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Category: Data Science

Storytelling With Data

Published 2016-07-25 by Kevin Feasel

Vik Paruchuri walks through exploratory data analysis using New York City schools data:

Heatmaps are good for mapping out gradients, but we’ll want something with more structure to plot out differences in SAT score across the city. School districts are a good way to visualize this information, as each district has its own administration. New York City has several dozen school districts, and each district is a small geographic area.

We can compute SAT score by school district, then plot this out on a map. In the below code, we’ll:

  • Group full by school district.

  • Compute the average of each column for each school district.

  • Convert the school_dist field to remove leading 0s, so we can match our geograpghic district data.

Also check out part 1 if you missed it.

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Clustering With Spark

Published 2016-07-22 by Kevin Feasel

Konur Unyelioglu shows how to implement k-means and Guassian clustering techniques in Apache Spark using MLlib:

Clustering is the task of assigning entities into groups based on similarities among those entities. The goal is to construct clusters in such a way that entities in one cluster are more closely related, i.e. similar to each other than entities in other clusters. As opposed to classification problems where the goal is to learn based on examples, clustering involves learning based on observation. For this reason, it is a form of unsupervised learning task.

There are many different clustering algorithms and a central notion in all of those is the definition of ’similarity’ between the entities that are being grouped. Different clustering algorithms may have different ways of measuring the similarity. In many clustering algorithms, another common notion is the so-called cluster center, which is a basis to represent the cluster. For example, in K-means clustering algorithm, the cluster center is the arithmetic mean position of all the points in that cluster.

This is a fairly lengthy article but if you want to get into machine learning with Spark, it’s a good one.

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Data Cleansing Outside Of Excel

Published 2016-07-22 by Kevin Feasel

Lee Baker shows some free alternatives to Excel for data cleansing:

Another issue is that some Excel functions operate on selected data, whereas others act on the whole worksheet. If you select a column of data and use Find to identify certain characters, it will identify only those characters in your chosen column. If you now use Replace it will change all such characters in the entire worksheet – which is probably not what you wanted to do, and you may have unwittingly introduced new errors into your data without being aware of it.

The safest way to clean your data in Excel is to copy an individual column to a separate worksheet, perform all your cleaning operations in isolation until you’re happy with the result, then copy your cleaned data to your original sheet (or better still, to a new sheet that stores only clean data). The repeated use of Copy, Paste and using multiple worksheets to clean your data can become extremely messy.

Lee recommends three free tools, and they look like they’re worth trying out.

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StackLite Dataset

Published 2016-07-22 by Kevin Feasel

David Robinson reports on a new Stack Exchange data set available to the public:

For each Stack Overflow question asked since the beginning of the site, the dataset includes:

  • Question ID
  • Creation date
  • Closed date, if applicable
  • Deletion date, if applicable
  • Score
  • Owner user ID (except for deleted questions)
  • Number of answers
  • Tags

This is ideal for performing analyses such as:

  • The increase or decrease in questions in each tag over time

  • Correlations among tags on questions

  • Which tags tend to get higher or lower scores

  • Which tags tend to be asked on weekends vs weekdays

  • Rates of question closure or deletion over time

  • The speed at which questions are closed or deleted

This is pretty exciting.  Getting good, high-quality data sets for demonstration and pedagogical purposes is time-consuming, so the fact that the Stack Exchange people are tossing one out our way could be a major time-saver.

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Projections And Confidence Intervals

Published 2016-07-15 by Kevin Feasel

Mirio De Rosa explains confidence intervals and sampling issues, using polls as an example:

Weighting is used to make sure samples reproduce the underlying characteristics of the population they are drawn from. For instance, in the UK 51.6% of voters are women, of these 22.5% are above 65 years of age, 23.1% have a higher education and so on. The people selected to make part of a sample may be recruited to ensure they match these proportions.

Within the context of weighting there are two major sampling procedures: Quota and stratified sampling. The relevant difference between them is the latter uses some sort of randomization device while with the former the interviewer decides whether or not to interview a person with certain characteristics. YouGov presumably applied Quota Sampling for the Brexit survey[4], and the gender, age and education weights they applied are shown in the following image.

Read the whole thing.

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Hack Those P Values!

Published 2016-06-24 by Kevin Feasel

Ned Bicare provides us a sure-fire method for getting our academic papers published:

“If you torture the data long enough, it will confess.”

This aphorism, attributed to Ronald Coase, sometimes has been used in a disrespective manner, as if it was wrong to do creative data analysis.
In fact, the art of creative data analysis has experienced despicable attacks over the last years. A small but annoyingly persistent group of second-stringers tries to denigrate our scientific achievements. They drag psychological science through the mire.
Ned has a great tool to play around with as well, letting us Statistics our way to academic success.
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Analyze Fantasy Sports With Spark

Published 2016-06-22 by Kevin Feasel

Jordan Volz is back with part two of his series on fantasy sports analysis using Apache Spark:

We’ll look at both zTot and nTot, and consider the player’s age and experience.The latter is potentially important because there have been shifts in what ages players joined the league over the timespan we are considering. It used to be rare for players to skip college, then it wasn’t, now they are required to play at least one year. It will be interesting to see if we see a difference in age versus experience in the numbers.

We start with the RDD containing all the raw stats, z-scores, and normalized z-scores. Another piece of data to consider is how a player’s z-score and normalized z-score change each year, so we’ll calculate the change in both from year to year. We’ll save off two sets of data, one a key-value pair of age-values, and one a key-value pair of experience-values. (Note that in this analysis, we disregard all players who played in 1980, as we don’t have sufficient data to determine their experience level.)

Jordan also looks at player performance over time and makes data analysis look pretty easy.

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Data Science At Stack Overflow

Published 2016-06-22 by Kevin Feasel

David Robinson discusses his role as a data scientist at Stack Overflow:

The most prominent example of where machine learning is used in our product is Providence; our system for matching users to jobs they’ll be interested in. (For example, if you visit mostly Python and Javascript questions on Stack Overflow, you’ll end up getting Python web development jobs as advertisements). I work with engineers on the Data team (Kevin Montrose,Jason Punyon, and Nick Larsen) to design, improve and implement these machine learning algorithms. (Here’s some more about the architecture of the system, built before I joined). For example, we’ve worked to get the balance right between jobs that are close to a user geographically and jobs that are well-matched in terms of technology, and ensuring that users get a variety of jobs rather than seeing the same ones over and over.

A lot of this process involves designing and analyzing A/B tests, particularly about changing our targeting algorithms, ad design, and other factors to improve clickthrough rate (CTR). This process is more statistically interesting than I’d expected, in some cases letting me find new uses for methods I’d used to analyze biological experiments, and in other cases encouraging me to learn new statistical tools. In fact, much of my series on applying Bayesian methods to baseball batting statistics is actually a thinly-veiled version of methods I’ve used to analyze CTR across ad campaigns.

Sounds like a fun place to be.

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Data Science Languages

Published 2016-06-13 by Kevin Feasel

David Crook walks through his data science workflow and discusses language choice:

So I’ve spent a while now looking at 3 competing languages and I did my best to give each one a fair shake. Those 3 languages were F#, Python and R. I have to say it was really close for a while because each language has its strengths and weaknesses. That said, I am moving forward with 2 languages and a very specific way I use each one. I wanted to outline this, because for me it has taken a very long time to learn all of the languages to the level that I have to discover this and I would hate for others to go through the same exercise.

Read on for his decision, as well as how you go from “here’s some raw data” to “here are some services to expose interesting results.”

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