Co-terminal Degree Program:
Bachelor of Science in Applied Mathematics
+Master of Data Science
April 8, 2014
Highlights/Narrative Part of the Proposal
We propose a new co-terminal degree B.S. in Applied Mathematics /Master of Data Science
program, so that the students can take advantage of the sharing of credit hours.
Description of the courses shared between the undergraduate (B.S. in Applied
Math) and graduate (Master of Data Science, MDS) programs
Our department offers a few dual-delivery courses, which allows students to choose courses
shared between the undergraduate and graduate programs easily. For example, our department
has Math 484 (Regression and Forecasting) and Math 564 (Applied Statistics), which have the
same lectures but different homework assignments, projects and exams. Students in the
proposed co-terminal BS/MDS degree program will take the 500-level version of three such
courses which then can be counted toward their undergraduate program as shared credits.
Even if a student in the program does not choose these dual-delivery 400/500-level courses,
(s)he still can share 9 credit hours easily because our MDS program allows for 9 credit hours at
the 400-level counted toward the total degree hour requirement (33 credit hours).
Motivation and Overview
The proposed dual-degree program is designed for students wishing to obtain a combined BS in
Applied Math and Master of Data Science. A typical Professional Master degree requires 15
months after the BS degree. This program can potentially reduce the time to a Professional
Master degree by one semester or more for bright students with sufficient GPA and credits.
Given that incoming students typically have had advanced courses during their high school, a
faster and, if preferred, research oriented program can also be opted for. This is especially true
for Camras scholars who are typically keen to continue for graduate studies and participate in
research. A sample five-year plan is provided below. In the following sample program, students
might finish even earlier if they took courses during summers.
The advantages of a combined degree program are typically:
i. Provide high-quality students opportunities to obtain both BS and Professional Master
degrees within five years, cutting the length of Professional Master degree by up to at
least one semester;
ii. Offer students chances to link the advanced undergraduate course work with graduate
course work;
iii. Provide students with research opportunities during their early years of study;
iv. Prepare bright students for professional careers, or a PhD program in Applied
Mathematics, Computer Science, or Statistics in an elite graduate school;
v. Offer an enhanced academic environment with accelerated learning;
vi. Simplify the graduate admission procedure.
Admission and Continuation in the Program
Because the program is designed for high quality students who are determined to get an
advanced degree, the math course and computer science loads are heavy in most semesters.
The students satisfying the following requirements are eligible to apply to the BS/MDS
combined program:
1. The student must have completed four semesters of full-time study in the BS program, or
have at least 80 credit hours (including transfer credits).
2. The student must have an overall GPA of 3.0 or better for all courses.
3. The student must have a GPA of 3.25 or better for math and computer science courses.
Applications must be accompanied by an official transcript and three letters of recommendation.
At least two letters of recommendation are from departmental faculty members. These two
letters shall attest to readiness and probable success in completing the graduate program. GRE
scores are not required for this dual degree program. Applications are reviewed and processed
by the Graduate Director of the department.
The student must maintain a graduate GPA of 3.00 or better in order to continue the combined
program. The student will obtain two degrees, one a BS in Applied Mathematics and the other
Master in Data Science. Credit requirements of both degrees subject to credit sharing of up to 9
credits as discussed above.
The students in the program may choose to obtain a BS degree only and not to complete the
requirements of the MDS degree. The credits hours in graduate-level math courses will be
counted toward the BS degree requirements as Applied Math Electives or Free Electives.
Students in the program cannot obtain the MDS degree unless they satisfy the requirements of
the BS degree and get a BS degree in Applied Mathematics.
Financial Assistance
Students may be offered Research Assistantships at any time during the program. Students in
the program will be eligible for all scholarships and financial aid packages for undergraduates,
as long as they don’t have BS degree. They are eligible for Dean’s Fellowship when they have
obtained equivalent credit requirement for BS degree and still have at least 18 credit hours to
complete the combined degree program. They can also apply for financial loans for graduate
students if they get the BS degree. Furthermore, students in the program can apply for external
funding. For example, they can apply for the NSF Graduate Fellowship in their fourth year and
fifth year in the five-year program.
Sample Program
Five-year Co-Terminal Degree Program
Should a student come in with AP credit for at least MATH 151 (Calculus I), and then no
summer courses will be required. This is a very common scenario.
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Humanities and Social Science Electives: 21 credits
IPRO I & II: 6 credits
Science Electives: 9 credits
Minor Electives: Database Management in CS (15 credits); includes CS115/116, CS 331, CS
422 or CS 429, CS 425 and CS 445.
Free Electives: 2 Free Elective.
Replace one Free Elective in the original BS in Applied Math with COM 523. Originally there
are 3 Free Electives for the BS in Applied Math program.
