21–30 of 83 posts
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?
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 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.
It is an art. Like writing awesome code, etc. practice, practice, and working with experienced people is key.
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 every…
"Data science" at it's core is just statistical analysis, but it has been slowly morphing over the past few decades thanks to the budding field of machine learning and the commoditization of computing power. This has drastically changed the field of statistical research, and although the underlying math is the same, the tools and the amount of data are constantly in flux. Someone along the way must have felt that this evolution of statistical analysis needed a new name. In all honesty, it's just a name, and it doesn't matter. What matters is if you understand how to use it.
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…
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 every…
If you want to do data science for real:
1. Get Masters of PhD from statistics, computer science, economics, physics or some other heavy field and specialize data analysis in that field. You must learn lots of statistics when doing so.
2. Learn programming, statistical machine learning and tools of the trade.
Good data science is not based on collecting large amounts of data passively and then mining it mindlessly. You need to ask right questions and design data collection and modeling process based on those questions.
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)
I have been going through it and I cannot think of a better resource.
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'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?
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.