14 ms·

How to learn data science

248 points by spYa 11y ago

neovive 11y ago
"You need something that will motivate you to keep learning." This is so true and often forgotten. I am always learning new things, but the concepts that stick, beyond just the basics, are tied to specific projects or solutions to real problems. I'm typically ok with being a "jack-of-all-trades" for most technologies, just to stay aware of new things. However, when it comes to applying new concepts, skills, or tech to solve problems, a deeper understanding is required; usually obtained through motivation.
- liviu- 11y ago
  >"You need something that will motivate you to keep learning." This is so true and often forgotten. I'm surprised that both you and OP seem to think this advice is rare, as I've personally seen it mentioned more than a few times. Professors always brainstorm ways to motivate students, employers always seek methods to motivate employees, etc. The answer always seems to be something in the lines of 'do what you love' which is so overused it loses its impact. Anyway, as someone new to data science, I did not feel like I gained any new information after reading the article, and all the advice seems either intuitive or rehashed. Looking forward to read the HN discussion though.
  - studentrob 11y ago
    > Professors always brainstorm ways to motivate students, employers always seek methods to motivate employees Uhh.. You must have quite a fortunate streak of having great teachers and great employers. Speaking for myself, I had some great teachers, but finding a boss who seeks to motivate employees with the right challenges is rare. Bosses and companies generally assume your salary is the primary motivator.
pvnick 11y ago
Good article for beginners. A couple thoughts, just to build on what the author said: First off, data science == fancy name for data mining/analysis. Wanted to clear that up due to buzzwordy nature of "data science." Learn SQL - this is the big one. You must be proficient with SQL to be effective at data science. Whether it's running on an RDBMS or translating to map/reduce (Hive) or DAG (Spark), SQL is invaluable. If you don't know what those acronyms mean yet, don't worry. Just learn SQL. Learn to communicate insights - I would add here to try some UI techniques. Highcharts, d3.js, these are good libraries for telling your data story. You can also do a ton just with Excel and not need to write any code beyond what you wrote for the mining portion (usually SQL). I would also go back to basics with regards to statistical techniques. Start with your simple Z Score, this is such an important tool in your data science toolbox. If you're just looking at raw numbers, try to Z-normalize the data and see what happens. You'd be surprised what you can achieve with a high school statistics textbook, Postgres/MySQL (or even Excel!), and a moderate-sized data set. These are powerful enough to answer the majority of your questions, and when they fail then move on to more sexy algorithms. Edit: one more thing I forgot to mention. After SQL, learn Python. There are a ton of libraries in the python ecosystem that are perfect for data science (numpy, scipy, scikit-learn, etc). It's also one of the top languages used in academic settings. My preferred data science workspace involves Python, IPython Notebook, and Pandas (This book is quite good: http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793 http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython...)
- dummy7953 11y ago
  You're basically describing stuff I was doing like 20 freaking years ago. Minus the Hive & Spark & Highcharts & d3.js - naturally. But back then I couldn't get any of my managers to understand or appreciate what I was doing. Fickle finger of fate.
  - pvnick 11y ago
    Hell even William Gosset was doing "data science" when he popularized the Student T distribution while working for the Guinness Brewery back in 1908.
    - mziel 11y ago
      I lived through Statistics, Business Analysis, Decision Analytics, Data Analytics, Data Mining now Data Science. Same thing renamed over and over again. Regarding post above, it's right. Data scientist is someone better at statistics (classical stats, bayesian, machine learning) than computer scientist, and better at programming (SQL, R/Python for building models) than academic statistician. Plus a teaspoon of visualization (ggplot or d3).
      - rrmm 11y ago
        AI has gone through the same sort of buzzword treadmill and even programming in general. Only after living through a few cycles does it really become obvious how cyclic these sorts of trends are. I'm trying to work on being less jaded about it, and not letting my annoyance with the-new-trendy-thing-that-i-remember-doing-years-ago-under-a-different-name get in the way of learning new technology and new lessons. But it's a struggle.
- Lofkin 11y ago
