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.<p>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.<p>That and no one <i>actually</i> 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.<p>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.<p>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.<p>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.