9 ms·

The Open Source Data Science Masters

132 points by hooande 12y ago

afdfdfdfd 12y ago
Is there something similar but for a CS degree?
- deleted 12y ago
  [deleted]
- rkda 12y ago
  You can check out Bottom Up CS http://www.bottomupcs.com/ http://www.bottomupcs.com/
- clarecorthell 12y ago
  Try CodeAcademy and CS106a (how I learned programming at Stanford) https://www.udemy.com/cs-106a-programming-methodology https://www.udemy.com/cs-106a-programming-methodology I've started cataloging some of the best beginning resources here. Main benefit is that people can battle out what the best resources are in PRs. https://github.com/datasciencemasters/go/blob/master/basic-programming.md https://github.com/datasciencemasters/go/blob/master/basic-p...
dkarapetyan 12y ago
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...
akbar501 12y ago
The list of Python libs is very helpful. Thanks for putting this together.
huherto 12y ago
The field of Data Science is so wide and there are many techniques. How do you decide on what to specialize since you don't know which techniques are going to be needed to solve your next problem?
- clarecorthell 12y ago
  The truthful meta-answer which you probably don't want to hear: We only have a statistical likelihood (of dubious confidence) of what will transpire in the future. We have no knowledge of the future. None. Same is true of your knowledge of your future problems. Use some basic bayesian logic and guess in an educated manner, like with most prediction problems. More practical advices: Find out what problems you're interested in working on, then google, read books, engage people working on those problems to tell you more about how they solve them. Another great place to start is to look at your business' biggest inefficiencies and informational gaps, and determine which of them could potentially be addressed with prediction or statistical inference. This used to be called Business Intelligence. Even a coffee shop can benefit from understanding simple seasonality.
dantiberian 12y ago
Here's my personal list for those interested, inspired by the Data Science Masters: http://danielcompton.net/2014/03/23/data-science-curriculum http://danielcompton.net/2014/03/23/data-science-curriculum
misiti3780 12y ago
no ggplot under visualization - what's up with that ?
- rkda 12y ago
  It's open source. Time for a pull request? ;-) https://github.com/datasciencemasters/go https://github.com/datasciencemasters/go
- clarecorthell 12y ago
  You'll find R resources here: https://github.com/datasciencemasters/go/blob/master/r-resources.md https://github.com/datasciencemasters/go/blob/master/r-resou... The OSDSM maintains a focus on python resources.
mailshanx 12y ago
Like many people here have mentioned, mastering everything can take far too long, and is completely unnecessary in practice. It is much, much more important to master the foundations, which consist of: 1) Probability and statistics 2) Linear algebra, and 3) Programming proficiency To be more precise here is what you would need to know in each area: 1) Probability and stats: Properties of the most common distributions (Normal, Poisson, Exponential), conditional probability, properties of expectation, Bayes rule and its applications, hypothesis testing, confidence intervals, bootstrap / jackknife, know how to compute the power of a test, be able to run Monte Carlo simulations to model real world phenomena. 2. Linear Algebra: Know enough to understand and compute Eigenvalues / Eigenvectors, SVD and various matrix factorizations. 3. Programming proficiency: Algorithms and design patterns. Its enough to know the typical undergraduate algorithms course content: trees, heaps, hash tables, various sorting algorithms, graph algorithms: shortest paths, spanning trees, max flows. If you know this, you can easily pickup everything else on a need-to-know basis. Depending on the domain you are working in, that might entail either natural language processing, computer vision, machine learning or big-data tools like Hadoop / Spark etc. All of that is very easy to learn once you have the foundations.
- geebee 12y ago
  These are good suggestions, and you're presenting a good guide for people who need to fill in some gaps prior to "grad school". But to me, this sounds like the standard (in the US) undergraduate curriculum for the general STEM majors - Math, Physics, Operations Research, CS, various branches of engineering. Typically, a student would have done what you're describing prior to grad school. A Masters degree is typically an opportunity to specialize a bit and have a little more space to pursue an interest. One suggestion I'd make is to go through the python scikit-learn library, do the examples, and research the math behind the algorithms to make sure you understand what's happening in the background (maybe even implement a bit on your own). I would agree that if you have the background you described, you'll be able to do this (though it'll take some study). http://scikit-learn.org/stable/ http://scikit-learn.org/stable/ One other thing… I'm a little less enthusiastic about learning purely on a "need-to-know" basis, because sometimes it takes knowledge of an algorithm to recognize the opportunity to apply it. I don't think you can keep all of this loaded in "exam-ready" memory (and I really wish interviewers would understand this), but I think it's important to study and read widely. You can learn (or review) the details in the moment, but you do need broad awareness to approach data with a certain frame of mind.
upquark 12y ago
Thank you for putting this together, looks like a great collection of resources.
shire 12y ago
how does someone who has a responsibility and dependents keep up with the time to learn all this stuff? time is of the essence.
- jeffreyrogers 12y ago
  If you constrain a problem enough it eventually becomes impossible. There is no royal road to learning hard things, you just have to put in the time.
- SpaceManNabs 12y ago
  It is actually not too bad. I picked up all of these things in my undergraduate studies and some side classes.
- clarecorthell 12y ago
  I took six months off for the OSDSM. I am very aware that it was a luxury to do so. I had to take loans, move out of my apartment, and I studied 10 hours per day. If you weren't working 10 hours per day, 6 days per week for six months (1440 hrs), and you took 6 hours per week to do the same, it would take you more than 4 years to finish a similar curriculum. Managing your time is still the hardest part of self-study. It always will be. Most people pay institutions to structure their lives with a workload, deadlines, time off, expectations, and consequences (positive and negative). Having the time for self-study is a luxury few people have; most people won't have the time, money, and opportunity to give up for a "classic liberal education," and by extension they won't have time for self-study, either (one such conversation on the topic: http://www.newyorker.com/culture/culture-desk/loud-nathan-heller-joshua-rothman-elite-colleges http://www.newyorker.com/culture/culture-desk/loud-nathan-he...). That's part of why new forms of education like The OSDSM are so necessary. Such curriculums fit exceptional cases that are less and less the exception.
clarecorthell 12y ago
I'm curious about the audience here - What career goal is driving your interest in learning Data Science?
- KrisAndrew 12y ago
  It's more of a response to the market. Beginning about 5 years ago I was increasingly asked to do more numerically oriented things. Prior to that I was mostly writing applications that generated SQL and wrapped the results in some HTML. Pretty boring. Data science is more compelling. Over the past 15-20 years there has been a massive amount of information piling up in databases and log files; not just from web applications but from desktop and mobile apps too. And there are companies who want to pan for gold in that data. So if you want to do something more interesting than fiddle with canvases, or CSS or MVC frameworks, then data science is fairly accessible if you're not afraid of math. Furthermore, most companies will have a need for it even if you don't really care to develop their software products directly. NB: I doubled my salary by moving into data science. Nowadays gas station attendants can write a Rails app to search a database. The bar has been lowered. Understanding stochastic gradient descent (among other things) and knowing where/when to use it commands more earning power.
  - dasboth 12y ago
    "I was mostly writing applications that generated SQL and wrapped the results in some HTML. Pretty boring." - This is basically why I'm interested in making the move (eventually). Earning power may turn out to be a perk but the main motivation is something that is perpetually stimulating.