  Great comment. BTW, you can make interactive visualizations in pure python with bokeh: http://bokeh.pydata.org/en/latest/ http://bokeh.pydata.org/en/latest/ Also with Blaze, you can use Pandas (or even Dplyr) syntax in python to query Hive, Spark and other large stores. http://blaze.pydata.org/en/latest/ http://blaze.pydata.org/en/latest/
minimaxir 11y ago
The actual problem with learning "data science" is making inferences and conclusions which do not violate the laws of statistics. I've seen many submissions to Hacker News and Reddit's /r/dataisbeautiful subreddit where the author goes "look, the analysis supports my conclusion and the R^2 is high, therefore this is a good analysis!" without addressing the assumptions required for those results. Of course, not everyone has a strong statistical background. Except I've seen YC-funded big data startups and venture capitalists commit the same mistakes, who should really, really know better. "Data science" is a buzzword that successful only due to obscurity and no one actually caring if the statistics are valid. That's why I've been attempting to open source all my statistical analyses/visualizations, with detailed steps on how to reproduce. (see my recent /r/dataisbeautiful submissions on reddit: https://www.reddit.com/user/minimaxir/submitted/ https://www.reddit.com/user/minimaxir/submitted/ )
- liviu- 11y ago
  >see my recent /r/dataisbeautiful submissions on reddit: https://www.reddit.com/user/minimaxir/submitted/ https://www.reddit.com/user/minimaxir/submitted/ They all seem very well-presented[0], but I can't help but ask - what do you do with this new information? [0] https://i.imgur.com/PvWYB2n.png https://i.imgur.com/PvWYB2n.png
  - minimaxir 11y ago
    I am planning a blog post on the relationship between YouTube video duration on other statistics. So I did a little exploratory analysis to validate the data. As shown, the distribution of durations in Music videos is much, much different than all other categories. As a result, it skews nearly every other analysis and I may have to exclude videos from the Music category entirely.
- probdist 11y ago
  The classic venn diagram of data science is still helpful: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram http://drewconway.com/zia/2013/3/26/the-data-science-venn-di... This article reads like a way to find yourself in the danger zone.
curiousjorge 11y ago
if I had some type of practical application that I knew could benefit from data science, like learning RoR to make a marketplace app for example, it would help a lot as I have a clear goal and route to achieve that. However, data science, machine learning, these are so broad, and seemingly complicated (my fear of complicated math formulas and statistics) and worse I don't know what I want to achieve out of it nor do I know what I want to make which really hinders the learning process for me. I need some incentive or reward at the end of the goal.
- stickperson 11y ago
  Absolutely. I remember seeing an "Epic NHL goal celebration" post on here a little while ago. That was a fun read and seemed like a good project to get some exposure to ML. http://blog.francoismaillet.com/epic-celebration/ http://blog.francoismaillet.com/epic-celebration/
- sanderjd 11y ago
  Yes, the step 1 that nobody seems to mention is that you need to have a question that you're curious about, which data analysis may be able to help you answer. The reward is having some answer to that question, with an argument for its validity. Instead of links to a bunch of datasets, I'd love to see a site that collects questions with the potential for data-driven answers. This perhaps exists somewhere.
tyfon 11y ago
I've been working as an analyst for 7 years, it's only last couple of years I've heard of statistical analysis referred to as data science. Am I missing something or is it just a new word?
- s73v3r 11y ago
  No, no, they're totally disrupting the field of statistical analysis. That's why they need a new name.
- noelsusman 11y ago
  In many ways it's just a new word for the same thing, but there's a few key differences. The main difference between traditional statistics and data science is strength in programming. Data scientists are also expected to be more well versed in statistical modeling than your average programmer or data analyst. With that said it's not really a new thing, people have been doing data science for decades. The demand for people who can program and also do more complex statistical modeling has skyrocketed so I think that's why there's a new name for it now. Part of the problem is that even with this definition there's a wide range of abilities present in data scientists. A long time computer programmer who has dabbled in statistics and a long time statistician who has dabbled in computer programming would both be data scientists even though they bring very different strengths to the table.
lessthunk 11y ago
Data science is a stupid buzzword. The ideal candidate knows enough about IT to massage data, the more the knows about the domain to investigate the better, and for sure some statistics. Most of all always do sanity checks .. does it make sense? Can it be? Is the data correct? It is an art. Like writing awesome code, etc. practice, practice, and working with experienced people is key.
