Moving data around is just grunt work.
Real science requires a creative and critical mind, which takes years to mold.
How to learn data science
31–40 of 83 posts
Re: How to learn data science
#32The 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…
[deleted]
Re: How to learn data science
#33As 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.
Re: How to learn data science
#34The 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…
I assume data science is often used in place of astrology. People just want to have something to cling to, to get over their fears and insecurity. So if you can generate some reassuring graphs, who cares if they are based on solid statistics or not?
>People just want to have something to cling to, to get over their fears and insecurity.
The other side of this is that some businesses (especially SMBs) are so horrible at utilizing their data that very basic analyses can reap big gains (80/20 rule!). For the vast majority of businesses there is no need for elaborate models or machine learning techniques.
Re: How to learn data science
#35Moving data around is just grunt work. Real science requires a creative and critical mind, which takes years to mold.
Sounds like you've also spent years molding professional disdain for everyone who's not a Real Scientist.
Re: How to learn data science
#36The 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…
Roughly 80% of data scientists I know have PhD in something very math heavy. Rest have masters degrees. There are programmers who can assist them doing the grunt work but it's just basic programming to assist analysts to crunch data. 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.…
I've seen a huge range in the people calling themselves data scientists. Some have very analytically intensive academic degrees, others just finished a data science boot camp, and there are a lot of people that used to be called 'business analysts' who are basically doing the same job with a fancier title. In every group, I've had people tell me that what they're doing is really data science, because data science needs the (academic|integrated|business) perspective that they have, and what the other people are doing isn't really data science.
Re: How to learn data science
#37Good 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…
I would shift priorities to Python (from SQL).
Unless one has "data scientist" title so to make "database engineer" look more fancy, then data comes in various shapes and forms. And most questions cannot be answered with a simple aggregation.
For example, data I work on (I am a data scientist freelancer) is flat csv files, xls files, JSON files, some text files I need to parse, various SQL, MongoDB, things I am getting from various APIs, etc...
While understanding joins is crucial (and normal forms, etc), SQL itself does take negligible amount of my time (and effort).
Re: How to learn data science
#38Read this (free) book: http://mmds.org/
Re: How to learn data science
#39Also 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.
Re: How to learn data science
#40The 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…
Roughly 80% of data scientists I know have PhD in something very math heavy. Rest have masters degrees. There are programmers who can assist them doing the grunt work but it's just basic programming to assist analysts to crunch data. 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.…
Academic credentials aren't enough, good data-driven decisionmaking is as much an art as an academic discipline. A p-value of .01 is a Nobel Prize in medicine and unpublishable in physics -- domain knowledge is important to have a feel for the difference.