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Machine Learning & Data Science

Linear Digressions - Old to New

Linear Digressions

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Machine learning and particle physics go together like peanut butter and jelly--but this is a relatively new development.  

For many decades, physicists looked through their fairly large datasets using the laws of physics to guide their exploration; that tradition continues today, but as ever-larger datasets get made, machine learning becomes a more tractable way to deal with the deluge.  

With this in mind, ATLAS (one of the major experiments at CERN, the European Center for Nuclear Research and home laboratory of the recently discovered Higgs boson) ran a machine learning contest over the summer, to see what advances could be found from opening up the dataset to non-physicists.  

The results were impressive--physicists are smart folks, but there’s clearly lots of advances yet to make as machine learning and physics learn from one another.  And who knows--maybe more Nobel prizes to win as well!

https://www.kaggle.com/c/higgs-boson

Hunting for the Higgs

In the 1850s, there were a lot of things we didn’t know yet: how to create an airplane, how to split an atom, or how to control the spread of a common but deadly disease: cholera.  

When a cholera outbreak in London killed scores of people, a doctor named John Snow used it as a chance to study whether the cause might be very small organisms that were spreading through the water supply (the prevailing theory at the time was miasma, or “bad air”).  By tracing the geography of all the deaths from the outbreak, Snow was practicing elementary data science--and stumbled upon one of history’s most famous outliers.  

In this episode, we’ll tell you more about this single data point, a case of cholera that cracked the case wide open for Snow and provided critical validation for the germ theory of disease.

http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak

How Outliers Helped Defeat Cholera

Computers are capable of many impressive feats, but making you laugh is usually not one of them. Or could it be? This episode will talk about a custom-built machine learning algorithm that searches through text and writes jokes based on what it finds. 

The jokes are formulaic: they're all of the form "I like my X like I like my Y: Z" where X and Y are nouns, and Z is an adjective that can describe both X and Y. For (dumb) example, "I like my men like I like my coffee: steaming hot." The joke is funny when ZX and ZY are both very common phrases, but X and Y are rarely seen together. 

So, given a large enough corpus of text, the algorithm looks for triplets of words that fit this description and writes jokes based on them. Are the jokes funny? You be the judge...

http://homepages.inf.ed.ac.uk/s0894589/petrovic13unsupervised.pdf

Computers Try to Tell Jokes

Baseball is characterized by a high level of equality between teams; even the best teams might only have 55% win percentages (contrast this with college football, where teams go undefeated pretty regularly). In this regime, where 2 outcomes (Giants win/Giants lose) are approximately equally likely, we can model the win/loss chances with a binomial distribution. 

Using the binomial distribution, we can calculate an interesting little result: what's the chance of the world series going to only 4 games? 5? 6? All the way to 7? Then we can compare to decades' worth of world series data, to see how well the data follows the binomial assumption. 

The result tells us a lot about sports psychology--if each game is independent of the others, 4/5/6/7 game series are equally likely. The data shows a different trend: 4 and 7 game series are significantly more likely than 5 or 6. There's a powerful psychological effect at play--everybody loves the 7th game of the world series, or a good sweep. And it turns out that the baseball teams, whether they intend it or not, oblige our love of short (4) and long (7) world series!

http://blog.philbirnbaum.com/2007/06/winning-world-series-in-x-games.html

Stats of World Series Streaks

A true classic topic in ML: Facial recognition is very high-dimensional, meaning that each picture can have millions of pixels, each of which can be a single feature. It's computationally expensive to deal with all these features, and invites overfitting problems. PCA (principal components analysis) is a classic dimensionality reduction tool that compresses these many dimensions into the few that contain the most variation in the data, and those principal components are often then fed into a classic ML algorithm like and SVM. 

One of the best thing about eigenfaces is the great example code that you can find in sklearn--you can distinguish pictures of world leaders yourself in just a few minutes!

http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html

Facial Recognition with Eigenfaces

The Medici were one of the great ruling families of Europe during the Renaissance.  How did they come to rule?  Not power, or money, or armies, but through the strength of their social network.  And speaking of great historical social networks, analysis of the network of letter-writing during the Enlightenment is helping humanities scholars track the dispersion of great ideas across the world during that time, from Voltaire to Benjamin Franklin and everyone in between.