- SG- 11y ago
  At least it's not something engineer like every other job in the tech field.
stdbrouw 11y ago
Definitely agree that those long lists that tell you to first become awesome at combinatorics, linear algebra, then learn all about statistical inference (that is, not actual statistical procedures but the mathematical underpinnings of statistics that would enable you to construct and evaluate methods you invent yourself), then move on to stochastic optimization... those are really more about machismo than about actually helping people to learn data science. Sure, linear algebra is helpful, but whether it's fundamental really depends on the kind of data science you're keen to do. I also generally dislike /r/machinelearning and /r/statistics because they seem to have been taken over by people who will tell you to either get a PhD or get out. But, for me, just learning whatever I thought I needed to help me solve the problem at hand got me stuck really fast. There's so much statistics where you really just have to learn it first before you can start to see when and why you'd like to use it. It never occurred to me to use hierarchical modeling and partial pooling for a certain set of problems until after I'd read Gelman & Hill. I never thought that inference on a changing process might require different techniques from the techniques for stationary processes until I had to study Hidden Markov Models for an exam. Heck, when I got started with data analysis I didn't even realize that the accuracy of most statistics improves proportional to sqrt(n) and so the next logical step in my mind was always "get more data!" instead of "learn more about statistics!" (If you look at the industry's obsession with unsampled data, data warehouses that store absolutely everything ever and map/reduce, my hunch is I'm not the only one who lacks or at some point lacked elementary statistical knowledge because it just never came up on their self-motivated, self-directed learning path.) So I think the ideal learning path incorporates a bit of both: learn more about what excites you and about what's immediately useful right now, but also put aside some time to fill out gaps in your knowledge – even things that don't immediately look useful – and make some time for fundamental/theoretical study. (x-posted from DataTau)
- mikedmiked 11y ago
  +1 for DataTau, I didn't know about that. http://www.datatau.com/ http://www.datatau.com/ Check out this too: http://www.pyquantnews.com/ http://www.pyquantnews.com/
washedup 11y ago
If you want to learn about data science, read this book: http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/ I have been going through it and I cannot think of a better resource.
searine 11y ago
Moving data around is just grunt work. Real science requires a creative and critical mind, which takes years to mold.
- stdbrouw 11y ago
  Sounds like you've also spent years molding professional disdain for everyone who's not a Real Scientist.
  - searine 11y ago
    No I've just seen too many people spin their wheels on "analysis" that is not hypothesis driven. You got to start with questions to get answers, and the hard part of science isn't crunching data, it is asking the right question!
    - danenania 11y ago
      And how does the Right Question appear if not through exploration and manipulation of the data? Theory can obviously be very useful, but much of this stress on advanced statistics and phds is just a smokescreen for academics who suck at programming. If you can't program and manipulate data, statistics won't save you because you won't have the ability to dig deep enough to find valuable insights. On the other side, if you know how to slice and dice data quickly and reliably, you can learn a huge amount by applying only the simplest statistical techniques. Generally the simple techniques are better anyway because they make mistakes less likely and your findings are easier to communicate.
      - searine 11y ago
        >And how does the Right Question appear if not through exploration and manipulation of the data? Questions don't magically come out of a data set. Doing so is called a fishing expedition and usually results in boring, descriptive results which have no impact. To answer impactful questions, you must go into your data collection with the questions in mind. To understand what questions to ask, you need a trained, critical, and creative mind. That is something you don't get from pushing bits. >If you can't program and manipulate data Programming, and manipulating data is easy. Almost every new statistician these days can, and does do this routinely. What's hard is the years of intuition about what is meaningful and what is noise. I know. It's hard to hear, and career programmers most of all hate to hear it, but its the truth.
acbart 11y ago
