SOC 20009 - Introduction to Data Science
"Introduction to Data Science" is an introductory course that will provide an overview of data science from both a computer science perspective and a social science perspective.
This course will orient students to the field, to key concepts, to the types of questions addressed, to the technical aspects of data science and to the process of making sense of data.
CSE 10101/CDT30010 – Elements of Computing I
Introduction to programming for students without prior programming experience. Programming structures suitable for basic computation. Elements of computer organization and networking.
Development of programming skills including data manipulation, multimedia programming, and networking. Standards for exchange and presentation of data. Comprehensive programming experience using Python.
MDSC students are required to take a statistics class. The minor accepts the following classes: SOC30903 “Statistics for Sociological Research”, ECON30330, “Statistics for Economics”, Math30540 “Mathematical Statistics”, Psy30100 “Experimental Psychology I: Statistics”, ACMS20340 “Statistics for Life Sciences”, ACMS30440 “Probability and Statistics”, ACMS30600 “Statistical Methods and Data Analysis”, ITAO 20200/BAMG 20150 “Statistical Inference in Business”.
Students may petition to have other statistics courses accepted to fulfill the requirement, by contacting Mim Thomas (firstname.lastname@example.org).
The following courses have seats set aside specifically for MDSC students:
SOC 30903 – Statistics for Sociological Research– Sociology faculty - Rotating, offered every semester
We frequently encounter statements or claims based on statistics, such as: "Women earn less than men," "The American population is becoming more racially and ethnically diverse," or "Married people are healthier than unmarried people."
On what information are these statements based? What kinds of evidence support or refute such claims? How can we assess their accuracy? This course will show students how to answer these sorts of questions by interpreting and critically evaluating statistics commonly used in the analysis of social science data. Hands-on data analysis and interpretation are an important part of the course.
You should finish the course with the ability to interpret, question, and discuss statistics accurately and with an understanding of which type of statistic is appropriate for different kinds of data and research questions. You should also finish the course with basic programming and data analysis skills. No prior statistical knowledge is required.
This course is ideal for students interested in the social and/or life sciences as well as business and/or law.
ECON 30330 – Statistics for Economics – Economics faculty - Rotating, offered every semester
This course seeks to introduce the student to the principles of probability and statistical theory appropriate for the study of economics. The emphasis of the course will be on hypothesis testing and regression analysis.
A Sampling of Electives
SOC 33199/43990 – Social Networks
Social networks are an increasingly important form of social organization. Social networks help to link persons with friends, families, co-workers and formal organizations. Via social networks information flows, support is given and received, trust is built, resources are exchanged, and interpersonal influence is exerted.
Rather than being static, social networks are dynamic entities. They change as people form and dissolve social ties to others during the life course. Social networks have always been an important part of social life: in our kinship relations, our friendships, at work, in business, in our communities and voluntary associations, in politics, in schools, and in markets.
Our awareness of and ability to study social networks has increased dramatically with the advent of social media and new communication tools through which people interact with others. Through email, texting, Facebook, Twitter and other platforms, people connect and communicate with others and leave behind traces of those interactions.
This provides a rich source of data that we can use to better understand our connections to each other; how these connections vary across persons and change over time; and the impact that they have on our behaviors, attitudes, and tastes.
This course will introduce students to (1) important substantive issues about, and empirical research on, social networks; (2) theories about network evolution and network effects on behavior; and (3) tools and methods that students can use to look at and analyze social networks.
The course will be a combination of lectures, discussions and labs. Course readings will include substantive research studies, theoretical writings, and methodological texts. Through this course students will learn about social networks by collecting data on social networks and analyzing that data.
DESN 40120 - Visual Communication Design 10: Visualization of Data
Visualization & sequencing of complex or abstract subject matter for the purpose of informing, educating or training the end-user. Design process includes the acquisition of information and data to become a subject matter expert on a project topic.
