In this post, I want to walk you through the logic of building a map, step by step …
Data science, artificial intelligence, automation, and other advanced technologies are reshaping the world.
As the world changes who’s likely to succeed?
The last few posts at Sharp Sight have been fairly intense.
Let’s make a quick map …
In several recent blog posts, I’ve emphasized the importance of data analysis.
My main point has been, that if you want to learn data science, you need to learn data analysis. Data analysis is the foundation of practical data science.
With that statement in mind, I want to show you step-by-step what an analysis looks like in R …
For a couple of years, I’ve been writing about the importance of data analysis, saying that data analysis is essentially the foundation of data science itself. While I admit that there’s room for argument (and I admit that the reality is a little more nuanced) I still firmly believe that data analysis is the true […]
As a beginning data scientist, you’ll have quite a few subject areas that you need to learn (and eventually master). While you’ll certainly need to learn some math and statistics, math and stats are not the first things I recommend to most beginners. Almost always, I recommend that people start with data visualization. The reason […]
In last week’s blog, I explained why you should Master R (even if it may eventually become obsolete). I wrote that article to address people who claim mastering R is a bit of a waste of time (because it will eventually become obsolete). But when I suggested that R may eventually become obsolete, this seemed […]
In last week’s blog post I asked How much data science do you actually remember? It’s a critical question. If you study data science, but forget everything that you learn, you’ll be in big trouble when you go in for an interview. Or, you’ll be in big trouble if you actually get a data science […]
How many data science books have you read? 5? 10? A few dozen? How many free online courses have you taken? A few? How many blog posts have you read? (I’d be willing to bet: you’ve read dozens.) If you’re like most budding data scientists, you’ve probably consumed a lot of material. You probably even […]