As someone who uses "Data Science" to teach "Computational Thinking", I think this blog post hits on a lot of really valuable pedagogoical notes. Getting motivated, learning things through doing, and having a strong context for your learning. For those wondering why I put my buzzwords in quotes, it's because I don't want to sound like I'm a huge proponent of either of them. CT is the term I use to describe how I teach my students about abstractions, algorithms, and some programming. DS is the term I use to describe how students learn all of that in the context of working with data related to their own majors. I'm not trying to claim some crazy paradigm shift, just that it's a great way to convince students that CS is useful to them.
gtrubetskoy 11y ago
Read this (free) book: http://mmds.org/ http://mmds.org/
vermontdevil 11y ago
Also learning R Language and using RStudio is a great way to get into. RStudio has so many packages to help you do any data analysis. The learning curve is quite steep though.
davemel37 11y ago
Anyone interested in data science should first study cognitive psychology. The CIA has a manual on the psychology of intelligence analysis that is a must read for anyone pursuing any analytical job. If you dont understand how your mind sees, processes, retains and recalls data...how can you possibly analyze it accurately?
- cwyers 11y ago
  You have a link to where to obtain said manual?
  - davemel37 11y ago
    https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/psychology-of-intelligence-analysis/ https://www.cia.gov/library/center-for-the-study-of-intellig...
    - cwyers 11y ago
      Thank you.
anderspitman 11y ago
These principles are useful when learning anything really: human language (immersion), programming (build something), sports (practice), etc. That said, as someone who worked in software engineering for 5 years without a degree, and recently returned to school, I would say be careful not to discount studying theory at the same time you're practicing your craft. I really think a combined approach of structured university courses and MOOCs, including reading textbooks, along with applying the knowledge has been the best approach for me. I was arrogant about "not needing" a degree for years, feeling justified by the fact that I was making very valuable contributions as an engineer, until I finally went back to school and realized how valuable theoretical knowledge can be.
graycat 11y ago
Here are some topics. Are they considered relevant to data science? Matrix row rank and column rank are equal. In matrix theory, the polar decomposition. Each Hermitian matrix has an orthogonal basis of eigenvectors. Weak law of large numbers. Strong law of large numbers. The Radon-Nikodym theorem and conditional expectation. Sample mean and variance are sufficient statistics for independent, identically distributed samples from a univariate Gaussian distribution. The Neyman-Pearson lemma. The Cramer-Rao lower bound. The margingale convergence theorem. Convergence results of Markov chains. Markov processes in continuous time. The law of the iterated logarithm. The Lindeberg-Feller version of the central limit theorem. The normal equations of linear regression analysis. Non-parametric statistical hypothesis tests. Power spectral estimation of second order, stationary stochastic processes. Resampling plans. Unbiased estimation. Minimum variance estimation. Maximum likelihood estimation. Uniform minimum variance unbiased estimation. Wiener filtering. Kalman filtering. Autoregressive moving average (ARMA) processes. Rank statistics are always sufficient. Farkas lemma. Minimum spanning trees on directed graphs. The simplex algorithm of linear programming. Column generation in linear programming (Gilmore-Gomory). The simplex algorithm for min cost capacitated network flows. conjugate gradients. The Kuhn-Tucker conditions. Constraint qualifications for the Kuhn-Tucker conditions. Fourier series. The Fourier transform. Hilbert space. Banach space. Quasi-Newton iteration and updates, e.g., Broyden-Fletcher-Goldfarb-Shanno. Orthogonal polynomials for numerically stable polynomial curve fitting. Lagrange multipliers. The Pontryagin maximum principle. Quadratic programming. Convex programming. Multi-objective programming. Integer linear programming. Deterministic dynamic programming. Stochastic dynamic programming. The linear-quadratic-Gaussian case of dynamic programming.
- S4M 11y ago
  The topics you mention are maths or applied maths topics. "Data Science" is a bubbly term that roughly means "take that big dump of data and give me some advice on how to make more money", so your list, very sadly, has little relevance with it.
  - graycat 11y ago
    Most of those topics I listed are supposed to be good at taking data and saying how to "make more money"!
gbersac 11y ago
I am doing this course and find it really good : https://www.edx.org/course/scalable-machine-learning-uc-berkeleyx-cs190-1x https://www.edx.org/course/scalable-machine-learning-uc-berk... It is about creating a linear and logistic regression + pca using spark (python api).
pvaldes 11y ago
Learn biology, chemistry and physics, question yourself often and use your instinct.

