requirements for a degree in Data Science 

Core Courses

Core courses are intended to equip students with fundamental skills and knowledge to take further courses in the domain of Data Science. The prerequisite sequences among some of the courses listed above must be honored.

  • Calculus 1
  • Introduction to Programming in Python
  • Intermediate Python OR Object-Oriented Programming
  • Dealing with Data 1
  • Dealing with Data 2
  • Probability 1 (Mod 1)
  • Probability 2 (Mod 2)
  • Linear Algebra

Data Science Area Courses

These courses include the main area courses in the domain of Data Science. Students are expected to take all 6 of them during the course of their AOC.

  • Algorithms for Data Science
  • Databases for Data Science
  • Software Engineering for Data Science
  • Applied Linear Models
  • Artificial Intelligence and Data Mining
  • Ethics in Data Science

Thesis-Preparation Courses (3 elective courses) and the Undergraduate Thesis

Students will conduct their thesis either as a theoretical/methodological Data Science, or as an applied Data Science thesis that combines skills acquired earlier in the program with skills and knowledge that will be gained by taking cross-disciplinary courses (e.g. courses from Humanities or Social Sciences). Hence, the student is expected to select all 3 elective courses either from pool A or from pool B.

The choice of an applied Data Science thesis option, hence the courses to take from pool B, requires the student to have two thesis advisors, one from Data Science and one from the other discipline. The course selection will be subject to both advisors’ approval.

For a theoretical/methodological Data Science AOC thesis, students will choose three courses from:

Pool A:

  • 3xxx and 4xxx courses from CSCI, STAN, MATH or courses from the Graduate Program
  • IDC 4132 Distributed Computing

For an applied Data Science AOC thesis, choose 3 courses from:

Pool B:

  • 3xxx and 4xxx courses from Humanities, Social Science or Natural Sciences not in Pool A.

Data Science Summer Internship or Community Project

Data Science is a practical field. As such, each AOC student is expected to do an internship or a community project, preferably following the completion of their 3rd year in the program. The internship or project topic must be in the Data Science field and it must be approved by the student’s advisor or internship coordinator.

Course Requirements for Data Science Secondary Field

The secondary field is intended to not only provide foundation courses, but also include some of the more advanced courses in Data Science that the students may find useful in conducting their major field of study.

To complete the Secondary Field, the student should take the following required courses:

  • Introduction to Programming in Python
  • Intermediate Python OR Object Oriented Programming
  • Dealing with Data 1
  • Dealing with Data 2
  • Databases for Data Science
  • Artificial Intelligence and Data Mining

The student must also select 2 courses from the following pool:

  • Algorithms for Data Science
  • Software Engineering for Data Science
  • Applied Linear Models
  • Distributed Computing
  • Statistical Learning
  • Data Visualization and Communication

Sample Path to the AOC

The sample path starts with the first-year introductory courses for Data Science including three courses that involve programming in Python and R and a course (with two mods) on probability. These courses are intended to provide AOC candidate students an initial view into the discipline and allow them to decide whether they would like to continue or not. In the second year, the students are expected to take the remaining foundation courses (Calculus and Linear Algebra), a Python continuation course, and also three of the core courses of Data Science (Databases, Algorithms and Software Engineering). With this background, students can go on to take the remaining core courses of Data Science and elective courses oriented towards their thesis.

Four Year Plan

Fall

  • Dealing with Data 1

  • Introduction to Programming in Python
  • Probability 1

  • Probability 2

Spring

  • Dealing with Data 2

  • Liberal Arts Curriculum Course

  • Liberal Arts Curriculum Course

Fall Term

  • Calculus 1

  • Databases for Data Science

  • Intermediate Python OR Object-Oriented Programming

Spring Term

  • Dealing with Data 2

  • Liberal Arts Curriculum Course

  • Liberal Arts Curriculum Course

Fall Term

  • Applied Linear Models

  • Ethics in Data Science

  • Elective

Spring Term

  • Artificial Intelligence and Data Mining

  • Elective

  • Elective

During your fourth year as a Data Science student, students will be required to work on your Thesis.

Two Year Plan

assuming student has had two statistics courses, two Programming courses (at least one in Python), Calculus 1 and Linear Algebra)

Fall Term

  • Probability 1 & 2

  • Databases for Data Science

  • Ethics in Data Science

Spring Term

  • Algorithms for Data Science

  • Software Eng. for Data Science

  • Elective

Fall Term

  • Applied Linear Models

  • Elective

  • Thesis

Spring Term

  • Artificial Intelligence and Data Mining

  • Elective

  • Thesis