Weekly Round-Up: Getting Started with Data Science, Interview Questions, GE’s Big Data Plans, Lean Startup Experiment Data, and Brain Visualizations

In this first week of 2013, we found a variety of interesting data science articles ranging in topics from some ways to get started in data science and some interview questions you can expect if you land an interview to some future applications for machine-generated data.

In this week’s round-up:

  • Software engineer’s guide to getting started with data science
  • Interview Questions for Data Scientists
  • General Electric lays out big plans for big data
  • Early Evidence Is Often Too Early And Not Really Evidence
  • Scientists construct first map of how the brain organizes everything we see

Software engineer’s guide to getting started with data science

This is a great guide submitted to R-bloggers describing the process and resources the author used to learn the basics of data science. It references the training materials he used for self learning, which classes he took, which books he read, what he found useful, and what didn’t work so well for him. This article is great for those who are interested in getting started in data science as well as those who have learned the basics and are looking for what they should learn next.

Interview Questions for Data Scientists

This is a post by Hilary Mason, Chief Scientist at bitly, about some of the questions she asks data scientist candidates during job interviews. The questions she highlights in this post are the non-technical questions that give her a sense of the applicant’s personality, how passionate they are about the work they’ve done, and how their brain works. For those job-seekers out there, you should be prepared to answer these types of questions in addition to the usual technical ones.

General Electric lays out big plans for big data

This InfoWorld article describes some of the plans GE has announced for combining sensors, data, analytics, and the cloud in a new initiative they call the Industrial Internet. This means making a lot of the processes, products, and solutions across their many lines of business more intelligent. The article also includes a link to a keynote speech from GE CEO Jeff Immelt about the initiative, which can be viewed here.

Early Evidence Is Often Too Early And Not Really Evidence

In this OnStartups article, Dharmesh Shah from HubSpot warns us about the quality of the data that is captured during experiments conducted by startups. Conducting experiments to validate your assumptions, getting customer feedback, and learning from that feedback is crucial to the success of a startup and is a key component of the Lean Startup methodology, but Shah warns that you should make sure you have enough data and that the data is sufficiently clean before making decisions. The article goes into some more detail about this and provides a couple really good examples.

Scientists construct map of how the brain organizes everything we see

This is an interesting project out of UC Berkeley, where scientists have constructed a map of how the brain processes and organizes things that we see. The article includes a video explaining how they captured the data necessary to do this, what they have created from it, and an overview of how they created it. It also includes a link to the awesome visualization that resulted so that you can play around with it yourself.

That’s it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there’s something we missed, feel free to let us know in the comments below.

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Tony Ojeda

Manager, Data Analysis and Strategic Solutions at Follett Higher Education Group
Tony Ojeda is an accomplished data scientist and entrepreneur with expertise in business process optimization and over a decade of experience creating and implementing innovative data products and solutions. He has a Masters in Finance from Florida International University and an MBA with concentrations in Strategy and Entrepreneurship from DePaul University. He is the founder of District Data Labs, a co-founder of Data Community DC, and is actively involved in promoting data science education through both organizations.
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