If you are interested in data science as a career, or just want to know what exactly a data scientist does, read this recent article from LifeHacker. Dan Mallinger, who works with Think Big’s data science practice, gives an overview of how he got to his position, what tools he uses to do his job every day, expectations on types of work, hours, and pay, and more. This is well worth a read.

Data Visualizations

Want to learn about data visualization? You should have been at the ICEBox in Syracuse University’s School of Information Studies last week. Billy Ceskavich, a graduate student in the Information Management program, gave an overview of using R, Adobe Illustrator, and D3.js for data visualization. If you weren’t there, you can see his presentation and associated code here.

The Most Common* Job In Every State - Source: NPR

The Most Common* Job In Every State – Source: NPR

For inspiration, here are a couple of well done visualizations from the past week. First, NPR shows the most popular job in every state since 1978. It is fascinating to see how these jobs have shifted in location, but also how consistent some of them are. Farmers, truck drivers, and secretaries all stand the test of time.

Where There Are More Single Men Than Women  - Source: CityLab

Where There Are More Single Men Than Women – Source: CityLab

CityLab shows which cities have more single men versus more single women. At first glance, the east coast seems to have many more single women, while the west coast has more single men. But CityLab dives deeper into the data and shows that there are more interesting facts at play. At younger ages, single men almost always outnumber single women. Once the age range gets older, single women begin to outnumber single men. This shows how important it is to continuously ask questions of data to make sure you are discovering the most accurate findings.

Buckets by Peter Beshai

Buckets by Peter Beshai

If you are interested in NBA data, a website called Buckets by Peter Beshai gives you all you could ask for. The site lets you see any NBA player’s shot accuracy based on distance and location relative to the basket. You can also see how players compare to each other, and look at information over any of the last four years. Carmelo Anthony shoots at a higher percentage than the average NBA player when he is eight or 21 feet from the basket.

The NBA data is incredible, and you can easily lose hours looking through all the information. Don’t tell Charles Barkley that you like analytics and basketball, though. This week, he said, “I’ve always believed analytics was crap.” FiveThirtyEight did a great writeup about why it is ironic that Barkley hates analytics – people that love analytics actually think Barkley was a much better player than he was given credit for. No word on how FiveThirtyEight feels about Derrick Coleman, though, who was similarly critical of analytics in basketball.

Data Podcasts

FiveThirtyEight is trying out podcasting. They have released a couple of episodes covering both sports and politics. They are well-worth a listen, and it will be exciting when they begin producing episodes more regularly.

Radiolab did a podcast entitled “The Trust Engineers” this week which focused on how Facebook uses data to try to make the internet a happier place. The issue is that they are using all Facebook users as guinea pigs in their experiments, and not everyone supports it. This is a great listen whether you are interested in how Facebook uses some of the data it collects, how Facebook’s access to large sample sizes blows normal social science tests out of the water, or the ethics of using data in experiments.

What kind of data news have you heard about lately? Let us know what you think of these reports – or others you’ve discovered – below in the comments!