Win-Vector Blog

When people ask me what it means to be a data scientist, I used to answer, “it means you don’t have to hold my hand.” By which I meant that as a data scientist (a consulting data scientist), I can handle the data collection, the data cleaning and wrangling, the analysis, and the final presentation of results (both technical and for the business audience) with a minimal amount of assistance from my clients or their people. Not no assistance, of course, but little enough that I’m not interfering too much with their day-to-day job.

This used to be a key selling point, because people with all the necessary skills used to be relatively rare. This is less true now; data science is a hot new career track. Training courses and academic tracks are popping up all over the place. So there is the question: what should such courses teach? Or more to the heart of the question — what does a data scientist do, and what do they need to know?

Read more…

Related posts:

1. Data Science, Machine Learning, and Statistics: what is in a name?
2. Data science project planning
3. Setting expectations in data science projects
4. Your Data is Never the Right Shape
5. Book Review: Ensemble Methods in Data Mining (Seni & Elder)

This was originally posted at ninazumel.com. I’m re-blogging it here.

Photo: John Mount

I came across a post from Emily Willingham the other day: “Is a PhD required for Good Science Writing?”. As a science writer with a science PhD, her answer is: is it not required, and it can often be an impediment. I saw a similar sentiment echoed once by Lee Gutkind, the founder and editor of the journal Creative Nonfiction. I don’t remember exactly what he wrote, but it was something to the effect that scientists are exactly the wrong people to produce literary, accessible writing about matters scientific.

I don’t agree with Gutkind’s point, but I can see where it comes from. Academic writing has a reputation for being deliberately obscure and prolix, jargonistic. Very few people read journal papers for fun (well, except me, but I’m weird). On the other hand, a science writer with a PhD has been trained for critical thinking, and should have a nose for bullpucky, even outside their field of expertise. This can come in handy when writing about medical research or controversial new scientific findings. Any scientist — any person — is going to hype up their work. It’s the writer’s job to see through that hype.

I’m not a science writer in the sense that Dr. Willingham is. I write statistics and data science articles (blog posts) for non-statisticians. Generally, the audience that I write for is professionally interested in the topic, but aren’t necessarily experts at it. And as a writer, many of my concerns are the same as those of a popular science writer.

I want to cut through the bullpucky. I want you, the reader, to come away understanding something you thought you didn’t — or even couldn’t — understand. I want you, the analyst or data science practitioner, to understand your tools well enough to innovate, not just use them blindly. And if I’m writing about one of my innovations, I want you to understand it well enough to possibly use it, not just be awed at my supposed brilliance.

I don’t do these things perfectly; but in the process of trying, and of reading other writers with similar objectives, I’ve figured out a few things.

Read more…

Related posts:

1. What does a generalized linear model do?
2. Kernel Methods and Support Vector Machines de-Mystified
3. Public Service Article: JSTOR and other Useful Research Archives
4. A Personal Perspective on Machine Learning

A recent run of too many articles on the same topic (exhibits: A, B and C) puts me in a position where I feel the need to explain my motivation. Which itself becomes yet another article related to the original topic. The explanation I offer is: this is the way mathematicians think. To us mathematicians the tension is that there are far too many observable patterns in the world to be attributed to mere chance. So our dilemma is: for which patterns/regularities should we derive some underlying law and which ones are not worth worrying about. Or which conjectures should try to work all the way to proof or counter-example? Read more…

Related posts:

1. The Joy of Calculation
2. What is Mathematics, Really?
3. Kernel Methods and Support Vector Machines de-Mystified
4. YAYGDA (Yet Another Yahoo Google Deal Article)
5. Ergodic Theory for Interested Computer Scientists

Hollywood movies are obsessed with outrunning explosions and outrunning crashing alien spaceships. For explosions the movies give the optimal (but unusable) solution: run straight away. For crashing alien spaceships they give the same advice, but in this case it is wrong. We demonstrate the correct angle to flee.

Related posts:

1. The Joy of Calculation
2. Lanchester’s Law: why small advantages swell in StarCraft
3. Kernel Methods and Support Vector Machines de-Mystified
4. “Easy” Portfolio Allocation
5. Must Have Software

We are very excited to announce a new Win-Vector LLC blog category tag: Pragmatic Machine Learning. We don’t normally announce blog tags, but we feel this idea identifies an important theme common to a number of our articles and to what we are trying to help others achieve as data scientists. Please look for more news and offerings on this topic going forward. This is the stuff all data scientists need to know.

