Stephanie Glen has not received any gifts yet
Posted on July 31, 2020 at 9:09am 0 Comments 0 Likes
This one picture shows what areas of calculus and linear algebra are most useful for data scientists.
If you read any article worth its salt on the topic Math Needed for Data Science, you'll see calculus mentioned. Calculus (and it's closely related counterpart, linear algebra) has some very narrow (but very useful) applications to data science. If you have a decent algebra background (which I'm assuming you do, if you're a data scientist!) then you can learn…
ContinuePosted on July 26, 2020 at 7:42am 0 Comments 0 Likes
P-values and critical values are so similar that they are often confused. They both do the same thing: enable you to support or reject the null hypothesis in a test. But they differ in how you get to make that decision. In other words, they are two different approaches to the same result. This picture sums up the p value vs critical value approaches.…
ContinuePosted on July 15, 2020 at 12:13pm 0 Comments 0 Likes
If you scour the internet for "ANOVA vs Regression", you might be confused by the results. Are they the same? Or aren't they? The answer is that they can be the same procedure, if you set them up to be that way. But there are differences between the two methods. This one picture sums up those differences.
Posted on July 8, 2020 at 9:02am 0 Comments 0 Likes
The following graphic is based on Sam Priddy's excellent DSC/Tableau Webinar How to Accelerate and Scale Your Data Science Workflows. Sam covered many interesting points for organizing, analyzing and presenting data--including which graph is best suited for different data types. This graphic is an overview of some of Sam's points. For more…
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Your article is so relevant Stephanie, as I am 48 and contemplating an online masters in Data Science. My answer has been to do as much as I can through MOOCs, self-driven projects, and other less-expensive learning. If it gets to the point that I have done all of that and proven to myself that I can do Data Science, and that I need a degree to get land a job, I will then do it. Reality is you do need to be more careful and more risk-averse as you age, as the time to recover from mistakes is just not there anymore.