The last few posts at Sharp Sight have been fairly intense. Let’s make a quick map ... The post Mapping unemployment data, 2016 appeared first on SHARP SIGHT LABS. [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

The histogram is a very useful visualization tool, and you need to master it. The post A simple histogram (and why you need to practice it) appeared first on SHARP SIGHT LABS. [Read more...]

In the world of data visualization, the heatmap is underrated and underutilized. It has limitations, but overall, it’s an excellent tool in your data science and data visualization toolkit. After you’ve mastered the foundational visualization techniques (you can write the code for the basic plots in your sleep, ... [Read more...]

A few weeks ago, an acquaintance told me that he was interested in getting started with machine learning. He’s a web developer who primarily works in Ruby and Python, but also has a small amount of experience with R. Day-to-day, his work is run-of-the-mill web development, and he’s ... [Read more...]

Claims of “the end of geography” and the flatness of the world notwithstanding, place still matters today. Discussing why place matters is somewhat beyond the scope of this post, so I will direct you to the excellent work of Parag Khanna and his book Connectography. To put it simply, the ... [Read more...]

In part 1, we went over how to use data visualization and data analysis prior to machine learning. For example, we discussed how to visualize the data to identify potential issues in the dataset, examine the variable distributions, etc. In this blog post, we’ll continue by building a very simple ... [Read more...]

In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is ... [Read more...]

When beginners get started with machine learning, the inevitable question is “what are the prerequisites? What do I need to know to get started?” And once they start researching, beginners frequently find well-intentioned but disheartening advice, like the following: You need to master math. You need all of the following: – ... [Read more...]

Over the last few blog posts, I’ve discussed some of the basics of what machine learning is and why it’s important: – Why machine learning will reshape software engineering – What is the core task of machine learning – How to get started in machine learning in R Throughout those posts, ... [Read more...]

Machine learning is hard. Some people spend weeks, months, even years trying to learn machine learning without any success. They play around with datasets, buy books, compete on Kaggle, but ultimately make little progress. One of the big problems, is that many people just want to “dive in and build ... [Read more...]

If you’ve been using R for a while, and you’ve been working with basic data visualization and data exploration techniques, the next logical step is to start learning some machine learning. To help you begin learning about machine learning in R, I’m going to introduce you to ... [Read more...]

A Sharp Sight Labs reader (and now student), Jason P. recently started learning data science. He has a background in data analysis (primarily with Excel and related tools in the Microsoft ecosystem) but he wanted to start learning some of the harder skills of data science. He contacted me after ... [Read more...]

One of the biggest issues that comes up when I talk to people who want to get started learning data science is the following: I don’t know where to get started! Recently, I argued that R is the best programming language to learn when you’re getting started with ... [Read more...]

Over and over, when talking with people who are starting to learn data science, there’s a frustration that comes up: I don’t know which programming language to start with.” And it’s not just programming languages, it’s also software systems like Tableau, SPSS, etc. There is an ... [Read more...]