Program Guidelines and Goals
Global connectivity and innovative technologies generate vast amounts of information that contribute to our understanding and evaluation of nature, human behavior, institutions, society, and beyond. This explosion of evidence to present and address problems is informing major decisions in academe, government, and the private sector. Those with an ability to work with quantitative and qualitative data, big and small, to identify puzzles, consider probing questions, evaluate claims, make inferences, and posit answers will be well positioned to expand knowledge, influence policy, and to be decision makers of the future.
The major in data analytics will provide you with a solid core of mathematics and computer science, followed by four specially designed data analytics courses. All of these courses are project-based, employing analytic methods, as well as ethics and interdisciplinary research skills, practiced in a variety of application domains. In addition, you will take the skills learned in the classroom and practice them in an internship in a professional setting, and then pursue a capstone project informed by this experience.
Data Analytics Major
The major in Data Analytics (DA) requires a minimum of 46 credits of coursework and a community practicum/internship experience, normally undertaken in the summer before the senior year.
The complete requirements are:
|DA 101||Introduction to Data Analytics|
|CS 111||Discovering Computer Science: Scientific Data and Dynamics|
|or CS 112||Discovering Computer Science: Markets, Polls, and Social Networks|
|MATH 135||Single Variable Calculus|
|or MATH 145||Multi-variable Calculus|
|CS 181||Data Systems|
|MATH 220||Applied Statistics|
|DA 200||Data Analytics Colloquium (1 credit), once as a sophomore and once as a junior or senior)|
|DA 301||Practicum in Data Analytics|
|DA 350||Advanced Methods for Data Analytics|
|DA 401||Seminar in Data Analytics|
|Data analytics internship (approved by the Data Analytics Committee)|
|In addition, choose three or more electives from one of the participating departments, including at least one quantitative methods course. The purpose of these electives is to give students disciplinary knowledge that they can carry into their internship and senior seminar.|
|A student may satisfy these electives in one of two ways. First, a student may concentrate their electives in one of the following disciplines by taking all of the courses for that discipline, as listed below.|
|Anthropology and Sociology (3 courses)|
|ANSO 100||People, Culture and Society|
|ANSO 351||Survey Research Methods|
|ANSO 343||Demography of Africa|
|or ANSO 347||Power in Society|
|Biology (4 courses)|
|BIOL 210||Molecular Biology and Unicellular Life|
|BIOL 220||Multicellular Life|
|BIOL 230||Ecology and Evolution|
|BIOL 356||Special Topics (Biostatistics)|
|or BIOL 309||Computational Biology|
|or BIOL 345||Eukaryotic Cell Biology|
(Eukaryotic Cell Biology - Dr. Yoo only)
|Economics (4 courses)|
|ECON 101||Introductory Macroeconomics|
|ECON 102||Introductory Microeconomics|
|ECON 302||Intermediate Microeconomic Analysis|
|ECON 467||Econometrics II (requires ECON 307 or MATH 220)|
|Philosophy (3 courses)|
|PHIL 121||Ethics: Philosophical Considerations of Morality|
|or PHIL 126||Social and Political Philosophy|
|PHIL 210||Philosophy of Science|
|Physics (3 courses)|
|General Physics I|
and General Physics II
|Principles of Physics I|
and Principles of Physics II
and Principles of Physics III
|PHYS 312||Experimental Physics|
|Political Science (3 courses)|
|POSC 201||Analyzing Politics|
|Any two of:|
|Doing Political Science: American Political Behavior|
|The Politics of Congress|
|Campaigns and Elections|
|Political Organizations in the U.S|
|Psychology (3 courses)|
|PSYC 100||Introduction to Psychology|
|PSYC 200||Research Methods and Statistics|
|PSYC 2XX/3XX (except research courses, 370, 410, 361-364, 451-452)|
Alternatively, a student may submit an individualized 3-4 course elective plan, which must include at least one analytics-intensive course, to be considered for approval by the Data Analytics Committee. A successful one-page proposal will clearly describe the student’s desired learning goals and how the proposed courses together achieve these goals. The proposal should also demonstrate the feasibility of completing the proposed courses in the time remaining before graduation. Proposals must be submitted prior to the end of the sophomore year.
Additional Points of Interest
Students who want to acquire deeper technical skills in data analytics may take additional advanced courses such as MATH 145 - Multi-variable Calculus, MATH 213 - Linear Algebra and Differential Equations, CS 173 - Intermediate Computer Science, CS 271 - Data Structures, MATH 435 - Mathematical Modeling, CS 337 - Operations Research, MATH 415 - Operations Research, and CS 339 - Artificial Intelligence. Students may also pursue a second major in Computer Science or Mathematics. Due to some course overlaps, these options require only 6-7 additional courses.
DA 101 - Introduction to Data Analytics (4 Credit Hours)
Many of the most pressing problems in the world can be addressed with data. We are awash in data and modern citizenship demands that we become literate in how to interpret data, what assumptions and processes are necessary to analyze data, as well as how we might participate in generating our own analyses and presentations of data. Consequently, data analytics is an emerging field with skills applicable to a wide variety of disciplines. This course introduces analysis, computation, and presentation concerns through the investigation of data driven puzzles in wide array of fields – political, economic, historical, social, biological, and others. No previous experience is required.
DA 200 - Data Analytics Colloquium (1 Credit Hour)
The Data Analytics colloquium involves three central learning components. 1) regular engagement with guest presentations and community activities in data analytics, 2) group discussion featuring critical analysis and connection of themes found in the guest presentations and in related data analytics topics, and 3) preparation and refinement of professional communication skills necessary for the required internship component of the data analytics major. This course provides an opportunity for students to connect on data analytics ideas and applications, using a range of perspectives that may or may not be normally encountered in a traditional course. Students will develop the knowledge, skills, and methods they need to progress to more advanced learning, while also creating bridges with members of the data analytics community within and outside of Denison. The course must be taken twice by majors: once as a sophomore, and again as either a junior or senior.
Prerequisite(s): DA 101 (may be taken concurrently).
DA 301 - Practicum in Data Analytics (4 Credit Hours)
Utilizing Denison as a model of society, this practicum set in a seminar will explore questions of collective import through the analysis of new and existing sources of data at Denison. A problem-driven approach will lead to the acquisition of new, appropriate data analytic skills, set in an ethical context that carefully considers the implications of data display and policy recommendations on community members. A significant component of the course is working with policymaking and implementing professionals on campus and developing presentation skills appropriate for professional communication with the public. Though a significant learning opportunity itself, this course should also be seen as a prelude to a community internship in the post-Junior year summer.
DA 350 - Advanced Methods for Data Analytics (4 Credit Hours)
This course is designed to develop students' understanding of the cutting edge methods and algorithms of data analytics and how they can be used to answer questions about real-world problems. These methods, and the underlying models, can be used to learn from existing data to make predictions about new data. The course will examine both supervised and unsupervised methods and will include topics such as clustering, classification, and network analysis.
DA 361 - Directed Study (1-4 Credit Hours)
DA 362 - Directed Study (1-4 Credit Hours)
DA 363 - Independent Study (1-4 Credit Hours)
DA 364 - Independent Study (1-4 Credit Hours)
DA 401 - Seminar in Data Analytics (4 Credit Hours)
This is a capstone seminar for the Data Analytics major in which students work collaboratively on research projects. Problems may drive from internship experiences, courses of study at Denison, or other sources subject to instructor approval. Heavy emphasis will be placed on providing ongoing research reports and collective problem solving and review.
DA 451 - Senior Research (4 Credit Hours)
DA 452 - Senior Research (4 Credit Hours)