Major Requirements

Course Requirements

• CSC 241 INTRODUCTION TO COMPUTER SCIENCE I
• CSC 242 INTRODUCTION TO COMPUTER SCIENCE II            
• CSC 300 DATA STRUCTURES I
• CSC 301 DATA STRUCTURES II
• CSC 321 DESIGN AND ANALYSIS OF ALGORITHMS
• CSC 373 COMPUTER SYSTEMS I
• CSC 374 COMPUTER SYSTEMS II
• CSC 394 SOFTWARE PROJECTS (Capstone)
• or MAT 398 SENIOR CAPSTONE SEMINAR (Capstone)
• or DSC 394 DATA SCIENCE PROJECT (Capstone)
• MAT 140 DISCRETE MATHEMATICS I
• MAT 141 DISCRETE MATHEMATICS II
• One (1) of the following three-course sequences: 
• Calculus Sequence (option 1)
• MAT 147 CALCULUS WITH INTEGRATED PRECALCULUS I
• MAT 148 CALCULUS WITH INTEGRATED PRECALCULUS II
• MAT 149 CALCULUS WITH INTEGRATED PRECALCULUS III
• Calculus Sequence (option 2)
• MAT 150 CALCULUS I
• MAT 151 CALCULUS II
• MAT 152 CALCULUS III
• Summer Calculus (option 3)
• MAT 155 SUMMER CALCULUS I
• MAT 156 SUMMER CALCULUS II
  
• Calculus for Mathematics and Science Majors Sequence (option 4)
• MAT 160 CALCULUS FOR MATHEMATICS AND SCIENCE MAJORS I
• MAT 161 CALCULUS FOR MATHEMATICS AND SCIENCE MAJORS II
• MAT 162 CALCULUS FOR MATHEMATICS AND SCIENCE MAJORS III
• Calculus with Scientific Applications Sequence (option 5)
• MAT 170 CALCULUS FOR LIFE SCIENCES I
• MAT 171 CALCULUS FOR LIFE SCIENCES II
• MAT 149 CALCULUS WITH INTEGRATED PRECALCULUS III  
• or MAT 152 CALCULUS III
• or MAT 162 CALCULUS FOR MATHEMATICS AND SCIENCE MAJORS III
  
• MAT 260 MULTIVARIABLE CALCULUS I
• MAT 262 LINEAR ALGEBRA
• Three (3) CDM Major Electives
• Three (3) MAT Major Electives
• One (1) CDM or MAT Major Elective

Note: Students may take CSC 243 PYTHON FOR PROGRAMMERS and one (1) Additional Major Elective in lieu of CSC 241 and CSC 242.

Major Electives

Students must take seven (7) Major Field electives chosen from the grouped list below. Of these, three (3) must be CDM courses and three (3) must be MAT courses, and one (1) could be either a CDM or MAT course.

Students must earn a grade of C- or higher in all major elective courses.

It is recommended that students concentrate on one or two areas for their advanced classes to achieve depth, but they are not required to do so. Students are strongly encouraged to discuss course selection with an advisor. Students may wish to arrange with a professor to take an independent study or a research experience (MAT 399 or CSC 399 or IT 300) in order to explore a subject more deeply than is possible in a scheduled course.

Theory of Computation 

The courses in the theory area explore the mathematical and logical foundations of computer science.

• MAT 302 COMBINATORICS
• MAT 303 THEORY OF NUMBERS
• MAT 310 ABSTRACT ALGEBRA I
• MAT 335 REAL ANALYSIS I
• MAT 336 REAL ANALYSIS II
  
• MAT 351 PROBABILITY AND STATISTICS I
  
• MAT 370 ADVANCED LINEAR ALGEBRA
  
• MAT 372 LOGIC AND SET THEORY
• CSC 235 PROBLEM SOLVING
• CSC 327 PROBLEM SOLVING FOR CONTESTS
• CSC 333 CRYPTOLOGY
• CSC 344 AUTOMATA THEORY AND FORMAL GRAMMARS
• CSC 347 CONCEPTS OF PROGRAMMING LANGUAGES
• CSC 348 INTRODUCTION TO COMPILER DESIGN
• CSC 358 SYMBOLIC PROGRAMMING
• MAT 387 OPERATIONS RESEARCH: LINEAR PROGRAMMING
• CSC 389 THEORY OF COMPUTATION

