[joshvm]

Don't bother with journals - in pretty much any subject - unless you have a degree and/or you understand what to look for, or are directed to notable articles in bibliographies or by peers. There is a lot of crap in all journals, it's often needlessly technical for practical purposes or too bleeding edge to actually be useful yet.

I'm not trying to be snarky, but honestly unless you know what you're looking for it's a fool's game. Once you've got the feel for a subject, you tend to find several authors that crop up time and time again, or landmark papers that really shifted the field. But that takes a long time, it takes most PhD students a year to fully understand and simply collate the background of a topic they may think they know a lot about.

That and no one actually reads journals. You do a search on Web of Knowledge or ADS or arXiv or whatever your poison and you see what comes up. Point is, you need to know what you're looking for.

This is akin to saying that if you read Phys Rev enough, you'll become a physicist. Sure, sure, keep up with the trends, but big important results get press which is enough to rely on to start off with.

To become a data scientist? Read the recommended textbooks and take a proper degree in statistics, computer or data science. Look at the courses on EdX and Coursera for a starting point, they'll help you decide whether this is something you seriously want to pursue.

Even if this is just a hobby, e.g. you're a coder that wants to branch out, you should still take the time to invest in education properly. Data science, like statistics in general, is very easy to mess up. When people draw bad conclusions from data (and good data scientists can make up any conclusion from any data set), bad things inevitably happen. Entire threads of science have been destroyed because somewhere, someone messed up their stats and apparently important results are meaningless.

[kriro]

While a little off topic I think my typical research process goes like this:

- Hear or read about something that sounds neat

- See if there's a wikipedia article (I always cringe when I hear some colleagues of mine say never, ever use it)

- Get a high level understanding of the topic from the wikipedia article...that usually leads to some other wikipedia articles + plain old Google searches...just fishing for whatever comes up [I also search for TED talks, youtube videos and MOOCs related to the topic]

- Scribble stuff down on a piece of paper and structure it in a way that makes sense to me (sometimes it's just a list, sometimes a full blown mindmap) ...at this point I have a decent high level understanding...which basically means I could describe the topic to someone without stumbling (which I usually try at this point)

- From the high level understanding I usually also get: key terms for searches, intor level books/articles that are linked etc.

- At this point working at a university comes in handy because it lets me be behind the annoying paywalls at will...search Google Scholar or similar databases for the mined key words. Everything that looks remotely interesting...oh wait BEST TOOL EVER

- Zotero is sick good, comes as a FF plugin...great. If you search in scientific databases and the like a little icon pops up in the address bar of the browser indicating it identified the sources...click, mark everything -> it goes into your collection (with full text access) [I order it by topic so for AI I might have Expert Systems and Rule Based, Fuzzy etc.]

- So basically I just wade through the databases and get everything that sounds interesting from the title into Zotero. Alays a good idea to get some "history of XYZ" or "XCY since author Y" sources

- Once done I read the abstracts and the conclusions and put a rough note what the articels are about. I also scan the sources to grow my collection of relevant articles (I mark what I don't think is relevant or move it into a special subcollection)

- I usually try to establish a history of the field with the major stepping stones, this is usually easy (sometimes not, worth a paper to make it easier for future researchers :P)

- If it's related to programming in any way I also search google or github directly for anything related. Code is good :)

[often there are tomes that are the de facto standards in their fields that serve as a massive source collection as well. Perfect example would be AI - A Modern Approach]