New Data Science Courses Spring 2022

DATA SCIENCE ACADEMY COURSES

The NC State Data Science Academy is delighted to offer 8 one-credit undergraduate courses this Spring under the DSC (data science) prefix. Please find the descriptions below in a ready-to-go blurb. We would appreciate your help getting the word out and encouraging your students to enroll. Three of the courses have no prerequisites while 495-002, 495-003 and 495-004 only require some basic familiarity with programming. We hope to attract students from all 10 colleges. Undergraduate students, graduate students, staff and faculty are all welcome to participate as students.

We also hope you will let us know which of your data science courses in your departments that we can encourage the students in these introductory courses to take next. Our goal is to give them a taste of data science and send them to you for more!

Best regards,
Dr. Rachel Levy
Executive Director, Data Science Academy and Professor of Mathematics
rlevy@ncsu.edu

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Data Science curious?

The NC State Data Science Academy welcomes you to eight sections of our one-credit courses this Spring that have the DSC (data science) prefix. Please find the descriptions below. Five of the sections have no prerequisites while 495-002, 495-003, and 495-004 only require some basic familiarity with programming. We hope to attract students from all 10 colleges so encourage your friends to sign up! You can take as many of the courses as you want.

DSC 495-001 and DCS 495-009, R/Python for Data Science
This course develops the introductory skills in R and Python that students need for data science. Topics include data types, data structures, control structures, good coding practices, and reproducible coding. Students will become acquainted with basic data science algorithms and their implementations in R and Python. NO PRIOR EXPERIENCE REQUIRED

DSC-495-001 Wed 9:35 – 10:25 AM
DSC-495-009 Wed 11:45 AM – 12:35 PM

DSC 495-002, Exploratory Data Analysis for Big Data
Exploratory data analysis (EDA) focuses on summarizing the main characteristics of data sets, often using visualization methods. The goal is not formal modeling or hypothesis testing, but understanding and exploring data to formulate hypotheses for further investigation. This course will present the techniques of EDA and generalize those to large data sets. Students should have basic knowledge of a programming language, such as appropriate use of data structures, such as lists and matrices, and flow control mechanisms, such as loops.

DSC-495-002 Fri 9:35 – 10:25 AM

DSC 495-003, Natural Language Processing
This course will explore the methods that are useful for analyzing text as a data source. The course will survey the different goals and questions relating to text, including areas like text processing, morphological analysis, syntactic analysis, lexical analysis, semantics, discourse analysis, and text summarization. Students should have basic knowledge of a programming language, such as appropriate use of data structures, such as lists and matrices, and flow control mechanisms, such as loops.

DSC-495-003 Wed 3:00 – 3:50 PM

DSC 495-004, Data Wrangling and Web Scraping
It is often said that 80% of the time spent on analyzing data is on finding, cleaning, and preparing data for analysis. This course will focus on how to format data sets for subsequent analysis, tools for manipulating and cleaning data sets, methods for reading data from tables on web pages, and techniques for merging multiple data sets. Students should have basic knowledge of a programming language, such as appropriate use of data structures, such as lists and matrices, and flow control mechanisms, such as loops.

DSC-495-004 Fri 11:45 AM – 12:35 PM

DSC 495-006, Data Science for Policy
Data is a fundamental part of learning more about the most effective ways to improve communities, including learning about what policies work and why. Data Science for Policy will introduce students to the role of data as evidence in the policy process, including identifying cause and effect in complex social environments. Students will discuss the fundamental problem of causal inference, and explore the ways statistical modeling can assist policy makers in identifying effective public policy.

DSC-495-006 Mon 9:35 – 10:25 AM

DSC 495-007 Data and Ethics
This course will provide a framework to analyze privacy and control of information/big data through the lens of ethical implications of data collection and management. Students will evaluate datasets and relevant case studies to evaluate the broader impact of data science on government policy and society using principles of fairness, accountability, and open-data. Students will integrate web scraping and textual analysis to examine the need for transparency while also learning best practices for responsible data management.

DSC-495-007 Mon 11:45 AM – 12:35 PM

DSC-495-008 Data Physicalization
A data physicalization (or simply physicalization) is a physical artifact whose materiality encodes data. Data physicalization engages its audience and communicates data using tangible data representations. This course covers topics such as visualization aesthetics, the data-object, data sculptures, critical making, and wearable/art technology. Students will analyze current examples of data physicalization, discuss visualization in the context of cultural and historical practice, and evaluate scholarship that recognizes intersections among physicalization, record-keeping, and data literacy.

DSC-495-008 Mon 3:00 – 4:00 PM