248 points by spYa 11y ago

"You need something that will motivate you to keep learning." This is so true and often forgotten. I am always learning new things, but the concepts that stick, beyond just the basics, are tied to specific projects or solutions to real problems. I'm typically ok with being a "jack-of-all-trades" for most technologies, just to stay aware of new things. However, when it comes to applying new concepts, skills, or tech to solve problems, a deeper understanding is required; usually obtained through motivation.

liviu- 11y ago
>"You need something that will motivate you to keep learning." This is so true and often forgotten. I'm surprised that both you and OP seem to think this advice is rare, as I've personally seen it mentioned more than a few times. Professors always brainstorm ways to motivate students, employers always seek methods to motivate employees, etc. The answer always seems to be something in the lines of 'do what you love' which is so overused it loses its impact. Anyway, as someone new to data science, I did not feel like I gained any new information after reading the article, and all the advice seems either intuitive or rehashed. Looking forward to read the HN discussion though.
- studentrob 11y ago
  > Professors always brainstorm ways to motivate students, employers always seek methods to motivate employees Uhh.. You must have quite a fortunate streak of having great teachers and great employers. Speaking for myself, I had some great teachers, but finding a boss who seeks to motivate employees with the right challenges is rare. Bosses and companies generally assume your salary is the primary motivator.

pvnick 11y ago

Good article for beginners. A couple thoughts, just to build on what the author said: First off, data science == fancy name for data mining/analysis. Wanted to clear that up due to buzzwordy nature of "data science." Learn SQL - this is the big one. You must be proficient with SQL to be effective at data science. Whether it's running on an RDBMS or translating to map/reduce (Hive) or DAG (Spark), SQL is invaluable. If you don't know what those acronyms mean yet, don't worry. Just learn SQL. Learn to communicate insights - I would add here to try some UI techniques. Highcharts, d3.js, these are good libraries for telling your data story. You can also do a ton just with Excel and not need to write any code beyond what you wrote for the mining portion (usually SQL). I would also go back to basics with regards to statistical techniques. Start with your simple Z Score, this is such an important tool in your data science toolbox. If you're just looking at raw numbers, try to Z-normalize the data and see what happens. You'd be surprised what you can achieve with a high school statistics textbook, Postgres/MySQL (or even Excel!), and a moderate-sized data set. These are powerful enough to answer the majority of your questions, and when they fail then move on to more sexy algorithms. Edit: one more thing I forgot to mention. After SQL, learn Python. There are a ton of libraries in the python ecosystem that are perfect for data science (numpy, scipy, scikit-learn, etc). It's also one of the top languages used in academic settings. My preferred data science workspace involves Python, IPython Notebook, and Pandas (This book is quite good: http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793 http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython...)

dummy7953 11y ago
You're basically describing stuff I was doing like 20 freaking years ago. Minus the Hive & Spark & Highcharts & d3.js - naturally. But back then I couldn't get any of my managers to understand or appreciate what I was doing. Fickle finger of fate.
- pvnick 11y ago
  Hell even William Gosset was doing "data science" when he popularized the Student T distribution while working for the Guinness Brewery back in 1908.
  - mziel 11y ago
    I lived through Statistics, Business Analysis, Decision Analytics, Data Analytics, Data Mining now Data Science. Same thing renamed over and over again. Regarding post above, it's right. Data scientist is someone better at statistics (classical stats, bayesian, machine learning) than computer scientist, and better at programming (SQL, R/Python for building models) than academic statistician. Plus a teaspoon of visualization (ggplot or d3).
    - rrmm 11y ago
      AI has gone through the same sort of buzzword treadmill and even programming in general. Only after living through a few cycles does it really become obvious how cyclic these sorts of trends are. I'm trying to work on being less jaded about it, and not letting my annoyance with the-new-trendy-thing-that-i-remember-doing-years-ago-under-a-different-name get in the way of learning new technology and new lessons. But it's a struggle.
Lofkin 11y ago
Great comment. BTW, you can make interactive visualizations in pure python with bokeh: http://bokeh.pydata.org/en/latest/ http://bokeh.pydata.org/en/latest/ Also with Blaze, you can use Pandas (or even Dplyr) syntax in python to query Hive, Spark and other large stores. http://blaze.pydata.org/en/latest/ http://blaze.pydata.org/en/latest/