Development of topics through the parsing of information, focusing of subject, sketching, illustration and graphical data representation. Delivery of information through an interactive, user-driven experience possibly exploring handheld devices.
PSY 60122 - Introduction to Statistical Learning
Cluster analysis is a statistical approach for the analysis of multivariate data that aims at discovering groups of subjects in a sample that are similar to each other. Clustering techniques are applied in a wide variety of areas including psychiatry (e.g., finding disease categories), marketing (e.g. different consumer profiles), sociology (e.g., social subgroups), etc.
Cluster analysis is an example of unsupervised learning. The latter term is derived from the fact that clusters are discovered in the absence of an outcome variable that guides the clustering. Regression trees and random forests, on the other hand, are supervised learning approaches. Outcomes in regression trees or forests can be categorical or continuous.
This graduate level course consists of two parts. The first part covers the basics of cluster analysis whereas the second part provides an introduction to regression trees and random forests.
The course consists of approximately 2/3 lectures providing the theoretical background, and 1/3 lab sessions, which will use the free software program R. The course is suitable for students with a strong interest in methods.
PSY 30105 – Exploratory and Graphical Data Analysis
The process by which Psychological knowledge advances involves a cycle of theory development, experimental design and hypothesis testing. But after the hypothesis test either does or doesn't reject a null hypothesis, where does the idea for the next experiment come from?
Exploratory data analysis completes this research cycle by helping to form and change new theories. After the planned hypothesis testing for an experiment has finished, exploratory data analysis can look for patterns in these data that may have been missed by the original hypothesis tests. A second use of exploratory data analysis is in diagnostics for hypothesis tests.
There are many reasons why a hypothesis test might fail. There are even times when a hypothesis test will reject the null for an unexpected reason. By becoming familiar with data through exploratory methods, the informed researcher can understand what went wrong (or what went right for the wrong reason).
This class is recommended for advanced students who are interested in getting the most from their data.
PSY 40124 - Psychological Measurement and Test Development
This course introduces measurement of human behavior in psychological studies, the construction and use of psychological instruments and educational assessments (including tests of intelligence, achievement, personality, and vocational interest), validation of these tests following classical test theory and item response theory, as well as practice in test construction, administration and validation.
The course also highlights issues of test equality across groups, assessing measurement error, interpretation of test scores in the context of criterion-referenced tests vs. norm-referenced tests, standard setting and so on.
PHIL 20632 – Robot Ethics
Robots or "autonomous systems" play an ever-increasing role in many areas, from weapons systems and driverless cars to health care and consumer services.
As a result, it is ever more important to ask whether it makes any sense to speak of such systems' behaving ethically and how we can build into their programming what some call "ethics modules."
After a brief technical introduction to the field, this course will approach these questions through contemporary philosophical literature on robot ethics and through popular media, including science fiction text and video.
MDSC Electives for Spring 2019 Include:
Text Analysis for Social Science
MDSC 43919 01 (CRN 31252)
SOC 43919 01 (CRN 30059)
MDSC 43990 01 (CRN 31253)
SOC 43990 01 (CRN 27729)
Elements of Computing II
CSE 10102 01 (CRN 25420)
CDT 30020 01, (CRN 25434)
CDT 34020 01 (CRN 31593)
Text Mining the Novel
CDT 30380 01 (CRN 30163)
ENGL 30010 01 (CRN 30162)
POLS 40815 01 (CRN 27102)
MGA 40801 01 (CRN 27352)
Data and AI Ethics
PHIL 20647 01 (CRN 31324)
STV 20647 01 (CRN 31425)
CDT 20515 01 (CRN 31471)
PHIL 20647 02 (CRN 31613)
PHIL 20647 03 (CRN 31615)
R for Data Science
PSY 30109 01 (CRN 31886)
PSY 60109 01 (CRN 31885)
Visualization of Data
CDT 40120 01 (CRN 31392)
DESN 40120 (CRN 26027)