Introducing: Data Analysis Studio for Summer 2016

We cover a lot of ground in the Lede Program – you might start without knowing much at all about your computer, and by the end you’re using the command line to run Python machine learning libraries, hex-binning geocoded CSV files, fetching quartiles out of dataframes, and running joins on SQL databasesThat’s a lot of tech, really really quickly.

Any time you have that many tools at your disposal, three big questions appear

  1. When should I use what tool?
  2. How do I keep it all straight?
  3. What if I want to learn more about X?

To focus on answering these questions we’re introducing a new course for Summer 2016: Data Analysis Studio, a project-based course focused on beginning-to-end workflow and applying your newfound skills.

You’ll spend seven weeks crafting projects from start to finish, learning new skills and refining the ones you’ve gained through your other coursework. Frequent critiques will not only make your projects stronger, but also introduce you and other students to new methods of analyzing data, building visuals and avoiding common pitfalls.

Let’s take a look at how Data Analysis Studio answers those “big three questions.”

When should I use what tool?

Early on in your data career, a lot of tools look the same, and much of their nuance is still hidden. For example, if you’re storing data – when is a CSV file best? What about a SQL database? Why would I need a shapefile? While in-class explanations and working through assignments helps to learn the basics, it can be difficult to understand the real-world implications of your choices until you’ve actually made them.

Data Analysis Studio not only gets you practice using these skills in a realistic environment, but feedback from instructors and visiting professionals keeps you moving in the right direction as you master the tools at your disposal. Multiple students often have similar problems that they approach with radically different solutions – project presentations and critique sessions help you share in their experience.

How do I keep it all straight?

Practice, practice, practice.

The first time you’re trying to convert between two file formats or attempting to understand an error message, it might take you a half-hour of googling. The next time you still don’t know offhand, but you’ll find the answer in 3 minutes. After that finding the solution might take 10 seconds of searching, or maybe you’ve made yourself a little note to yourself about how to solve the problem.

Homework is fantastic for getting you acquainted with a tool or concept, but only by completing projects again and again will you internalize what it takes to use those tools and concepts to build a project. By leveraging multiple start-to-finish projects, Data Analysis Studio helps solidify your skillset.

What if I want to learn more about X?

One of the most rewarding parts of the Lede Program is watching students find a technology that they love using, and seeing how far they run with it. Maybe a 90-minute introduction to d3 will start them running until they get to force layouts, or an introduction to CartoDB will end up with a time-lapse, animated Torque map.

Data Analysis Studio gives students the freedom to expand in the direction they’d like to go. Maybe you’re interested in data visualization, or textual analysis, or scraping documents from the web. By focusing on personal projects and refining skills learned in the other three summer courses, Data Analysis Studio provides a scaffolding to expand on the tools you’re interested in.

Sound good? Apply now!

If that sounds like something you’d be interested in, feel free to explore our site or hop on over to our application page. We hope to see you this summer!

If you’re curious in learning more about the intersection of data, coding and visualization, check out the Lede Program – an intensive certification program at Columbia’s School of Journalism, in conjunction with the Department of Computer Science. Find out more on our mail page – applications are open soon!

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