Data Science | Advising Handbook

Data Science is an interdisciplinary major with coursework in mathematics, computer science, statistics, data modeling, communication and visualization, and ethics.

To finish in four years, it is important that freshmen complete: Math 226, Math 227, and CS 150, CS 250.

The Sophomore courses listed below will first be offered in the 2020-2021 academic year, and the Junior/Senior courses in 2021-2022 academic year.

A typical four-year course sequence would be *:

  Fall Winter Spring
Freshmen

MATH 226 Calculus I (4)

CS 150 Intro to CS (4)

 

MATH 227 Calculus II (4)

CS 250 Intro to CS II (4)

Sophomore

DSCI 340 Algorithms and Data Structures (4)

MATH 206 Computational Linear Algebra (4)

 

MATH 307 Calculus Based Statistics (4)

PHIL 202 Ethics & Society (4)

*CS 130 Intro to Software Tools (@)

Junior/Senior

DSCI 440 Machine Learning and Data Mining (4)

CS 445 Databases

 

DSCI 407 Statistical Modeling and Regression (4)

* MATH 385 Junior Seminar (2)

Senior DSCI 490 Capstone (2)   DSCI 492 Capstone (2)

 

Transfer students who have completed the above freshman fall and spring coursework can finish in three years.

*The Department of Mathematics and Computer Science is accepting CS 130 as a substitute for MEDA 319 and MATH 385 as a substitute for SCI 280 in the Data Science Major. 

Data Science Minor

There is currently no minor offered in Data Science.

Transfer Students and Switching to a Data Science Major

A transfer student with enough of the prerequisite courses could complete the data science major in two years. However, this could be complicated by the timing of courses which are only offered every other year. A transfer student would need to have, at a minimum, transfer equivalency for a full year of Calculus and a full year of Introductory Computer Science. Also, depending upon the timing of course offerings, a transfer student might need to have an Algorithms and Data Structures course and perhaps a calculus-based Statistics course as well.

The following two-year plan for courses assumes that students have all of this background.

First Year After Transfer

Fall

Cr

   

Spring

Cr

MATH 206 Computational Linear Algebra

4

   

 PHIL 202 Ethics & Society

 4

DSCI 440 Machine Learning and Data Mining

4

MATH 385  Junior Seminar

2

CS 445 Databases

 

DSCI 407 Statistical Modeling and Regression

4

 

 

 

 

CS 130 Intro to Software Tools

2

Second Year After Transfer

Fall

Cr

   

Spring

Cr

DSCI 490: Capstone I

2

 

 

 DSCI 492: Capstone II

 2

*In the event that DSCI 440 and DSCI 407 were not offered until the student’s senior year, they would have the opportunity to take DSCI 340: Algorithms and Data Structures (prerequisite for DSCI 440 and CS 445) in the fall of their first year and they would have the opportunity to take MATH 307: Calculus-based Statistics (prerequisite for DSCI 407) in the spring of their first year. In such cases, the follow-on courses – DSCI 440, CS 445, and DSCI 407 – would be postponed until their senior year.

Annual Class Planning Guide

This planning guide shows the predicted offerings for certain courses in this major. Use the guide to help you think about future terms. For a list of classes currently offered and how they fulfill core requirements, please see BoxerOnline and cross-reference this page as needed. Meet with your advisor and/or reach out to the department if you have questions.

Course Number

Course Name

Fall

Winter

Spring

Notes

DSCI 100

 Foundations of Data Science

 

 X

 

 

DSCI 340

Algorithms & Data Structures 

 X

 

 

 

DSCI 407

Statistical Modeling and Regression

 

 

 X

 Even Years

DSCI 440

Machine Learning and Data Mining

 X

 

 

 Odd Years

DSCI 490

Senior Capstone

 X

 

 

 

DSCI 492

Senior Capstone II

 

 

 X