Advice to students
Students who are planning to major in data analytics should take the introductory requirements and at least one calculus course in their first year (DATA 180, COMP 130 or COMP 132 depending on placement, and MATH 151, MATH 170, or MATH 171 depending on placement). Specifically, students should take at least the first four courses listed above in their first year or, at latest, by the first semester sophomore year. This major has a hierarchical dimension, and the vertical structure of the program requires that students successfully complete prerequisites for admission to higher-level classes in a timely manner. For course descriptions and requirements for the major, refer to the Academic Bulletin: Data Analytics.
Courses appropriate for prospective majors
- COMP 130, Introduction to Computing (fall and spring semesters)—also fulfills the lab science distribution requirement*
- MATH 170, Single Variable Calculus (fall and spring semesters)—also fulfills the QR distribution requirement**
- DATA 180, Introduction to Data Science (fall and spring semesters)***
- MATH 171, Multivariable Calculus (fall and spring semesters)
For course descriptions and requirements for the major, refer to the Academic Bulletin: Data Analytics.
* COMP 130 is designed as a first exposure to computer science and should be requested by students with limited or no prior programming experience. COMP 132 assumes students have had the equivalent of one course of prior programming experience (e.g. a high-school course, or substantial self-taught experience). We recommend that data analytics majors take COMP 130 unless they are planning on taking additional computer science courses.
** All students taking the calculus sequence must take the mathematics placement exam to determine whether they begin at MATH 151 or MATH 170. To determine placement, we recommend visiting the online, interactive placement guide.
*** DATA 180 requires MATH 170 as a prerequisite.
Courses that fulfill distribution requirements
Quantitative Reasoning:
- COMP 130, Introduction to Computing
- MATH 170, Single Variable Calculus
Writing in the Discipline (WiD):
The all-college WiD requirement in the data analytics major will be fulfilled through a series of courses, where writing naturally occurs, and creation of a writing portfolio. The types of writing that data analysts do takes on many different forms for a variety of audiences: graphical/visual representations, briefs, memos, reports, academic papers, code, data documentation, and more for developers, internal and external technical audiences, general audiences, and others.
To satisfy the WiD requirement, a student will complete at least one assignment that travels through the writing process of drafting, receiving feedback, and revising in PHIL 258, DATA 200, DATA 300, and DATA 400. All phases of the writing process will be documented in an electronic portfolio, and the instructor for each course will note if the assignment received a passing grade. Towards the end of the senior seminar (DATA 400), students will augment their portfolio with an additional piece of writing that reflects on their academic journey in the data analytics major and discusses how their curricular and co-curricular experiences have contributed to their future goals. The senior seminar professor will be responsible for reviewing the portfolios and approving the WiD credit for students. Upon completion of DATA 400 and successful submission of a portfolio, the WiD requirement will be satisfied.
PRE-APPROVED 3-COURSE SEQUENCES
Refer to the Academic Bulletin: Data Analytics for details.
EXPERIENTIAL COMPONENT
There are four ways to complete the data analytics experiential component. Please see the page dedicated the experiential component.
Suggested curricular flow through the major
The following curricular guidelines will help you pace your progress through the major. While no specific course must be taken in any given semester, the vertical structure of the program requires that you successfully complete prerequisites for admission to higher-level classes in a timely manner. A summary of the suggested curricular flow is provided below.
- Introductory Requirements (completed by beginning of 2nd year spring):
- MATH 170: Single Variable Calculus
- MATH 171: Multivariable Calculus
- DATA 180: Introduction to Data Science
- COMP 130: Introduction to Computing or COMP 132: Principles of Object-Oriented Design
- Discipline Course I
- Intermediate Requirements (completed by beginning of 3rd year spring):
- MATH 225: Probability and Statistics I
- DATA 200: Data Systems for Data Analytics
- PHIL 258: Philosophy of DATA
- Discipline Course II
- Advanced Requirements (completed by beginning of 4th year spring):
- MATH 325: Probability and Statistics II or ECON 298: Econometrics
- DATA 300: Statistical and Machine Learning
- Discipline Course III
- Senior Seminar (completed during 4th year spring):
- DATA 400: Data Analytics Capstone
There are many possible paths through the data analytics major. Which path to take depends on the student’s prior coursework and placement (in computer science and mathematics). Below, we show six models with different entry points.
Model |
1 |
2 |
3 |
4 |
5 |
6 |
---|---|---|---|---|---|---|
Entry Point |
MATH 151 |
MATH 170 |
DATA 180 MATH 171 |
MATH 151 COMP 130 credit |
MATH 170 COMP 130 credit |
DATA 180 |
With careful planning, all six models allow the possibility for students to spend at least one semester abroad. All paths also require an experiential component (typically completed over the summer) not included in the course plans.
Model |
1 |
2 |
3 |
4 |
5 |
6 |
---|---|---|---|---|---|---|
1st Fall |
MATH 151 ECON 111 |
MATH 170 COMP 130 |
DATA 180 |
MATH 151 ECON 111 |
MATH 170 |
DATA 180 |
1st Spring |
MATH 170 COMP 130 ECON 112 |
DATA 180 MATH 171 |
MATH 171 COMP 130 |
MATH 170 ECON 112 |
DATA 180 MATH 171 |
MATH 171 Discipline I |
2nd Fall |
DATA 180 MATH 171 |
MATH 225 |
MATH 225 Discipline I |
DATA 180 MATH 171 |
MATH 225 |
MATH 225 Discipline II |
2nd Spring |
Discipline I DATA 200 |
MATH 325 |
MATH 325 |
DATA 200 Discipline I |
MATH 325 |
MATH 325 |
3rd Fall |
PHIL 258 MATH 225 |
PHIL 258 Discipline II |
PHIL 258 Discipline II |
PHIL 258 MATH 225 |
PHIL 258 Discipline II |
Study Abroad |
3rd Spring |
Study Abroad |
Study Abroad* |
Study Abroad* |
Study Abroad |
Study Abroad* |
Study Abroad |
4th Fall |
ECON 298** DATA 300 Discipline III |
DATA 300 Discipline III |
DATA 300 Discipline III |
ECON 298** DATA 300 Discipline III |
DATA 300 Discipline III |
DATA 300 PHIL 258 Discipline III |
4th Spring |
DATA 400 |
DATA 400 |
DATA 400 |
DATA 400 |
DATA 400 |
DATA 400 |
*Study abroad for one year is possible here with careful planning. Please consult with your data analytics advisor as early as possible to identify a study abroad program for this scenario.
**Students in this situation must take ECON 298 instead of MATH 325 to study abroad. This adds ECON 111 and ECON 112 to the curriculum.
Independent study and independent research
Each faculty member has special fields of study and will usually be available for advice in that area.
Additional Remarks
Students who are interested in graduate school in data analytics should consider taking the following additional courses (in no particular order):
- MATH/COMP 241: Computational Mathematics
- MATH 262: Linear Algebra (requires MATH 211 as a prerequisite)
- MATH 270: Integration and Infinite Series
- MATH/COMP 331: Operations Research (requires MATH 262 as a prerequisite)
- COMP 232: Data Structures and Problem Solving (requires COMP 132 as a prerequisite)
- ECON 398: Advanced Econometrics