Data are becoming the new raw material of business
The Economist

Continuing Your Data Science Job Hunt Through the Holidays

xmas-wishThe holidays can seem like a tough time to job search, people are out of the office and holiday schedules are hectic.  But there are lots of things you can do to take advantage of this time, keep your search moving forward, and set yourself up for post holiday success. 

  1. Review your skill set

Read through every job description you can, even the ones for jobs that didn’t originally interest you. Where are your skills gaps? If you see fluency in C++ in one or two job descriptions, but not on most, you might be okay not knowing it well. But if you see fluency in C++ listed over and over, the next few weeks are a great time for you to work on learning it.

  1. Take on a new project

One of the best things you can do to really master those new skills (and demonstrate your knowledge) is to apply them. We’ve been publishing links to lots of publically available data sets on our blog. Take one and treat it as a case study, what problem might this company or organization have, and how can you use data science to solve it? You can add your work to your github, blog about it, or share it on your LinkedIn! These are publically available data sets, so definitely show off your work.

Continue reading

Tweet about this on TwitterShare on FacebookShare on LinkedIn

SAS Pain Points

Having trouble with SAS?  Check out this handy video by our former fellow Paul Paczuski.

Tweet about this on TwitterShare on FacebookShare on LinkedIn

The Details of Data Science: Alumni Spotlight on Ellen Ambrose

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Ellen was a Fellow in our Spring 2016 cohort who landed a job with one of our hiring partners, Protenus.

Tell us about your background. How did it set you up to be a great data scientist? 

The title data scientist implies both technical skills, as well as the ability to ask and answer questions scientifically. My academic decisions were driven by the desire to develop the second of these skills. I studied math in college because I was attracted to the logical way of thinking and proving concepts that I encountered in math class. I went on to do a PhD in neuroscience in part because I wanted to pursue a question to an extreme level of detail, leveraging the logic that I learned doing math. (I also wanted to understand how learning works at the level of neural ensembles, but that’s another story.)

As result of my academic trajectory I also learned to write analysis code. I enjoyed coding, and at first I considered it a perk of my particular field of neuroscience that a lot of coding (mostly in Matlab) was necessary for analyzing the large datasets I was collecting. However, I eventually came to appreciate coding in it’s own right and started taking steps to learn new languages and to improve my analyses by incorporating better tools. By this time I had realized that I would be happy doing coding full time, so The Data Incubator was a great segue way to new concepts and tools in the world of data science.
Continue reading

Tweet about this on TwitterShare on FacebookShare on LinkedIn

Crunching Yelp Data to a Job at Crunchbase: Alumni Spotlight on Newton Le

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Newton was a Fellow in our Summer 2016 cohort who landed a job with one of our hiring partners, Crunchbase.

Tell us about your background. How did it set you up to be a great data scientist? 

I have an electrical engineering and computer science degree from UC Berkeley, which gave me a strong coding foundation. I am also almost done with a PhD in structural engineering at UC Davis, which gave me a lot of experience solving analytical problems computationally. Continue reading

Tweet about this on TwitterShare on FacebookShare on LinkedIn

Data Sources for Cool Data Science Projects Part 6

Links to Part 1, Part 2Part 3Part 4 , Part 5

At The Data Incubator, we run a free six week data science fellowship to help our Fellows land industry jobs. Our hiring partners love considering Fellows who don’t mind getting their hands dirty with data.  That’s why our Fellows work on cool capstone projects that showcase those skills.  One of the biggest obstacles to successful projects has been getting access to interesting data.  Here are a few cool public data sources you can use for your next project:

Continue reading

Tweet about this on TwitterShare on FacebookShare on LinkedIn

Data Science Project Ideas

We love data science and cool data science projects.  If you’re a applying for our free data science fellowship and looking to propose a data science project here are four project ideas.

Github

GitHub is a great source of data on how engineers write code.  A recent post found discrimination against Pull Requests submitted by women on GitHub, although perhaps that study could have been better.  But there are lots of other ideas to pursue.  We can easily learn an n-gram classifier on whether a line of code is a comment or not and search for commented out code.  Are repos by academics more likely to have commented out code?  Are they more likely to violate lint rules?  Additionally, it would be interesting to analyze commits that are in response to bug fixes to predict which lines of code bugs are more likely to occur in.

Continue reading

Tweet about this on TwitterShare on FacebookShare on LinkedIn

Data Scientist Salaries

At The Data Incubator we’ve worked with hundreds of fellows looking to enter industry and our alumni work at companies including LinkedIn, Palantir, Amazon, Capital One, and the NYTimes.  

Starting salary is one of the most common concerns for professionals entering any field, but as we’ve only been using the job title “Data Scientist” for about eight years it can be particularly challenging for prospective data scientists to find good information on their job market. LinkedIn and Facebook were the first to give employees on their data teams the title of data scientist, but now there are thousands of data scientists working across all industries alongside data engineers, data analysts, and quantitative analysts.

Continue reading

Tweet about this on TwitterShare on FacebookShare on LinkedIn

Data Sources for Cool Data Science Projects: Part 5

Links to Part 1, Part 2Part 3, Part 4 

At The Data Incubator, we run a free six week data science fellowship to help our Fellows land industry jobs. Our hiring partners love considering Fellows who don’t mind getting their hands dirty with data.  That’s why our Fellows work on cool capstone projects that showcase those skills.  One of the biggest obstacles to successful projects has been getting access to interesting data.  Here are some more cool public data sources you can use for your next project:

Continue reading

Tweet about this on TwitterShare on FacebookShare on LinkedIn

Data Sources for Cool Data Science Projects: Part 4

Links to Part 1, Part 2, Part 3

At The Data Incubator, we run a free six week data science fellowship to help our Fellows land industry jobs. Our hiring partners love considering Fellows who don’t mind getting their hands dirty with data.  That’s why our Fellows work on cool capstone projects that showcase those skills.  One of the biggest obstacles to successful projects has been getting access to interesting data.  Here are some more cool public data sources you can use for your next project: Continue reading

Tweet about this on TwitterShare on FacebookShare on LinkedIn

3 Reasons You Should Attend a Data Conference

This piece was written by The Data Incubator’s, Megan Cummings

Virtually every week across the globe there is a high-quality data conference happening. Each year, you should take a moment to analyze your budget and availability to find a conference that would best fit your needs. Whether you’re a computer science student, entry level data scientist, Chief Technology Officer, or CEO, attending a conference is an essential key to career development, growing your professional network, and staying on top of the latest trends. So if at any point you’ve ever been hesitant to register for a conference, here are the top three takeaways:

Learning

This is the most obvious benefit of attending a conference and the sole reason why some people choose to attend. There’s something new to learn in the data science universe every day, and many of these conferences are at the foreground of these cutting-edge changes. Conferences are where you can access the most up to date industry information, oftentimes from the sources themselves. With hundreds, sometimes thousands of attendees, you can also gain exposure to a boundless variety of new ideas and trends that can benefit your latest project or business venture. When planning out your conference schedule for the year, be picky and selective about which ones to dedicate your time and money to by first asking yourself what you hope to learn from each one. Take time to research the speakers, break out sessions, and workshops and make a schedule far in advance to ensure you’re getting the most out of the conference as possible. On top of that, make sure what you’re taking away is beneficial for your organization, professional development, or career growth.

Continue reading

Tweet about this on TwitterShare on FacebookShare on LinkedIn