Data are becoming the new raw material of business
The Economist

# What is the probability of winning the Hamilton lottery?

People interested in seeing the Broadway musical Hamilton — and there are still many of them, with demand driving starting ticket prices to $\$600$— can enter Broadway Direct’s daily lottery. Winners can receive up to 2 tickets (out of 21 available tickets) for a total of$\$10$.

What’s the probability of winning?

How easy is it to win these coveted tickets? Members of NYC’s Data Incubator Team have collectively tried and failed 120 times. Given our data, we cannot simply divide the number of successes by the number of trials to calculate our chances of winning — we would get zero (and the odds, which are apparently small, are clearly non-zero).

This kind of situation often comes up under many guises in business and big data, and because we are a data science corporate training company, we decided to use statistics to determine the answer. Say you are measuring the click-through-rate of a piece of organic or paid content, and out of 100 impressions, you have not observed any clicks. The measured CTR is zero but the true CTR is likely not zero. Alternatively, suppose you are measuring the rate of adverse side effects of a new drug. You have tested 40 patients and haven’t found any, but you know the chance is unlikely to be zero. So what are the odds of observing a click or a side effect?  Continue reading

# Standing Out as a STEM Graduate: Alumni Spotlight on Bernard Beckerman

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. Bernard was a Fellow in our Fall 2016 cohort who landed a job with Uptake.

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

I studied Materials Science and Engineering at Northwestern University for my PhD. Graduate school prepared me with an array of technical skills including programming, statistical analysis, and the ability to build, communicate, and defend a scientific argument. These are all important in producing data science products and presenting them to those at all levels of a corporate structure.

What do you think you got out of The Data Incubator?

TDI helped me leverage my programming and critical thinking skills toward a career in data science by giving me essential skills and project experience that made me stand out from other advanced-degree STEM graduates. These include machine learning, parallel programming, and interactive data visualization. TDI also connected me to a cohort of accomplished students that has been a great support as I’ve started my career.  Continue reading

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# The Science of Data Science: Alumni Spotlight on Paul George

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. Paul was a Fellow in our Fall 2016 cohort who landed a job with Cloudera.

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

Following the completion of my PhD in Electrical and Computer Engineering in 2009, I joined Palantir Technologies as a Forward Deployed Engineer (client-facing software engineer). There, I helped Palantir enter a new vertical, that of Fortune 500 companies, where I built data integration and analysis software for novel commercial workflows. I left Palantir in 2012 and in 2013 I co-founded SolveBio, a genomics company whose mission is to help improve the variant-curation process; the process by which clinicians and genetic counselors research genetic mutations and label them as pathogenic, benign, or unknown. At SolveBio, my work was primarily focused on building scalable data cleansing, transformation and ingestion infrastructure that could be used to power the SolveBio genomics API. I also worked closely with geneticists and other domain experts in a semi-client-facing role.

The theme of my six years as a software engineer has been to help domain experts, whether they be fraud investigators at a bank or clinicians at a hospital, analyze disparate data to make better decisions. I have built infrastructure in both Java and Python, have used large SQL and NoSQL databases, and have spent countless hours perfecting Bash hackery (or wizardry, depending on your perspective).

My experiences as a software engineer were very relevant to data science in that I learned many ways to access, manipulate, and understand a variety of datasets from a variety of sources in a variety of formats. As the adage goes, “Garbage in. Garbage out.” No more is this true than in data science. Performing good data science requires cleaning and organizing data, and I feel very comfortable with this process.

# The Iraq War by the Numbers: Extracting the Conflicts’ Staggering Costs

One of our fellows recently had a piece published about her very unique and timely capstone project. The original piece is posted on Data Driven Journalism

In her own words:

This war is not only important due to its staggering costs (both human and financial) but also on account of its publicly available and well-documented daily records from 2004 to 2010.

