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Launching our Data Science and Big Data Track

blog.udacity.com

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Re: Launching our Data Science and Big Data Track

#21

Post by varelse »

stiff wrote: post #12

99% of people looking for information about big data and 99% of people looking to do data science, don't have nowhere near big data, and don't need to be taught hadoop. Those people are instead often lacking fundamental knowledge and are looking for a trick technological solution instead of reviewing their basics. A "data science" track should hence be 90% about algorithms, data structures, linear algebra and compute…

100% agree except that I think courses like this are great for people who want to bluff their way through a job interview to get one of those $150K-$250K big data jobs that are the rage right now (watching Andrew Ng's machine learning lectures beforehand as well would be the pro move in my book). In my experience so far, most of these positions appear to be Java programming gigs where, sadly, issues like SIMD, cache locality, and the GPU are actively ignored or dismissed. Autoboxing alone to use Java generics pretty much destroys any hope of efficient cache use.

I've walked out of job interviews over this sort of thing wherein it's been described as a "performance programming" position for big data and/or machine learning except that everything has to be in Java. And sure, Java performance programming is a thing, but compared to what one can achieve with SIMD, attention to cache-locality and/or running on the GPU for the 20% of the code that eats 80-99% of the cycles, it's laughably uninteresting to me, big bucks aside.

Re: Launching our Data Science and Big Data Track

#22

Post by disgruntledphd2 »

stiff wrote: post #12

99% of people looking for information about big data and 99% of people looking to do data science, don't have nowhere near big data, and don't need to be taught hadoop. Those people are instead often lacking fundamental knowledge and are looking for a trick technological solution instead of reviewing their basics. A "data science" track should hence be 90% about algorithms, data structures, linear algebra and compute…

Man, I love that book. I loved it so much, as a matter of fact, that I went and bought it from Amazon. Even though I haven't directly used a large amount of it (though the locally sensitive hashing stuff and the communicational complexity stuff is easily worth the price), it has definitely made me think about large datasets in a much more productive fashion.

My only complaint is with the awful, awful cover. No link, but if you've ever seen it, you'll know exactly what I mean.

Re: Launching our Data Science and Big Data Track

#23

Post by bcbrown »

stiff wrote: post #20
resu_nimda wrote: post #17

Earlier quoted context omitted.

Aren't there already plenty of courses and books out there about algorithms, data structures, RDBMS, etc? I have a pretty good background in a lot of that (can always learn more of course), but I don't know anything about Hadoop and MapReduce (which is conveniently not mentioned in your critique, probably because it does fall under your list of acceptable topics), so I find this course interesting. I find the claim o…

What really annoys me is this particular blog post, not just the existence of the course, for example this thing: “What is Big Data?” They will teach you fundamental principles of Hadoop, MapReduce, and how to make sense of big data. Developers will learn skills that provide fundamental building blocks towards deriving maximum value from the world's data. Technologists and business managers will gain the knowledge to…

If you have any suggestions for online courses or other self-learning resources for distributed systems, I would be much obliged. The Ullman book is already in my queue.

Re: Launching our Data Science and Big Data Track

#24

Post by JPKab »

stiff wrote: post #20
resu_nimda wrote: post #17

Earlier quoted context omitted.

Aren't there already plenty of courses and books out there about algorithms, data structures, RDBMS, etc? I have a pretty good background in a lot of that (can always learn more of course), but I don't know anything about Hadoop and MapReduce (which is conveniently not mentioned in your critique, probably because it does fall under your list of acceptable topics), so I find this course interesting. I find the claim o…

What really annoys me is this particular blog post, not just the existence of the course, for example this thing: “What is Big Data?” They will teach you fundamental principles of Hadoop, MapReduce, and how to make sense of big data. Developers will learn skills that provide fundamental building blocks towards deriving maximum value from the world's data. Technologists and business managers will gain the knowledge to…

The Ullman book is intimidating to say the least.

I'm frankly humbled at how little I know. Thanks for posting it. I'm going to be spending a lot of evenings on it.

Re: Launching our Data Science and Big Data Track

#26

Post by mdm_ »

stiff wrote: post #12

99% of people looking for information about big data and 99% of people looking to do data science, don't have nowhere near big data, and don't need to be taught hadoop. Those people are instead often lacking fundamental knowledge and are looking for a trick technological solution instead of reviewing their basics. A "data science" track should hence be 90% about algorithms, data structures, linear algebra and compute…

Any recommendations for learning more about PostgreSQL's features? I use Postgres for basic web app data store kind of applications, but haven't gone much beyond that. I've skimmed the manual and it doesn't look that dissimilar to MySQL's, but I see a lot of comments heaping praise on Postgres and/or criticizing MySQL. Is there a good book on Postgres?

Re: Launching our Data Science and Big Data Track

#27

Post by sanskritabelt »

varelse wrote: post #21
stiff wrote: post #12

99% of people looking for information about big data and 99% of people looking to do data science, don't have nowhere near big data, and don't need to be taught hadoop. Those people are instead often lacking fundamental knowledge and are looking for a trick technological solution instead of reviewing their basics. A "data science" track should hence be 90% about algorithms, data structures, linear algebra and compute…

100% agree except that I think courses like this are great for people who want to bluff their way through a job interview to get one of those $150K-$250K big data jobs that are the rage right now (watching Andrew Ng's machine learning lectures beforehand as well would be the pro move in my book). In my experience so far, most of these positions appear to be Java programming gigs where, sadly, issues like SIMD, cache…

It's faster than python and R, dogg.

Re: Launching our Data Science and Big Data Track

#28

Post by sanskritabelt »

mathattack wrote: post #19
jsaxton86 wrote: post #7

It seems that the majority of Big Data/Data Science applications are designed to give advertisers insight into things I don't really want them to have insight into. That really sucks, because the technology is cool, but I don't want to help build that kind of future. It's kind of analogous to how I feel about Computer Vision: there are a handful of legitimate purposes for it, but most applications of the technology f…

There are plenty of Big Data applications that aren't unethical. Back in the 90s (before the term Big Data was conceived) two of the biggest users of Teradata were P&G and Wal*mart. It was more about supply chain and retail store efficiency than anything nefarious. Big data helped make sure that store shelves had what people wanted. Today there are mass spamvertising campaigns on Big Data, but there are also applicat…

Helping Walmart is, at best, morally suspect.

Re: Launching our Data Science and Big Data Track

#30

Post by Houshalter »

sanskritabelt wrote: post #28
mathattack wrote: post #19

Earlier quoted context omitted.

There are plenty of Big Data applications that aren't unethical. Back in the 90s (before the term Big Data was conceived) two of the biggest users of Teradata were P&G and Wal*mart. It was more about supply chain and retail store efficiency than anything nefarious. Big data helped make sure that store shelves had what people wanted. Today there are mass spamvertising campaigns on Big Data, but there are also applicat…

Helping Walmart is, at best, morally suspect.

Reducing retail waste is better for everyone. Though some of the stuff they do is questionable, like how carefully items in the store are placed to maximize the amount of unnecessary crap they sell to impulsive people.
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