minimaxir 11y ago

The actual problem with learning "data science" is making inferences and conclusions which do not violate the laws of statistics. I've seen many submissions to Hacker News and Reddit's /r/dataisbeautiful subreddit where the author goes "look, the analysis supports my conclusion and the R^2 is high, therefore this is a good analysis!" without addressing the assumptions required for those results. Of course, not everyone has a strong statistical background. Except I've seen YC-funded big data startups and venture capitalists commit the same mistakes, who should really, really know better. "Data science" is a buzzword that successful only due to obscurity and no one actually caring if the statistics are valid. That's why I've been attempting to open source all my statistical analyses/visualizations, with detailed steps on how to reproduce. (see my recent /r/dataisbeautiful submissions on reddit: https://www.reddit.com/user/minimaxir/submitted/ https://www.reddit.com/user/minimaxir/submitted/ )

liviu- 11y ago
>see my recent /r/dataisbeautiful submissions on reddit: https://www.reddit.com/user/minimaxir/submitted/ https://www.reddit.com/user/minimaxir/submitted/ They all seem very well-presented[0], but I can't help but ask - what do you do with this new information? [0] https://i.imgur.com/PvWYB2n.png https://i.imgur.com/PvWYB2n.png
- minimaxir 11y ago
  I am planning a blog post on the relationship between YouTube video duration on other statistics. So I did a little exploratory analysis to validate the data. As shown, the distribution of durations in Music videos is much, much different than all other categories. As a result, it skews nearly every other analysis and I may have to exclude videos from the Music category entirely.
probdist 11y ago
The classic venn diagram of data science is still helpful: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram http://drewconway.com/zia/2013/3/26/the-data-science-venn-di... This article reads like a way to find yourself in the danger zone.

curiousjorge 11y ago

if I had some type of practical application that I knew could benefit from data science, like learning RoR to make a marketplace app for example, it would help a lot as I have a clear goal and route to achieve that. However, data science, machine learning, these are so broad, and seemingly complicated (my fear of complicated math formulas and statistics) and worse I don't know what I want to achieve out of it nor do I know what I want to make which really hinders the learning process for me. I need some incentive or reward at the end of the goal.

stickperson 11y ago
Absolutely. I remember seeing an "Epic NHL goal celebration" post on here a little while ago. That was a fun read and seemed like a good project to get some exposure to ML. http://blog.francoismaillet.com/epic-celebration/ http://blog.francoismaillet.com/epic-celebration/
sanderjd 11y ago
Yes, the step 1 that nobody seems to mention is that you need to have a question that you're curious about, which data analysis may be able to help you answer. The reward is having some answer to that question, with an argument for its validity. Instead of links to a bunch of datasets, I'd love to see a site that collects questions with the potential for data-driven answers. This perhaps exists somewhere.

tyfon 11y ago

I've been working as an analyst for 7 years, it's only last couple of years I've heard of statistical analysis referred to as data science. Am I missing something or is it just a new word?

s73v3r 11y ago
No, no, they're totally disrupting the field of statistical analysis. That's why they need a new name.
noelsusman 11y ago
In many ways it's just a new word for the same thing, but there's a few key differences. The main difference between traditional statistics and data science is strength in programming. Data scientists are also expected to be more well versed in statistical modeling than your average programmer or data analyst. With that said it's not really a new thing, people have been doing data science for decades. The demand for people who can program and also do more complex statistical modeling has skyrocketed so I think that's why there's a new name for it now. Part of the problem is that even with this definition there's a wide range of abilities present in data scientists. A long time computer programmer who has dabbled in statistics and a long time statistician who has dabbled in computer programming would both be data scientists even though they bring very different strengths to the table.

lessthunk 11y ago

Data science is a stupid buzzword. The ideal candidate knows enough about IT to massage data, the more the knows about the domain to investigate the better, and for sure some statistics. Most of all always do sanity checks .. does it make sense? Can it be? Is the data correct? It is an art. Like writing awesome code, etc. practice, practice, and working with experienced people is key.

