Major

13 courses and an experiential component 

Core Requirements
DATA 180, 200, and 300
PHIL 258

Mathematics Requirements
MATH 170, 171, 225, and 325 (or ECON 298)

Computer Science Requirement
COMP 130

Three-course Discipline Sequence Requirement 
The three-course sequence can be selected from the list below. The pre-approved three-course sequences below come from nineteen different departments and all three academic divisions at the college.  These courses are subject to change by departments as needed.  Alternatively, another three-course sequence that is proposed by the student and approved by the data analytics committee can satisfy this requirement.

Capstone Requirement
DATA 400

Experiential Component
There are four ways to complete the data analytics experiential component. Students can complete the experience during any summer or semester after they take DATA 180 and COMP 130 in the following ways.

  • Internship (with INP designation): The student participates in an internship experience using data analytics skills, broadly defined, under the supervision of a mentor in the field.
  • Research (with REXP designation): The student participates in a scholarly scientific research experience using data analytics skills, broadly defined, under the supervision of a professional researcher.
  • Independent Study/Research or Student-Faculty Research (500, 550, or 560 course): The student participates in a scholarly scientific study or research experience using data analytics skills, broadly defined, under the supervision of a professor.
  • Study Abroad Program with Internship/Research Experience: Options in this category may vary depending on the study abroad program, so a student should consult with CGSE about internships while exploring study abroad programs.

For more detailed information, please see the department's webpage dedicated to the experiential component.

Pre-approved three-course sequences
Please refer to the pre-approved three-course sequences list.

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
    • Discipline Course I
  • Intermediate Requirements (completed by beginning of 3rd year spring):
    • MATH 225: Probability and Statistics I
    • DATA 200: Database Systems and Database Management 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: Senior Seminar in Data Analytics

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
MATH 171
COMP 130 credit

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  Discipline I

MATH 225 Discipline I

DATA 180 MATH 171

MATH 225  Discipline I

MATH 225 Discipline II

2nd Spring

Discipline I DATA 200

MATH 325
(or ECON 298)     
DATA 200

MATH 325
(or ECON 298)      
DATA 200

DATA 200 Discipline I

MATH 325
(or ECON 298)     
DATA 200

MATH 325
(or ECON 298)      
DATA 200

3rd Fall

PHIL 258  MATH 225  Discipline II

PHIL 258  Discipline II

PHIL 258  Discipline II

PHIL 258 MATH 225   Discipline II

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.

Courses

101 Special Topics
Topics to be announced when offered.
Prerequisite: Dependent upon topic.
Attributes: Quantitative Reasoning

180 Introduction to Data Science
An introduction to the principles and tools of data science focusing on exploratory data analysis. Topics include types of variables, mathematical representations of data, data wrangling and transformations, data visualization and numerical summaries, and supervised and unsupervised machine learning. The course will include an introduction to computational tools such as the R statistical environment. No prior programming experience is required.
Prerequisites: MATH 170 or department placement. This course is cross-listed as COMP 180 and MATH 180.
Attributes: ENST Foundations (ESFN), Quantitative Reasoning

200 Database Systems and Data Management for Data Analytics
A comprehensive introduction to the management and manipulation of database systems as it applies to data analytics. Topics related to data query languages to relational databases and NoSQL data systems will be covered, as well as the access and acquisition of other structured and unstructured data repositories available across the Internet. An understanding of techniques for transforming and restructuring data representations to allow for analysis will also be addressed.
Prerequisite: COMP 130 and DATA/COMP/MATH 180. Cross-listed with COMP 200.

201 Special Topics
Topics to be announced when offered.
Prerequisite: Dependent upon topic.

300 Statistical and Machine Learning
An introduction to the fundamental concepts and methods for statistical and machine learning. Focus is given on providing both a theoretical foundation and the practical skills needed to apply machine learning to a variety of applications in various disciplines. Topics include supervised methods such as regression and classification, and unsupervised methods such as clustering and dimensionality reduction.
Prerequisite: DATA 200 and MATH 225.

301 Special Topics
Topics to be announced when offered.
Prerequisite: Dependent upon topic.

400 Senior Seminar in Data Analytics
A capstone course that provides students with an opportunity to apply the data analytics knowledge they have acquired to independent research projects. At least one of the projects must be derived from the chosen discipline specific electives. Students will get experience in all aspects of solving real-world problems, including project planning, consideration of legal and ethical issues, collecting and processing data, analyzing and interpreting results, writing reports, and giving presentations.
Prerequisites: DATA 300, MATH 325 or ECON 298, PHIL 258 and the three- course disciplinary sequence.