These documents provide a very high spatial and temporal resolution view of the conflict. For example, I extracted from these government memos the number of violent events per day in each county. Then, using latent factor analysis techniques, e.g. non-negative matrix factorization, I was able to cluster the top three principal war zones. Interestingly these principal conflict zones were areas populated by the three main ethno-religious groups in Iraq.

You can watch her explain it herself:

Editor’s Note: The Data Incubator is a data science education company.  We offer a free eight-week fellowship helping candidates with PhDs and masters degrees enter data science careers.  Companies can hire talented data scientists or enroll employees in our data science corporate training.

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# From Astronomy to AI: Alumni Spotlight on Athena Stacy

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. Athena was a Fellow in our Fall 2016 cohort who landed a job with Brighterion.

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

My background is in astronomy.  My research consisted of developing and performing computer simulations of star formation in the early universe.  The goal of these simulations was to better understand what stellar clusters looked like in regions of the universe that telescopes cannot observe.  Thus I was already familiar with computer programming and visualizing data.  This was very helpful in the transition to data science.  Knowing how to present my research clearly to a range of audiences — both beginning students and other experts in the field — has helped as well!

What do you think you got out of The Data Incubator?

Tons!  Just about everything I know about machine learning I learned at TDI.  I met lots of great, friendly, and supportive people through TDI as well.  This includes the instructors and mentors as well as the other fellows in my cohort, many of which I’m sure I will keep up with for many years to come.   Through TDI  I’ve also made contacts with other companies and data scientists in the San Francisco Bay area, which has been quite helpful in getting those job interviews!  Continue reading

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# Parallelizing Jupyter Notebook Tests

## How we cut our end-to-end test suite runtime by 66% using parallelism

While there’s a common stereotype that data scientists are poor software engineers, at The Data Incubator, we believe that mastering the fundamentals of software engineering is important for data science and we strive to implement rigorous engineering standards for our data science company.  We have an extensive curriculum for data science corporate training, data science fellowship, and online data science course leveraging the jupyter (née ipython) notebook format.  Last year, we published a post about testing Jupyter notebooks — applying rigorous software engineering testing standards to new technologies popular in data science.

However, over time, as our codebase as grown, we’ve added in more and more notebooks to our curriculum material. This led to tests on our curriculum taking ~30 minutes to run! We quickly identified parallelism as a low-hanging fruit that would make sense for a first approach, with a couple of points:

1. We have curriculum materials that run code in Spark 2.0 parallelizing runs in that kernel is hard because of how the Spark execution environment spins up.  We also have curriculum materials in the jupyter R Kernel.
2. Subprocess communication in Python (what our testing code is written in) is a pain, so maybe there’s a way to use some other parallelization library to avoid having to reinvent that wheel.
3. Most of our notebooks are in Python, so those shouldn’t have any issues.

These issues aside, this seemed like a reasonable approach because each Jupyter notebook executes as its own subprocess in our current setup – we just had to take each of those processes and run them at the same time. Taking a stab at 3., parallelizing python tests, while finding a way around 2. – annoying multiprocess communication issues – yielded great results!  Continue reading

# Polling and big data in the age of Trump, Brexit, and the Colombian Referendum

Our founder, Michael Li, recently collaborated with his colleague Raymond Perkins, a researcher and PhD candidate at Princeton University, on this piece about big data and polling. You can find the original article at Data Driven Journalism.

The recent presidential inauguration and the notably momentous election that preceded it has brought about numerous discussions surrounding the accuracy of polling and big data. The US election results paired with those of Brexit, and the Colombian Referendum have left a number of people scratching their heads in confusion. Statisticians, however understand the multitude of sampling biases and statistical errors than can ensue when your data is involving human beings.

“Though big data has the potential to virtually eliminate statistical error, it unfortunately provides no protection against sampling bias and, as we’ve seen, may even compound the problem. This is not to say big data has no place in modern polling, in fact it may provide alternative means to predict election results. However, as we move forward we must consider the limitations of big data and our overconfidence in it as a polling panacea.”