Computational Methods

The computational methods area investigates quantitative and computational methods in computer science

• CSC 331 SCIENTIFIC COMPUTING
• MAT 304 DIFFERENTIAL EQUATIONS
  
• MAT 330 METHODS OF COMPUTATION AND THEORETICAL PHYSICS I
  
• MAT 331 METHODS OF COMPUTATION AND THEORETICAL PHYSICS II
  
• MAT 359 SIMULATION MODELS AND MONTE CARLO METHOD
  
• MAT 384 MATHEMATICAL MODELING
  
• MAT 385 NUMERICAL ANALYSIS I
• MAT 386 NUMERICAL ANALYSIS II

Artificial Intelligence 

For students with an interest in the computational relations between syntax and semantics.

• CSC 380 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE
• CSC 357 EXPERT SYSTEMS
• CSC 358 SYMBOLIC PROGRAMMING

Data Science Area

For students who are interested in statistical and computational analysis of data. Many of the courses in this area require the student to take MAT 351-353.

• DSC 324 ADVANCED DATA ANALYSIS (FORMERLY CSC 334) 
• or MAT 354 MULTIVARIATE STATISTICS
• DSC 341 FOUNDATIONS OF DATA SCIENCE (FORMERLY CSC 367)
• DSC 333 INTRODUCTION TO BIG DATA PROCESSING
  
• DSC 345 MACHINE LEARNING
  
• DSC 365 DATA VISUALIZATION (FORMERLY DSC 350)
• MAT 341 STATISTICAL METHODS USING SAS
  
• MAT 349 APPLIED PROBABILITY
• or MAT 351 PROBABILITY AND STATISTICS I
• MAT 352 PROBABILITY AND STATISTICS II
• MAT 353 PROBABILITY AND STATISTICS III
• MAT 354 MULTIVARIATE STATISTICS
  
• MAT 355 STOCHASTIC PROCESSES
• MAT 356 APPLIED REGRESSION ANALYSIS
  
• or  DSC 323 DATA ANALYSIS AND REGRESSION (FORMERLY CSC 324)
  
• MAT 357 NONPARAMETRIC STATISTICS
• MAT 358 APPLIED TIME SERIES AND FORECASTING
• MAT 359 SIMULATION MODELS AND MONTE CARLO METHOD
• MAT 360 GENERALIZED LINEAR MODELS

Computer Vision

Computer vision studies the mathematical and algorithmic underpinnings of image analysis and image processing. 

• MAT 261 MULTIVARIABLE CALCULUS II
• MAT 335 REAL ANALYSIS I
• MAT 370 ADVANCED LINEAR ALGEBRA
  
• MAT 381 FOURIER ANALYSIS AND SPECIAL FUNCTIONS
• MAT 384 MATHEMATICAL MODELING
• MAT 387 OPERATIONS RESEARCH: LINEAR PROGRAMMING
  
• MAT 388 OPERATIONS RESEARCH: OPTIMIZATION THEORY
  
• CSC 381 INTRODUCTION TO DIGITAL IMAGE PROCESSING
• CSC 382 APPLIED IMAGE ANALYSIS

Research

• CSC 395 RESEARCH COLLOQUIUM  
• CSC 399 INDEPENDENT STUDY
• MAT 390 MATHEMATICS READING AND RESEARCH
• MAT 396 SENIOR THESIS RESEARCH

Open Electives

Open elective credit also is required to meet the minimum graduation requirement of 192 hours.  

See www.cdm.depaul.edu to see sample schedule of course requirements on a year-by-year basis.

Degree Requirements

Students in this degree must meet the following requirements:

• Complete a minimum of 192 credit hours (generally 48 courses) 
• Earn a grade of C- or higher in WRD 103, WRD 104, and all Major and Minor courses 
• Earn a grade of D or higher in all other Liberal Studies and Open Elective courses
• Maintain a cumulative GPA of 2.0 or higher