Data Scientist, Part 1.1

26 October 2012

We learn from other people. We often learn from others with similar interests. Who are they? How do we find them?

I am studying at the University of Washington to become a data scientist. My first course, Introduction to Data Science, started a few weeks ago. All of the students completed a survey. Our professor, Dr. Bill Howe, made the anonymized results public. I put the data and my analysis on GitHub for anyone to see.

I am interested in three questions:

  1. What data science topics are students most interested in?
  2. How similar are students’ preferences?
  3. How should we group students together?

Pick The Winner

Which data science topics are the most popular? Let’s use boxplots to find out; they are an effective way to summarize this data.

This can be done in 7 lines of code in R:

survey <- read.csv(file="ReshapedResponses.csv", header=TRUE)
survey[,3] <- survey[,3]+2 #go from range (-2 to 2) to (0 to 4)
orderedSurvey = with(survey, reorder(Question, Response, median))

p <- ggplot(data=survey, aes(x=Response, y=orderedSurvey))+
  labs(x="Importance", y="Question")

The results are clear. My fellow students want to learn about techniques to work with “big data” or “fairly big data” in both practical and abstract ways. There is also a desire to understand machine learning.

Am I like You?

My fellow students are a diverse group. How similar are our learning preferences?

If we pick a random student, S1, how similar are their preferences to another student’s, S2? If we can calculate a single metric to measure similarity for one pair of students, we could calculate that metric for__ _all _pairs of students. This is a Euclidean distance problem, and it comes from problems solved using clustering algorithms.

We’ll be using R again, again writing 7 lines of code:

survey <- read.csv(file="surveyresults.csv",header=TRUE,sep="\t")
survey.clean <- survey[,5:13]+2 #go from range [-2 2] to [0 4]
survey.matrix <- as.matrix(survey.clean) #create a matrix
survey.dist <- dist(survey.matrix) #compute matrix distance
survey.mds <- cmdscale(survey.dist) #compute mds of the distance
survey.mds.df <- #get a table again
qplot(data=survey.mds.df, x=V1, y=V2) #plot the results

The result shows each student’s learning preference compared to their classmates’.

Finding Niches

Which groups of students in that chart have similar learning preferences? How do we find those groups? How big should they be?

Time for another algorithm: k-means clustering. This algorithm is used to find the k best clusters for a set of points. For example, if k=3, it would group all the data into 3 clusters.

Since k is not chosen automatically, we need to find a good value for it_._ There are a few different approaches to consider. We’ll use the elbow method: look at a graph of k vs efficiency, and identify where the line bends (like an elbow). It’s at k=5.

Great! Let’s see the clusters. We can do that with 2 lines of R code:

survey.kmeans <- kmeans(x=survey.mds, centers=5)
qplot(data=survey.mds.df, aes(x=V1, y=V2, color=factor(survey.kmeans$cluster)))

We can see there are 5 groups. They are 3, 6, 8, 8, and 12 people in size, and cover ~80% of the variation in learning preferences between students. That’s pretty good. If this data wasn’t anonymous, a teacher could use it to group students together with similar interests. Success!

Resources and Next Steps

This may seem intimidating for IT professionals or developers. It isn’t. There are fantastic resources available. You just need data, a computer, time, and curiosity.

I have progressed this far using only:

What’s next? More data. More algorithms. More questions. And above all, more insight.