At The Data Incubator, this central misconception about big data is one of the core lessons we try to impart on our students. Apply to be a Fellow today!

Editor’s Note: The Data Incubator is a data science education company.  We offer a free eight-week fellowship helping candidates with PhDs and masters degrees enter data science careers.  Companies can hire talented data scientists or enroll employees in our data science corporate training.

# How Employers Judge Data Science Projects

One of the more commonly used screening devices for data science is the portfolio project.  Applicants apply with a project that they have showcasing a piece of data science that they’ve accomplished.  At The Data Incubator, we run a free eight week fellowship helping train and transition people with masters and PhD degrees for careers in data science.  One of the key components of the program is completing a capstone data science project to present to our (hundreds of) hiring employers.  In fact, a major part of the fellowship application process is proposing that very capstone project, with many successful candidates having projects that are substantially far along if not nearly completed.  Based on conversations with partners, here’s our sense of priorities for what makes a good project, ranked roughly in order of importance:

1. Completion: While their potential is important, projects are assessed primarily based on the success of analysis performed rather than the promise of future work.  Working in any industry is about getting things done quickly, not perfectly, and projects with many gaps, “I wish I had time for”, or “ future steps” suggests the applicant may not be able to get things done at work.
2. Practicality: High-impact problems of general interest are more interesting than theoretical discussions on academic research problems. If you solve the problem, will anyone care? Identifying interesting problems is half the challenge, especially for candidates leaving academia who must disprove an inherent “academic” bias.
3. Creativity: Employers are looking for creative, original thinkers who can identify either (1) new datasets or (2) find novel questions to ask about a dataset. Employers do not want to see the tenth generic presentation on Citibike (or Chicago CrimeNYC Taxi, BTS Flight Delay, Amazon Review, ) data. Similarly, projects that explain a non-obvious thesis supported by concise plots are more compelling than ones that present obvious conclusions (e.g. “more riders use Citibike during the day than at night”). Employers are looking for data scientists who can find trends in the data that they don’t already know. Continue reading

# Search results: Careers in high tech

I was recently interviewed for a piece for ScienceMag about careers in high tech. You can find the original post on ScienceMag.

With big data becoming increasingly popular and relevant,  data scientist jobs are opening up across every industry in virtually every corner of the globe. Unfortunately, the multitude of available positions isn’t making it any easier to actually land a job as a data scientist. Competition is abundant, interviews can be lengthy and arduous, and good ideas aren’t enough to get yourself hired. Michael Li  emphasizes that technical know-how is what hiring managers crave. “No one needs just an ‘ideas’ person. They need someone who can actually get the job done.”

This shouldn’t discourage anyone from pursuing a career in data science because it can be both rewarding and profitable. If you’re looking to brush up your skills and jump start your career, consider applying for our free data science fellowship with offerings now in San Francisco, New York, Washington DC, Boston, and Seattle. Learn more and apply on our website.

# Tying Together Elegant Models: Alumni Spotlight on Brendan Keller

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. Brendan was a Fellow in our Fall 2015 cohort who landed a job with one of our hiring partners, Jolata.

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

I did my PhD research in theoretical condensed matter physics at the University of California, Santa Barbara. The focus of my research was on studying the phase diagram of chains of non-abelian anyons. Because such chains are gapless in most regions of the phase diagram we had to model them using very large matrices in C++. To make this computation more tractable we used hash tables and sparse matrices.  Besides my background in numerics I also took the time to learn Python, Pandas, SQL and MapReduce in Cloudera a few months before starting the fellowship.

What do you think you got out of The Data Incubator?

The Data Incubator gave me a solid foundation in data parsing, large scale data analysis and machine learning. I went into the fellowship already knowing about various concepts like SVM, bag-of-words and cross-validation. But I learned how tie these together into a elegant models that are both modular and easy to modify or upgrade. I also learned how to use Map Reduce on a cluster where the behavior of your program can be quite different then on a single node.  Continue reading

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