Computer Science - data science

Thursday Extra 2/8/18: Incorporating Data Science into Introductory CS Course

petersos — Mon, 05 Feb 2018 15:07:43 +0000

Thursday, February 8, 2018
4:15 p.m. in Science 3821
Refreshments at 4:00 p.m. in the Computer Science Commons (Science 3817)

A Functional Approach to Data Science in CS1, presented by Professor Samuel A. Rebelsky, discusses the new "data science" version of CSC 151 he has been doing with Titus Klinge and Sarah Dahlby Albright.

As part of the development of a new interdisciplinary initiative in data science that draws from statistics, mathematics, computer science, and the social sciences, we have developed a new introductory CS course that emphasizes data science and that we refer to as DataCSCi. Unlike other introductory data science courses, such as Berkeley's Data 8, our course retains the broad array of concepts necessary not only to introduce programming principles related to data science, but also to prepare students for the second course in our standard introductory computer science sequence. In particular, the course includes coverage of recursion (numeric and structural), unit testing, linked data structures, and other concepts we rely upon in subsequent courses in computer science.

At the same time, we introduce students to a wide variety of techniques and approaches that support them in their subsequent work in data science, including techniques for wrangling, cleaning, and visualizing data. We achieve this combination of breadth and depth through two core approaches: We focus on a spiral "use then implement" approach and we focus on a functional model of programming using Scheme/Racket. While Python and R are the most commonly used languages for data science, we find that Scheme works particularly well to introduce students to concepts both complex, like map-reduce, and simple, like list filtering.

CS Table 11/21/17: Games and the Gig Economy

petersos — Thu, 16 Nov 2017 21:23:01 +0000

At the November 21 CS Table, we’ll be discussing the phenomenon of the Gig Economy—an economy characterized by independent workers contracted for short-term jobs—and how recent developments in technology have supported this new economy. The most famous of these is Uber, and in particular, we’ll look more closely at how Uber uses data science and psychology in tandem with mobile technology to power its driver networks (for better or worse). Please read this pair of meaty (but intriguing!) articles:

Nathan Heller. Is the Gig Economy Working? The New Yorker. May 15, 2017.
Noam Scheiber. How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons. The New York Times. April 2, 2017. The online article features some excellent data visualizations of the concepts described within the article.

Computer science table (CS Table) is a weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science. CS Table meets Tuesdays from 12:00–1:00pm in JRC 224A (inside the Marketplace). Contact the CS faculty for the weekly reading. Students on meal plans, faculty, and staff are expected to cover the cost of their meals. Visitors to the College and students not on meal plans can charge their meals to the department (sign in at the JRC front desk).

CS Table 4/12: Role of data science in elections

petersos — Tue, 12 Apr 2016 13:14:07 +0000

Join us for a discussion of the role of data science in elections. The four articles below discuss the novel use of data in President Obama's 2012 reelection campaign, the Cruz campaign's approach this election cycle, and two retrospectives on Nate Silver's predictions from the 2012 election.

Inside the Secret World of the Data Crunchers Who Helped Obama Win. Michael Scherer, Time Magazine, 11/07/2012.
Cruz campaign credits psychological data and analytics for its rising success. Tom Hamburger, The Washington Post, 12/13/2015.
In Defense of Nate Silver, Election Pollsters, and Statistical Predictions. Zeynep Tufekci, Wired, 11/02/2012.
Numbers nerd Nate Silver's forecasts prove all right on election night. Luke Harding, The Guardian, 11/07/2012.

Computer science table (CS Table) is a weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science. CS Table meets Tuesdays from 12:00-12:45 in JRC 224C. Students on meal plans, faculty, and staff are expected to cover the cost of their meals. Visitors to the College and students not on meal plans can charge their meals to the department.

CS Table 2/16: What is Data Science?

petersos — Mon, 15 Feb 2016 22:44:14 +0000

In honor of Hillary Mason '00 giving convocation this Thursday at 11 am (go!), we will be talking about Data Science in table this week. In particular, we'll be answering the basic questions about data sciences—"what?" and "how?".

Data Scientist: The Sexiest Job of the 21st Century. Thomas H. Davenport and D.J. Patil. Harvard Business Review, October 2012.
A Taxonomy of Data Science. Hilary Mason and Chris Wiggins. Dataists, September, 2010.

If you are curious about the field, I recommend checking out these two optional readings as well:

What is Data Science? Mike Loukides. O'Reilly blog, June 2010.
Data Science Workflow: Overview and Challenges. Phillip Guo. Blog@CACM, October 2013.

Computer science table (CS Table) is a weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science. CS Table meets Tuesdays from 12:00-12:45 in JRC 224C. Students on meal plans, faculty, and staff are expected to cover the cost of their meals. Visitors to the College and students not on meal plans can charge their meals to the department.