Data Science

Data science is a growing field that can be used to reveal insight into authorship of old texts, assist in personalized health care, predict future epidemics, understand customer behavior and provide business insight, quantify and predict effects of climate change, and improve automation via tools like computer vision. On our campus, data science can be used to study food insecurity, final exam scheduling, housing and traffic flow. This wide variety of applications is what makes data science so important.

This coordinate major (co-major) provides a suite of linked courses that supplement a technical major in business analytics (BSBA), computer science (BS), statistics (BS) or mathematics (BS) by adding breadth and a liberal arts perspective. These courses will supplement a student’s technical major by adding the data science knowledge and skills needed to succeed in their chosen field of endeavor while experiencing the interdisciplinarity of the field and its broader impact across various disciplines.

Bachelor of Arts in Data Science 

The BA in data science is only available as a co-major to students whose primary major is a BSBA in Business Analytics, BS in Computer Science, BS in Statistics or BS in Mathematics. These disciplines form the core of data science, so the primary major ensures that students have sufficient depth in a particular field central to data science. Complementing the depth a student receives from their primary major, the BA in data science provides breadth across the interdisciplinary field of data science. The major is not intended as – nor can it be declared as – a stand-alone course of study. No courses may be counted for both majors. Students who have completed this co-major will receive one degree (the BS in their primary major) and have noted on their transcript that they have completed all the major requirements for the BA co-major in data science. They will not receive a BA degree.

The Bachelor of Arts in Data Science requires eight courses.

With a BSBA in Business Analytics

Students majoring in business analytics should choose MATH 201 to satisfy their calculus requirement.

Program Requirements

DATA 250Fundamentals of Data Science1
CSCI 204Data Structures & Algorithms1
MATH 202Calculus II1
MATH 245Linear Algebra1
STAT 217Statistics II1
Three theme courses3
Total Credits8
 

With a BS in Computer Science

Program Requirements

DATA 250Fundamentals of Data Science1
MATH 245Linear Algebra1
STAT 230Data Visualization & Computing1
One technical elective1
Four theme courses4
Total Credits8
 

With a BS in Statistics or BS in Mathematics

Students majoring in statistics should choose STAT 354 as one of their 300-level electives. Students majoring in mathematics must complete STAT 216, STAT 217, STAT 230 and STAT 354 and should choose STAT 354 as one of their 300-level electives. Because of the additional math course requirements, students majoring in mathematics will effectively need to take 10 courses beyond their primary major to fulfill the BA co-major in data science requirements. 

Program Requirements

DATA 250Fundamentals of Data Science1
CSCI 204Data Structures & Algorithms1
One technical elective 11
Four theme courses4
One ethics course 21
Total Credits8
1

Students majoring in mathematics must use STAT 217 for their technical elective.

2

Students may choose from among PHIL 213, PHIL 220, PHIL 228 or PHIL 274. If a prerequisite course is required, it should be included as one of the theme courses.

Technical Electives

ANOP 330Predictive Analytics: Machine Learning Fundamentals for Business1
CSCI 311Algorithm Design & Analysis1
CSCI 349Applied Machine Learning1
CSCI 365Image Processing & Analysis1
GEOL 230Environmental GIS1
GEOL 334Geophysics1
GEOG 204Applied G.I.S.1
SOCI 209Analyzing the Social World1
STAT 217Statistics II1
STAT 354Modern Data Analysis1

Theme Courses

Students interested in this co-major must prepare a brief proposal for their theme courses in conjunction with their academic adviser and then submit it to the Data Science Coordinating Committee for approval. The courses should be focused on a data science-related theme of their own design (e.g., visualization, ethics, communication) and not a broad discipline, and that theme should be one that allows the possibility of data science-related activities, either within the courses themselves or in the student’s future career or further education. The proposal should include a list of at least six potential courses that fit their proposed theme. At least two theme courses taken for the co-major must be in arts & humanities, and students may select at most two 100-level courses. If the chosen ethics course for statistics/mathematics majors requires a 100-level prerequisite, the student may select at most three 100-level theme courses. No more than one theme course may be in ANOP/CSCI/MATH/STAT.

Students earning a B.A. in Data Science will:

  1. Strengthen skills in visualization, writing and presentation of data (1, 6, 7)

  2. Understand both the technical aspects of data science and how human, social and institutional structures shape technical work (1, 2, 6, 8, 9)

  3. Learn about ethical actions when managing and analyzing data (2, 5)

Numbers in parentheses reflect related Educational Goals of Bucknell University.

Courses

DATA 100. Introduction to Data Science. 1 Credit.

Offered Either Fall or Spring; Lecture hours:3,Other:1.5
An introduction to data science where students develop their data acumen and learn techniques for analyzing and modeling data. The course covers the entire data analysis process and students develop a reproducible analysis workflow in R. No prior statistics or computing knowledge is expected. Not open to students who have taken ANOP 330, CSCI 349, DATA 250 or STAT 230.

DATA 250. Fundamentals of Data Science. 1 Credit.

Offered Either Fall or Spring; Lecture hours:3
An introduction to the concepts, core techniques and software of data science; emphasizing both data science principles and methods. Topics may include: computational libraries for data science and visualization; statistical and machine learning algorithms for regression, classification and clustering. Prerequisites: CSCI 204 and MATH 216 or STAT 216 or MATH 227 or STAT 227.

DATA 306. Data Science & Statistical Consulting. 1 Credit.

Offered Alternating Fall Semester; Lecture hours:3
Experiential learning course with collaborative data focused projects. Students will learn about and engage with important data science topics. Advanced statistical software will be used. Prerequisites: (STAT 216, STAT 227, ANOP 102, PSYC 215 or ENGR 226) and (STAT 230, CSCI 203 or ANOP 203) and (STAT 217, CSCI 349, ANOP 330, or MECH 484). Equivalent to STAT 306.

Faculty

Coordinating Committee: Matthew D. Bailey (Business Analytics), Abby Flynt (Mathematics & Statistics), Brian R. King (Computer Science)