Relevant links:
https://www2.bc.edu/~jonescq/mb851/Mar12/PadgettAnsell_AJS_1993.pdf
http://republicofletters.stanford.edu/index.html

Great Social Networks in History

Survival analysis is all about studying how long until an event occurs--it's used in marketing to study how long a customer stays with a service, in epidemiology to estimate the duration of survival of a patient with some illness, and in social science to understand how the characteristics of a war inform how long the war goes on.  This episode talks about the special challenges associated with survival analysis, and the tools that (data) scientists use to answer all kinds of duration-related questions.

Survival Analysis

First up in this episode: a crash course in natural language processing, and important steps if you want to use machine learning techniques on text data.  Then we'll take that NLP know-how and talk about a really cool analysis of State of the Union text, which analyzes the topics and word choices of every President from Washington to Obama.

Relevant link:
https://civisanalytics.com/blog/data-science/2016/01/15/data-science-on-state-of-the-union-addresses/

Text Analysis on the State Of The Union

"People with a family history of heart disease are more likely to eat healthy foods, and have a high incidence of heart attacks."  Did the healthy food cause the heart attacks?  Probably not.  But establishing causal links is extremely tricky, and extremely important to get right if you're trying to help students, test new medicines, or just optimize a website.  In this episode, we'll unpack randomized experiments, like AB tests, and maybe you'll be smarter as a result.  Will you be smarter BECAUSE of this episode?  Well, tough to say for sure...

Relevant link:
http://tylervigen.com/spurious-correlations

Experiments and Messy, Tricky Causality

This episode started out as a discussion of recommendation engines, like Netflix uses to suggest movies.  There's still a lot of that in here.  But a related topic, which is both interesting and important, is how to keep data private in the era of large-scale recommendation engines--what mistakes have been made surrounding supposedly anonymized data, how data ends up de-anonymized, and why it matters for you.

Relevant links:
http://www.netflixprize.com/
http://bits.blogs.nytimes.com/2010/03/12/netflix-cancels-contest-plans-and-settles-suit/?_r=0
http://arxiv.org/PS_cache/cs/pdf/0610/0610105v2.pdf

Recommendation Engines and Privacy

In this session of the Super Data Science Podcast, I chat with business strategy expert Brendan Hogan. You will learn how he uses Data to alter strategy of businesses, you will learn about the three pillars of research and how to build a successful career from your strengths.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/4

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 004 : Data and Strategy, Three Pillars of Research and Building your Career with Brendan Hogan

What do you get when you cross a support vector machine with matrix factorization?  You get a factorization machine, and a darn fine algorithm for recommendation engines.

Factorization Machines

If you've been in data science for more than a year or two, chances are you've noticed changes in the field as it's grown and matured. And if you're newer to the field, you may feel like there's a disconnect between lots of different stories about what data scientists should know, or do, or expect from their job. This week, we cover two thought pieces, one that arose from interviews with 35(!) data scientists speaking about what their jobs actually are (and aren't), and one from the head of data science at AirBnb organizing core data science work into three main specialties.

Relevant links:
https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists
https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal

The Three Types of Data Scientists, and What They Actually Do

Linear regression is a great tool if you want to make predictions about the mean value that an outcome will have given certain values for the inputs. But what if you want to predict the median? Or the 10th percentile? Or the 90th percentile. You need quantile regression, which is similar to ordinary least squares regression in some ways but with some really interesting twists that make it unique. This week, we’ll go over the concept of quantile regression, and also a bit about how it works and when you might use it.

