know which subset of knowledge is required to make a beginning. That's where I need help.
  - neilsharma 10y ago
    That's true -- there's definitely a bit of knowledge needed to get started, but most of that can probably be taught in 2-3 classes. I think the first problem in learning data science is coming up with a "Foundations" curriculum: - Learn a relevant programming language (R, Python) + tools (ipython, anaconda, etc) - Basic linear algebra (nothing more complex than multiplying matrices) and calculus (what are derivatives and integrals) - Intro to statistics (just to know the vocabulary -- covariance, correlation, standard error, etc.) - Rough overview of Machine Learning / AI as a whole and where its used in the world today After that comes the second problem: "What do I do next?" - coming up with several interesting but manageably small projects - getting data for these projects - access to quality advanced resources that can be consumed as needed while working on the project. A 3-month long MOOC on Neural Networks is an impractical resource. A well-written blog post (with code) or youtube video is far better. MOOCs + textbooks seem to do better for the first problem if you can sift through all the noise, but fail at the second.

95 points by nns 10y ago

That's a nice overview of autodidact resources for DS. But I suggest that you tweak the name a little, like "The Open DS Masters Program" or "Toward OSDS Mastery". "OSDS Masters" sounds like a plural noun, like you're trying to say, "at this website you can find the great open source masters of data science" -- like Richard Stallmann or the authors of Weka. It's a bit confusing.

cholantesh 10y ago
I presumed the 'Masters' was akin to the usage in "master's degree'
- j_s 10y ago
  If so then they should add the word 'Degree' everywhere.
  - dragonwriter 10y ago
    Or, just eliminate the subtitle and merge the one important word in the subtitle that isn't in the subtitle, and call it "The Open Source Data Science Master's Curriculum", which has the advantage that while it invokes the idea of a curriculum of the level of a Master's Degree, doesn't falsely present itself as an offering an actual degree, which it is not.
    - cholantesh 10y ago
      I don't think that's the intent, but I can see how it would give such an impression. That alone should be reason to change the title, though.

Rogerh91 10y ago

I really like this collection of resources--it's perfect for people really trying to get into the basics of data science.

jmde 10y ago

This seems like a nice compilation for introductory material in one place. I still can't get over the term "data science", though. Not only is it ridiculously meaningless - what sort of science doesn't involve data, and how often would data be useful to something that isn't scientific at some level - its meaninglessness derives from the hyped buzzword trendiness that drove its upswing. I say this as someone whose expertise is really sitting at the nexus of what would be considered data science. I feel as if I have been doing what might be considered data science for a long time, before there was a label for it, but watching its ascendance in demand and popularity has been troubling. I should be happy, but I feel like it's being driven by fashion rather than fundamentals, which makes me worried about the trajectory going forward, and disturbed by some communities being thrown under the bus.

denzil_correa 10y ago
> what sort of science doesn't involve data There are many like theoretical computer science which do not involve data. > I say this as someone whose expertise is really sitting at the nexus of what would be considered data science. I feel as if I have been doing what might be considered data science for a long time, before there was a label for it, but watching its ascendance in demand and popularity has been troubling. I should be happy, but I feel like it's being driven by fashion rather than fundamentals, which makes me worried about the trajectory going forward, and disturbed by some communities being thrown under the bus. There will be a time where things will consolidate. During this time, people who really do data science will be stuck with while people who just have it as a title for the sake of it would face problems.
- cholantesh 10y ago
  >> what sort of science doesn't involve data >There are many like theoretical computer science which do not involve data. That computer science is a 'science' is also pretty contentious! :)
  - ende 10y ago
    Much of computer science is a science. The contention seems to comes from the fact that software engineers tend to come from computer science departments. Maybe more universities should create separate software engineering departments?

Notre1 10y ago

Clare Corthell, the creator of the Open Source Data Science Masters project, is interviewed in the 2016-07-30 episode of This Week in Machine Learning & AI (TWiML): https://twimlai.com/twiml-talk-1-clare-corthell-open-source-data-science-masters-hybrid-ai-algorithmic-ethics/ https://twimlai.com/twiml-talk-1-clare-corthell-open-source-...

neilsharma 10y ago

Good collection, but as someone who has been slowly learning data science over the past few years, I think it needs far fewer lectures and waaaay more projects. The biggest difficulty I have with learning data science is not how the algorithm or tools work, but the problem setup. Where is the data? How do I clean it? What insights can I draw from this? Which algorithms to use? What can I do with the algorithm assuming it works? Most MOOC projects decide all this for you by giving you a set of tasks to do in order and skeleton code to work off of. Your job is simply to implement a small part of whatever algorithm you learned that week and press run. This way lacks creative development, exploration, trial and error, and critical thinking skills necessary when you go out in the real world. Also, I think there should be more emphasis on publishing, even if your attempts are inaccurate. Push out a jupyter notebook to github of how you tested out a rudimentary monte carlo simulation on stock data. Or write a blog post with your attempt at determining how much silicon valley home prices will drop if 10K more family units magically existed in SF. Or try to code a random forest algorithm from scratch in a language of your choice. You don't have to be right, but publishing forces you to at least take a critical look at your work and think about the material deeply. MOOCs, at least from my experience, just encourage you to move on to the next topic the moment your code works, without diving too deeply.

shanusmagnus 10y ago
Agree. I'll add that while it's valuable to compile a giant list of topic areas, and references and resources for those topic areas, working through all the stuff on this list would take years to do in a non-trivial way. What I really prize are curricula that err in the other direction; something like: here are the handful of foundational topic areas you _have_ to know about, and the pieces of those areas that will give you the absolute minimum. But taken collectively, you will then be able to make a beginning; and be able to engage with other, more advanced, resources, as the need arises, and as the sophistication of your projects require. It's hard (at least for me) to know which subset of knowledge is required to make a beginning. That's where I need help.
- neilsharma 10y ago
  That's true -- there's definitely a bit of knowledge needed to get started, but most of that can probably be taught in 2-3 classes. I think the first problem in learning data science is coming up with a "Foundations" curriculum: - Learn a relevant programming language (R, Python) + tools (ipython, anaconda, etc) - Basic linear algebra (nothing more complex than multiplying matrices) and calculus (what are derivatives and integrals) - Intro to statistics (just to know the vocabulary -- covariance, correlation, standard error, etc.) - Rough overview of Machine Learning / AI as a whole and where its used in the world today After that comes the second problem: "What do I do next?" - coming up with several interesting but manageably small projects - getting data for these projects - access to quality advanced resources that can be consumed as needed while working on the project. A 3-month long MOOC on Neural Networks is an impractical resource. A well-written blog post (with code) or youtube video is far better. MOOCs + textbooks seem to do better for the first problem if you can sift through all the noise, but fail at the second.