Applied Math Electives: 18 credits. Students are advised to take more courses related to DS
9-credit sharing between BS and MDS: 1 minor electives, 1 free-elective in the 7th semester
and 8th semester and Math 564 (can be counted toward BS as it is dual-delivered as Math
484)
Total credits=128 (for BS in AM) + 33 (for Master of Data Sci) – 9 (shared)=152
Semester 1
MATH 100 Introduction to the
Profession
MATH 151 Calculus I
CS 115 Introduction to Computer
Programming I
Science Elective 1
Humanities 100-level Elective
Total Hours
Credits
3
Semester 2
MATH 152 Calculus II
MATH 230 Introduction to Discrete Math
CS 116 Objective-Oriented
Programming II
PHYS 123 General Physics I
Social Science Elective 1
Total Hours
Credits
5
3
2
Semester 3
MATH 251 Multivariate and Vector Cal
MATH 332 Elementary Linear Algebra
CS 331 Data Structures and Algorithms
(Minor Elective 1)
Science Elective 2
Humanities or Social Science Elective
Total Hours
Credits
4
3
3
Semester 4
MATH 252 Introduction to Diff Equation
Credits
4
5
2
3
3
16
4
3
17
3
3
16
MATH 350 Intro to Computational Math
Science Elective 3
Minor Elective 2 (CS 422 or CS429)
Social Science Elective 2
Total Hours
3
3
3
3
16
Semester 5
MATH 430 Applied Algebra or
MATH 454 Graph Theory
MATH 475 Probability
Applied Math Elective 1
CS 425 Data Base Organization (Minor
Elective 3)
Humanities Elective (300+)
Free Elective 1
Total Hours
Credits
3
Semester 6
MATH 402 Complex Analysis
Applied Math Elective 2
Applied Math Elective 3
Social Science Elective 3
Free Elective 2
IPRO 397
Total Hours
Credits
3
3
3
3
3
3
18
Semester 7
Credit
s
3
3
MATH 400 Real Analysis
Applied Math Elective 4(Data Science
Elective)
Minor Elective 4 (CS 445)
Humanities Elective (300+)
IPRO 497
Total Hours
3
3
3
3
3
18
3
3
3
15
Semester 8
Applied Math Elective 5
Applied Math Elective 6
Minor Elective 5 (Data Science Elective)
Free Elective 3 (COM 523
Communicating Sciences)
Total Hours
Credits
3
3
3
3
Semester 9
CS 525 Advanced Database
Organization or CS 554 Data-Intensive
Computing
MATH 564 Applied Statistics
Or MATH 563 Mathematical Statistics
CSP/MATH 570 Data Science Seminar
Data Science Elective
Total Hours
Credits
3
12
3
1
3
10
Semester 10
CS 584 Machine Learning or
MATH 569 Statistical Learning
CSP/MATH 571 Data Preparation and
Analysis
CS 587 Software Project Management
Total Hours
Credits
3
Summer
CSP/MATH 572 Data Science
Practicum
Total Hours
Credits
6
3
3
9
6
Undergraduate Program Directions
Undergraduate Program Type: Bachelor of Science in Applied Mathematics
Total Undergraduate Program Credit Hours (including shared credit): 128 hours
Program Description:
Program Purpose: To combine the Applied Math BS degree an MAS in Data Science
degree.
Program Benefits:
 Allow students to complete both degrees in 5 years and be positioned better in a
competitive job market. This is roughly 6 months faster compared with the time it
would normally take to complete the degrees separately.
 Reduced credit hour requirements by sharing 9 credits between the programs.
 Enable students with scholarships to use them towards the completion of a
graduate degree (this may depend on the exact terms of the specific
scholarship).
 Simplify graduate admission procedures.
 Allow students to continue with graduate studies at greater ease while the
undergraduate material is still fresh in their mind.
Course Requirements:
For the program requirement of Applied Math BS Requirements, see
http://science.iit.edu/programs/undergraduate/bachelor-science-applied-mathematics
Graduate Program Directions
1) Program Overview: Master of Data Science
a) Describe the objective of the new program:
Students in the program can expect to obtain deep theoretical knowledge and
understanding of the assumptions made by machine learning methods; be grounded in
applied statistics; learn skills in object-oriented programming; identify scientific problems
and processes; and be capable of facilitating effective communication with both
scientific and non-scientific collaborators. In addition, students will work in teams on
real-world data science projects through a practicum at the end of the program. Unlike
many data analytics and business intelligence degrees, IIT’s program is firmly rooted in
computational science and applied mathematics. A well-designed multidisciplinary
degree program of this sort teaches students the essential theory, software engineering,
and communication skills data scientists need for success.
The Master of Data Science (MAS DS) is a professional master's degree jointly offered
by the Department of Computer Science and the Department of Applied Mathematics.
2) Program Justification:
a) Provide a detailed discussion on why the program is needed: To enable undergrads
to complete both degrees in 5 years using undergraduate scholarships/funding.
Graduates with both degrees are expected to be more competitive and have better job
prospects.
b) Provide a detailed description of the relationship of the proposed program to other
degree programs offered by IIT and by the academic unit: none
c) Provide an estimate of the expected number of students: 1-2 per year
3) Program Resources:
a) Describe the personnel requirements necessary to offer the program. Include faculty,
teaching assistant, and support staff. For faculty, indicate current faculty to be
associated with the program, detail any requirements for additional faculty hires, and
note the number of part-time faculty needed to support the program. Describe how and
when resources will be made available to hire any additional personnel that are
required: No additional resources needed. Students in the program will be part of the
normal BS and MS/MCS degree programs in the AM and CS departments.
b) Describe the facilities necessary to offer the program. Describe how and when
resources will be made available to obtain any additional facilities that are required: No
additional resources needed
4) Program Description:
a) Provide the detailed degree requirements for the program.