132 points by hooande 12y ago

afdfdfdfd 12y ago

Is there something similar but for a CS degree?

deleted 12y ago
[deleted]
rkda 12y ago
You can check out Bottom Up CS http://www.bottomupcs.com/ http://www.bottomupcs.com/
clarecorthell 12y ago
Try CodeAcademy and CS106a (how I learned programming at Stanford) https://www.udemy.com/cs-106a-programming-methodology https://www.udemy.com/cs-106a-programming-methodology I've started cataloging some of the best beginning resources here. Main benefit is that people can battle out what the best resources are in PRs. https://github.com/datasciencemasters/go/blob/master/basic-programming.md https://github.com/datasciencemasters/go/blob/master/basic-p...

dkarapetyan 12y ago

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...

akbar501 12y ago

The list of Python libs is very helpful. Thanks for putting this together.

huherto 12y ago

The field of Data Science is so wide and there are many techniques. How do you decide on what to specialize since you don't know which techniques are going to be needed to solve your next problem?

clarecorthell 12y ago
The truthful meta-answer which you probably don't want to hear: We only have a statistical likelihood (of dubious confidence) of what will transpire in the future. We have no knowledge of the future. None. Same is true of your knowledge of your future problems. Use some basic bayesian logic and guess in an educated manner, like with most prediction problems. More practical advices: Find out what problems you're interested in working on, then google, read books, engage people working on those problems to tell you more about how they solve them. Another great place to start is to look at your business' biggest inefficiencies and informational gaps, and determine which of them could potentially be addressed with prediction or statistical inference. This used to be called Business Intelligence. Even a coffee shop can benefit from understanding simple seasonality.

dantiberian 12y ago

Here's my personal list for those interested, inspired by the Data Science Masters: http://danielcompton.net/2014/03/23/data-science-curriculum http://danielcompton.net/2014/03/23/data-science-curriculum

misiti3780 12y ago

no ggplot under visualization - what's up with that ?

rkda 12y ago
It's open source. Time for a pull request? ;-) https://github.com/datasciencemasters/go https://github.com/datasciencemasters/go
clarecorthell 12y ago
You'll find R resources here: https://github.com/datasciencemasters/go/blob/master/r-resources.md https://github.com/datasciencemasters/go/blob/master/r-resou... The OSDSM maintains a focus on python resources.