SG- 11y ago
At least it's not something engineer like every other job in the tech field.

stdbrouw 11y ago

Definitely agree that those long lists that tell you to first become awesome at combinatorics, linear algebra, then learn all about statistical inference (that is, not actual statistical procedures but the mathematical underpinnings of statistics that would enable you to construct and evaluate methods you invent yourself), then move on to stochastic optimization... those are really more about machismo than about actually helping people to learn data science. Sure, linear algebra is helpful, but whether it's fundamental really depends on the kind of data science you're keen to do. I also generally dislike /r/machinelearning and /r/statistics because they seem to have been taken over by people who will tell you to either get a PhD or get out. But, for me, just learning whatever I thought I needed to help me solve the problem at hand got me stuck really fast. There's so much statistics where you really just have to learn it first before you can start to see when and why you'd like to use it. It never occurred to me to use hierarchical modeling and partial pooling for a certain set of problems until after I'd read Gelman & Hill. I never thought that inference on a changing process might require different techniques from the techniques for stationary processes until I had to study Hidden Markov Models for an exam. Heck, when I got started with data analysis I didn't even realize that the accuracy of most statistics improves proportional to sqrt(n) and so the next logical step in my mind was always "get more data!" instead of "learn more about statistics!" (If you look at the industry's obsession with unsampled data, data warehouses that store absolutely everything ever and map/reduce, my hunch is I'm not the only one who lacks or at some point lacked elementary statistical knowledge because it just never came up on their self-motivated, self-directed learning path.) So I think the ideal learning path incorporates a bit of both: learn more about what excites you and about what's immediately useful right now, but also put aside some time to fill out gaps in your knowledge – even things that don't immediately look useful – and make some time for fundamental/theoretical study. (x-posted from DataTau)

mikedmiked 11y ago
+1 for DataTau, I didn't know about that. http://www.datatau.com/ http://www.datatau.com/ Check out this too: http://www.pyquantnews.com/ http://www.pyquantnews.com/

washedup 11y ago

If you want to learn about data science, read this book: http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/ I have been going through it and I cannot think of a better resource.

searine 11y ago

Moving data around is just grunt work. Real science requires a creative and critical mind, which takes years to mold.

stdbrouw 11y ago
Sounds like you've also spent years molding professional disdain for everyone who's not a Real Scientist.
- searine 11y ago
  No I've just seen too many people spin their wheels on "analysis" that is not hypothesis driven. You got to start with questions to get answers, and the hard part of science isn't crunching data, it is asking the right question!
  - danenania 11y ago
    And how does the Right Question appear if not through exploration and manipulation of the data? Theory can obviously be very useful, but much of this stress on advanced statistics and phds is just a smokescreen for academics who suck at programming. If you can't program and manipulate data, statistics won't save you because you won't have the ability to dig deep enough to find valuable insights. On the other side, if you know how to slice and dice data quickly and reliably, you can learn a huge amount by applying only the simplest statistical techniques. Generally the simple techniques are better anyway because they make mistakes less likely and your findings are easier to communicate.
    - searine 11y ago
      >And how does the Right Question appear if not through exploration and manipulation of the data? Questions don't magically come out of a data set. Doing so is called a fishing expedition and usually results in boring, descriptive results which have no impact. To answer impactful questions, you must go into your data collection with the questions in mind. To understand what questions to ask, you need a trained, critical, and creative mind. That is something you don't get from pushing bits. >If you can't program and manipulate data Programming, and manipulating data is easy. Almost every new statistician these days can, and does do this routinely. What's hard is the years of intuition about what is meaningful and what is noise. I know. It's hard to hear, and career programmers most of all hate to hear it, but its the truth.

acbart 11y ago

As someone who uses "Data Science" to teach "Computational Thinking", I think this blog post hits on a lot of really valuable pedagogoical notes. Getting motivated, learning things through doing, and having a strong context for your learning. For those wondering why I put my buzzwords in quotes, it's because I don't want to sound like I'm a huge proponent of either of them. CT is the term I use to describe how I teach my students about abstractions, algorithms, and some programming. DS is the term I use to describe how students learn all of that in the context of working with data related to their own majors. I'm not trying to claim some crazy paradigm shift, just that it's a great way to convince students that CS is useful to them.