Relevant links:
https://www.aeaweb.org/articles?id=10.1257/jep.15.4.143
https://eng.uber.com/analyzing-experiment-outcomes/

Quantile Regression

It’s not news that data scientists are expected to be capable in many different areas (writing software, designing experiments, analyzing data, talking to non-technical stakeholders). One thing that has been changing, though, as the field becomes a bit older and more mature, is our ideas about what data scientists should focus on to stay relevant. Should they specialize in a particular area (if so, which one)? Should they instead stay general and work across many different areas? In either case, what are the costs and benefits?

This question has prompted a number of think pieces lately, which are sometimes advocating for specializing, and sometimes pointing out the benefits of generalists. In short, if you’re trying to figure out what to actually do, you might be hearing some conflicting opinions. In this episode, we break apart the arguments both ways, and maybe (hopefully?) reach a little resolution about where to go from here.

The Great Data Science Specialist vs. Generalist Debate

We often hear from folks wondering what advice we can give them as they search for their first job in data science. What does a hiring manager look for? Should someone focus on taking classes online, doing a bootcamp, reading books, something else? How can they stand out in a crowd? 

There’s no single answer, because so much depends on the person asking in the first place, but that doesn’t stop us from giving some perspective. So in this episode we’re sharing that advice out more widely, so hopefully more of you can benefit from it.

Advice to those trying to get a first job in data science

In this episode of the SuperDataScience Podcast, I chat with one of the key people behind the Python package scikit-learn, Andreas Mueller. You will learn about gradient boosting algorithms, XGBoost, LightGBM and HistGradientBoosting. You will hear Andreas's approach to solving problems, what machine learning algorithms he prefers to apply to a given data science challenge, in which order and why. You will also hear about problems with Kaggle competitions. You will find out the four key questions that Andreas recommends to ask when you have a data challenge in front of you. You will learn about his 95% rule to creating models, and creating success in business enterprises with the help of machine learning. And, finally, you will also learn about the Data Science Institute at Columbia University.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/283

SDS 283: Getting The Most Out of Data With Gradient Boosting

I sat down with Morgan Mendis whom I met at DSGO this year. He is one of the most advanced data scientists I’ve met and he’s been using his skills and experience to give back to his community. We discuss his career, his dreams, his ideology, and his hunt for a VP of Data Science at his former company.

In this episode you will hear:
• Catch up since DSGO 2019 [8:04]
• VP of Data Science at Inspire [12:00]
• Morgan’s career dreams [22:04]
• Morgan’s experience [30:50]
• Tools & solutions [1:01:33]
• How you can get involved [1:12:45]

Additional materials: www.superdatascience.com/321

SDS 321: The Life of One Advanced Data Scientist

I chatted with top Upwork freelancer Wesley Engers who has worked over 150 jobs in data science. He’s worked in a variety of industries and shared a few of his most interesting jobs and offered advice for those considering diving into freelance data science work.

In this episode you will learn:
• Wesley on Upwork [9:11] 
• Wesley’s background [16:20]
• How Wesley onboards a client [26:32]
• Good clients vs. bad clients [31:12]
• Tools [37:23]
• Wesley’s best projects [45:09]
• Freelance vs. full-time work [59:26]
• Tips about getting into Upwork [1:06:48]

Additional materials: www.superdatascience.com/323

SDS 323: Data Science as a Freelance Career

In today’s FiveMinuteFriday episode, I wanted to experience explaining support vector regression without the use of any visual aids.

Additional materials: www.superdatascience.com/370

SDS 370: What is Support Vector Regression (SVR)?

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

https://hdsr.mitpress.mit.edu/pub/xsrt4zs2/release/2

The Data Science Open Source Ecosystem

This is a re-release of an episode that originally ran in October 2019.

If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”

Procella: YouTube's super-system for analytics data storage

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.

A Data Science Take on Open Policing Data

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be an improvement if the image is now harder to capture (because of strict data quality requirements associated with the algorithm that wouldn’t stop a human doing the same job). Likewise, an algorithm getting a prediction mostly correct might not be an overall benefit if it introduces more dramatic failures when the prediction happens to be wrong. For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems.

A Reality Check on AI-Driven Medical Assistants

All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week. Thanks, best wishes, and good night!

—Katie and Ben