mailshanx 12y ago

Like many people here have mentioned, mastering everything can take far too long, and is completely unnecessary in practice. It is much, much more important to master the foundations, which consist of: 1) Probability and statistics 2) Linear algebra, and 3) Programming proficiency To be more precise here is what you would need to know in each area: 1) Probability and stats: Properties of the most common distributions (Normal, Poisson, Exponential), conditional probability, properties of expectation, Bayes rule and its applications, hypothesis testing, confidence intervals, bootstrap / jackknife, know how to compute the power of a test, be able to run Monte Carlo simulations to model real world phenomena. 2. Linear Algebra: Know enough to understand and compute Eigenvalues / Eigenvectors, SVD and various matrix factorizations. 3. Programming proficiency: Algorithms and design patterns. Its enough to know the typical undergraduate algorithms course content: trees, heaps, hash tables, various sorting algorithms, graph algorithms: shortest paths, spanning trees, max flows. If you know this, you can easily pickup everything else on a need-to-know basis. Depending on the domain you are working in, that might entail either natural language processing, computer vision, machine learning or big-data tools like Hadoop / Spark etc. All of that is very easy to learn once you have the foundations.

geebee 12y ago
These are good suggestions, and you're presenting a good guide for people who need to fill in some gaps prior to "grad school". But to me, this sounds like the standard (in the US) undergraduate curriculum for the general STEM majors - Math, Physics, Operations Research, CS, various branches of engineering. Typically, a student would have done what you're describing prior to grad school. A Masters degree is typically an opportunity to specialize a bit and have a little more space to pursue an interest. One suggestion I'd make is to go through the python scikit-learn library, do the examples, and research the math behind the algorithms to make sure you understand what's happening in the background (maybe even implement a bit on your own). I would agree that if you have the background you described, you'll be able to do this (though it'll take some study). http://scikit-learn.org/stable/ http://scikit-learn.org/stable/ One other thing… I'm a little less enthusiastic about learning purely on a "need-to-know" basis, because sometimes it takes knowledge of an algorithm to recognize the opportunity to apply it. I don't think you can keep all of this loaded in "exam-ready" memory (and I really wish interviewers would understand this), but I think it's important to study and read widely. You can learn (or review) the details in the moment, but you do need broad awareness to approach data with a certain frame of mind.

upquark 12y ago

Thank you for putting this together, looks like a great collection of resources.

shire 12y ago

how does someone who has a responsibility and dependents keep up with the time to learn all this stuff? time is of the essence.

jeffreyrogers 12y ago
If you constrain a problem enough it eventually becomes impossible. There is no royal road to learning hard things, you just have to put in the time.
SpaceManNabs 12y ago
It is actually not too bad. I picked up all of these things in my undergraduate studies and some side classes.
clarecorthell 12y ago
I took six months off for the OSDSM. I am very aware that it was a luxury to do so. I had to take loans, move out of my apartment, and I studied 10 hours per day. If you weren't working 10 hours per day, 6 days per week for six months (1440 hrs), and you took 6 hours per week to do the same, it would take you more than 4 years to finish a similar curriculum. Managing your time is still the hardest part of self-study. It always will be. Most people pay institutions to structure their lives with a workload, deadlines, time off, expectations, and consequences (positive and negative). Having the time for self-study is a luxury few people have; most people won't have the time, money, and opportunity to give up for a "classic liberal education," and by extension they won't have time for self-study, either (one such conversation on the topic: http://www.newyorker.com/culture/culture-desk/loud-nathan-heller-joshua-rothman-elite-colleges http://www.newyorker.com/culture/culture-desk/loud-nathan-he...). That's part of why new forms of education like The OSDSM are so necessary. Such curriculums fit exceptional cases that are less and less the exception.

clarecorthell 12y ago

I'm curious about the audience here - What career goal is driving your interest in learning Data Science?

KrisAndrew 12y ago
It's more of a response to the market. Beginning about 5 years ago I was increasingly asked to do more numerically oriented things. Prior to that I was mostly writing applications that generated SQL and wrapped the results in some HTML. Pretty boring. Data science is more compelling. Over the past 15-20 years there has been a massive amount of information piling up in databases and log files; not just from web applications but from desktop and mobile apps too. And there are companies who want to pan for gold in that data. So if you want to do something more interesting than fiddle with canvases, or CSS or MVC frameworks, then data science is fairly accessible if you're not afraid of math. Furthermore, most companies will have a need for it even if you don't really care to develop their software products directly. NB: I doubled my salary by moving into data science. Nowadays gas station attendants can write a Rails app to search a database. The bar has been lowered. Understanding stochastic gradient descent (among other things) and knowing where/when to use it commands more earning power.
- dasboth 12y ago
  "I was mostly writing applications that generated SQL and wrapped the results in some HTML. Pretty boring." - This is basically why I'm interested in making the move (eventually). Earning power may turn out to be a perk but the main motivation is something that is perpetually stimulating.