1) The minimum number of credit hours. 33
2) Whether a thesis is required, not required, or optional. OPTIONAL
3) Whether there is a project course. YES
4) Whether there is a project report. YES
5) Whether there is a comprehensive exam (for M.S. and MAS) NO
6) If a thesis is required, indicate that a thesis defense is required. NA
7) List all required courses. See details below
8) List all elective options. See details below
b) Indicate the admission criteria for the program. 3.25 GPA in AM and CS courses after
year 2. Students must maintain a minimum of 3.0 GPA throughout the program.
c) Provide a timeline and schedule for offering the program: Fall 2014
5) Description of courses shared between Undergraduate and Graduate programs (a
maximum of 9 credit hours may be shared course work):
a) Identify shared required courses. None.
b) Identify shared elective courses. : 1 minor electives and 1 free-elective in the BS
requirement and Math564 (can be counted toward BS as it is dual-delivered as Math 484)
c) Identify if course substitutions or exceptions are allowed and identify the restrictions
or limitations. None
Graduate Program Type: Master of Data Science (MAS DS)
List of Program Credit Hour Requirements:
Core Coursework
All MAS DS students must complete 6 core courses as follows: (at least 16 credit
hours).
Statistics (one of): MATH 563 MATHEMATICAL STATISTICS, MATH 564 APPLIED
STATISTICS
Machine Learning (one of): CS 584 MACHINE LEARNING, MATH 569 STATISTICAL
LEARNING
Data Processing (one of): CS 425 DATABASE ORGANIZATION (only for students
lacking computer science undergraduate degree or other computer science
experience), CS 525 ADVANCED DATABASE ORGANIZATION, CS 554 DATAINTENSIVE COMPUTING
Working with Data: CSP/MATH 571 DATA PREPARATION AND ANALYSIS
Communication (one of): COM 421 TECHNICAL COMMUNICATION, SCI 522 PUBLIC
ENGAGEMENT FOR SCIENTISTS
Project Management (one of): PHYS 563 PROJECT MANAGEMENT: BUSINESS
PRINCIPLES (2 credit hours), CS 587 SOFTWARE PROJECT MANAGEMENT
All students are also required to attend at least one semester of the CSP/MATH 570
DATA SCIENCE SEMINAR. (0 credit hour)
Data Science Practicum - Students are required to take CSP/MATH 572 DATA
SCIENCE PRACTICUM in which they will work in small groups to solve real-world data
analysis problems for actual scientific or industrial clients. Innovation and clarity of
presentation will be key elements of evaluation. Students also have an option to fulfill
course requirements through an approved data analytics internship. (6 credit hours)
Electives - MAS DS students may choose their 3 elective courses from the following list:
(9 credit hours)
Computation Fundamentals: CS 425 DATABASE ORGANIZATION, CS 430
INTRODUCTION TO ALGORITHMS, CS 450 OPERATING SYSTEMS, CS 535
DESIGN AND ANALYSIS OF ALGORITHMS, CS 546 PARALLEL AND DISTRIBUTED
PROCESSING, CS 553 CLOUD COMPUTING, CS 589 SOFTWARE TESTING AND
ANALYSIS
Computer Science Applications: CS 512 TOPICS IN COMPUTER VISION, CS 529
INFORMATION RETRIEVAL, CS 556 CYBER-PHYSICAL SYSTEMS: LANGUAGES
AND SYSTEMS, CS 557 CYBER-PHYSICAL SYSTEMS: NETWORKING AND
ALGORITHMS, CS 583 PROBABILISTIC GRAPHICAL MODELS, CS 585 NATURAL
LANGUAGE PROCESSING
Mathematics, Probability and Statistics: MATH 532 LINEAR ALGEBRA, MATH 540
PROBABILITY, MATH 542 STOCHASTIC PROCESSES, MATH 565 MONTE CARLO
METHODS, MATH 567 DESIGN OF EXPERIMENTS, MATH 574 BAYESIAN
COMPUTATIONAL STATISTICS
Mathematical and Scientific Computing: MATH 577 COMPUTATIONAL
MATHEMATICS I, MATH 578 COMPUTATIONAL MATHEMATICS II, MATH 590
MESHFREE METHODS, BIOL 550 BIOINFORMATICS AND BIOTECHNOLOGY,
PHYS 440 COMPUTATIONAL PHYSICS
Other relevant 500-level coursework may be taken for elective credit, with approval of
the program director.
Other Requirements
Students must take a total of at least 9 credits of CS/CSP coursework and at least 9
credits of MATH coursework to graduate, not including the capstone practicum course.
Prerequisite courses MATH 474, CS 401, and CS 402, if taken, will not count towards
degrees fulfillment.