gtrubetskoy 11y ago

Read this (free) book: http://mmds.org/ http://mmds.org/

vermontdevil 11y ago

Also learning R Language and using RStudio is a great way to get into. RStudio has so many packages to help you do any data analysis. The learning curve is quite steep though.

davemel37 11y ago

Anyone interested in data science should first study cognitive psychology. The CIA has a manual on the psychology of intelligence analysis that is a must read for anyone pursuing any analytical job. If you dont understand how your mind sees, processes, retains and recalls data...how can you possibly analyze it accurately?

cwyers 11y ago
You have a link to where to obtain said manual?
- davemel37 11y ago
  https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/psychology-of-intelligence-analysis/ https://www.cia.gov/library/center-for-the-study-of-intellig...
  - cwyers 11y ago
    Thank you.

anderspitman 11y ago

These principles are useful when learning anything really: human language (immersion), programming (build something), sports (practice), etc. That said, as someone who worked in software engineering for 5 years without a degree, and recently returned to school, I would say be careful not to discount studying theory at the same time you're practicing your craft. I really think a combined approach of structured university courses and MOOCs, including reading textbooks, along with applying the knowledge has been the best approach for me. I was arrogant about "not needing" a degree for years, feeling justified by the fact that I was making very valuable contributions as an engineer, until I finally went back to school and realized how valuable theoretical knowledge can be.

graycat 11y ago

Here are some topics. Are they considered relevant to data science? Matrix row rank and column rank are equal. In matrix theory, the polar decomposition. Each Hermitian matrix has an orthogonal basis of eigenvectors. Weak law of large numbers. Strong law of large numbers. The Radon-Nikodym theorem and conditional expectation. Sample mean and variance are sufficient statistics for independent, identically distributed samples from a univariate Gaussian distribution. The Neyman-Pearson lemma. The Cramer-Rao lower bound. The margingale convergence theorem. Convergence results of Markov chains. Markov processes in continuous time. The law of the iterated logarithm. The Lindeberg-Feller version of the central limit theorem. The normal equations of linear regression analysis. Non-parametric statistical hypothesis tests. Power spectral estimation of second order, stationary stochastic processes. Resampling plans. Unbiased estimation. Minimum variance estimation. Maximum likelihood estimation. Uniform minimum variance unbiased estimation. Wiener filtering. Kalman filtering. Autoregressive moving average (ARMA) processes. Rank statistics are always sufficient. Farkas lemma. Minimum spanning trees on directed graphs. The simplex algorithm of linear programming. Column generation in linear programming (Gilmore-Gomory). The simplex algorithm for min cost capacitated network flows. conjugate gradients. The Kuhn-Tucker conditions. Constraint qualifications for the Kuhn-Tucker conditions. Fourier series. The Fourier transform. Hilbert space. Banach space. Quasi-Newton iteration and updates, e.g., Broyden-Fletcher-Goldfarb-Shanno. Orthogonal polynomials for numerically stable polynomial curve fitting. Lagrange multipliers. The Pontryagin maximum principle. Quadratic programming. Convex programming. Multi-objective programming. Integer linear programming. Deterministic dynamic programming. Stochastic dynamic programming. The linear-quadratic-Gaussian case of dynamic programming.

S4M 11y ago
The topics you mention are maths or applied maths topics. "Data Science" is a bubbly term that roughly means "take that big dump of data and give me some advice on how to make more money", so your list, very sadly, has little relevance with it.
- graycat 11y ago
  Most of those topics I listed are supposed to be good at taking data and saying how to "make more money"!

gbersac 11y ago

I am doing this course and find it really good : https://www.edx.org/course/scalable-machine-learning-uc-berkeleyx-cs190-1x https://www.edx.org/course/scalable-machine-learning-uc-berk... It is about creating a linear and logistic regression + pca using spark (python api).

pvaldes 11y ago

Learn biology, chemistry and physics, question yourself often and use your instinct.