How to learn data science
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How to learn data science
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Re: How to learn data science
#2"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.
Re: How to learn data science
#3Good 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...)
Re: How to learn data science
#4"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 t…
>"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.
Re: How to learn data science
#5Good 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. I…
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.
Re: How to learn data science
#6The 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/ )
Re: How to learn data science
#7Good 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. I…
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.
Hell even William Gosset was doing "data science" when he popularized the Student T distribution while working for the Guinness Brewery back in 1908.
Re: How to learn data science
#8The 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 every…
>see my recent /r/dataisbeautiful submissions on reddit: 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?
Re: How to learn data science
#9The 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 every…
The classic venn diagram of data science is still helpful: 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.
Re: How to learn data science
#10The 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 every…
>see my recent /r/dataisbeautiful submissions on reddit: 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
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.