Related posts:

1. A Personal Perspective on Machine Learning
2. The differing perspectives of statistics and machine learning
3. Kernel Methods and Support Vector Machines de-Mystified
4. Setting expectations in data science projects
5. My Favorite Graphs

In both working with and thinking about machine learning and statistics I am always amazed at the differences in perspective and view between these two fields. In caricature it boils down to: machine learning initiates expect to get rich and statistical initiates expect to get yelled at. You can see hints of what the practitioners expect to encounter by watching their preparations and initial steps. Read more…

Related posts:

1. A Personal Perspective on Machine Learning
2. Why you can not to use statistics to dispute magic
3. Statistics to English Translation, Part 2b: Calculating Significance
4. Book Review: Ensemble Methods in Data Mining (Seni & Elder)
5. Statistics to English Translation, Part 2a: ’Significant’ Doesn’t Always Mean ’Important’

A big congratulations to Win-Vector LLC‘s Dr. Nina Zumel for authoring and teaching portions of EMC‘s new Data Science and Big Data Analytics training and certification program. A big congratulations to EMC, EMC Education Services and Greenplum for creating a great training course. Finally a huge thank you to EMC, EMC Education Services and Greenplum for inviting Win-Vector LLC to contribute to this great project.

Read more…

Related posts:

1. Setting expectations in data science projects
2. Modeling Trick: Masked Variables
3. My Favorite Graphs
4. What does a generalized linear model do?
5. The cranky guide to trying R packages

How is it even possible to set expectations and launch data science projects?

Data science projects vary from “executive dashboards” through “automate what my analysts are already doing well” to “here is some data, we would like some magic.” That is you may be called to produce visualizations, analytics, data mining, statistics, machine learning, method research or method invention. Given the wide range of wants, diverse data sources, required levels of innovation and methods it often feels like you can not even set goals for data science projects.

Many of these projects either fail or become open ended (become unmanageable).

As an alternative we describe some of our methods for setting quantifiable goals and front-loading risk in data science projects. Read more…

Related posts:

1. Data science project planning
2. On Being a Data Scientist
3. Don’t use correlation to track prediction performance
4. Level fit summaries can be tricky in R
5. More on ROC/AUC

A lot of people consider the static typing found in languages such as C, C++, ML, Java and Scala as needless hairshirtism. They consider the dynamic typing of languages like Lisp, Scheme, Perl, Ruby and Python as a critical advantage (ignoring other features of these languages and other efforts at generic programming such as the STL).

I strongly disagree. I find the pain of having to type or read through extra declarations is small (especially if you know how to copy-paste or use a modern IDE). And certainly much smaller than the pain of the dynamic language driven anti-patterns of: lurking bugs, harder debugging and more difficult maintenance. Debugging is one of the most expensive steps in software development- so you want incur less of it (even if it is at the expense of more typing). To be sure, there is significant cost associated with static typing (I confess: I had to read the book and post a question on Stack Overflow to design the type interfaces in Automatic Differentiation with Scala; but this is up-front design effort that has ongoing benefits, not hidden debugging debt).

There is, of course, no prior reason anybody should immediately care if I do or do not like dynamic typing. What I mean by saying this is I have some experience and observations about problems with dynamic typing that I feel can help others.

I will point out a couple of example bugs that just keep giving. Maybe you think you are too careful to ever make one of these mistakes, but somebody in your group surely will. And a type checking compiler finding a possible bug early is the cheapest way to deal with a bug (and static types themselves are only a stepping stone for even deeper static code analysis). Read more…

Related posts:

1. Programmers Should Know R
2. Automatic Differentiation with Scala
3. My Favorite Graphs
4. Thievery considered harmful
5. I know, I am the one being a jerk

It is a subtle point that statistical modeling is different than model based science. However, empirical scientists seem to go out of their way to conflate the two before the public (as statistical modeling is easier to perform and model based science is more highly rewarded). It is often claimed that model based science is being done when in fact statistics is what is being done (for instance some of the unfortunate distractions of flawed reports related to the important question of the magnitude of plausible anthropogenic global warming).

Both model based science and statistics are wonderful fields, but it is important to not receive the results of one when you have paid for the other.

We will pointedly discuss one of the differences. Read more…

Related posts:

1. Statistics to English Translation, Part 2a: ’Significant’ Doesn’t Always Mean ’Important’
2. Statistics to English Translation, Part 2b: Calculating Significance
3. The differing perspectives of statistics and machine learning
4. “I don’t think that means what you think it means;” Statistics to English Translation, Part 1: Accuracy Measures
5. Programs reduced to statistics