Applied Mathematics: Data Science and Cryptography, Bachelor of Science
The Applied Mathematics major has two concentrations, Data Science and Cryptography. The Data Science concentration presents the principles of data representation, big data management, and statistical modeling. Students learn to use modern computing techniques to reveal hidden causal and temporal relationships within large data sets. Hidden information is often benign but it might also be evidence of malevolent activities that have already occurred or are in progress. Cryptography is the science of both personal and institutional data security. Students learn to secure information, maintain data integrity, authenticity, and non-reputability. Cryptologists play a vital role in detecting events yet to unfold, especially when attempting to interdict and thwart incipient cyber intrusions and terrorist attacks.
The curriculum offers an integrated academic program with the depth and breadth necessary to make graduates truly competitive in the job market. Both concentrations provide the knowledge and the skills that are in demand in high tech entrepreneurship, finance, modern communications, medicine, security, transportation, and manufacturing. The New York City metropolitan region is being repositioned as a nexus of technological innovation and discovery as well as a haven for entrepreneurial leadership. Such a metamorphosis requires the availability of a renewable work force possessing skills in data analysis and data security. Consequently, employment opportunities are expected to be available for applied mathematics graduates for the foreseeable future.
Those individuals that opt to undertake graduate study will find that they are well prepared to enroll in a wide range of Masters and Doctoral programs such as Digital Forensics and Cyber Security, Financial Mathematics, Machine Learning, traditional Mathematics, and Mathematics Education. Indeed, the required mathematics core aligns well with the core requirements of other CUNY mathematics programs thereby affording graduates the widest possible choice of subsequent educational opportunities.
Learning Outcomes. Students will:
- Apply the principles of mathematical proof and deductive logic to prove level appropriate mathematical statements or create counterexamples within the context of the real number axioms and the axioms defining various algebraic structures.
- Apply the mathematical modeling process to modern problems in data science and cryptography for the purpose of analyzing large data sets and encrypting plain text or decrypting cipher text.
- Function effectively in an interdisciplinary team environment and express quantitative information effectively to others.
- Identify and adhere to the ethical constraints of respecting personal data privacy and evaluate and assess ethical standards for the application of cryptographic algorithms in contemporary contexts.
Credits Required.
Applied Mathematics: Data Science & Cryptography Major
|
54-57 |
| General Education |
42 |
| Electives |
21-24 |
| Total Credits Required for B.S. Degree |
120 |
Co-Coordinators. Professors Michael Puls (212-484-1178, mpuls@jjay.cuny.edu) and Hunter Johnson (212-237-8846, hujohnson@jjay.cuny.edu), Department of Mathematics and Computer Science.
Advisors. Professors Hunter Johnson (212.237.8846, hujohnson@jjay.cuny.edu), Shaobai Kan (646.557.4866, skan@jjay.cuny.edu), Michael Puls (212.484.1178, mpuls@jjay.cuny.edu), Antoinette Trembinska (212.237.8838, atrembinska@jjay.cuny.edu), Department of Mathematics and Computer Science
Advising information. Applied Mathematics Advising Resources Page (including a Sample Four Year Advising Plan)
Additional information. Students who enrolled for the first time at the College or changed to this major in September 2023 or thereafter must complete the major in the form presented here. Students who enrolled prior to that date may choose the form shown here or the earlier version of the major. A copy of the earlier version may be obtained in the 2022-2023 Undergraduate Bulletin.
Foundational Courses
May be required depending on mathematics placement
Advisor recommendation:
MAT 141 fulfills the Required Core: Mathematics and Quantitative Reasoning area of the Gen Ed Program.
Total Credit Hours: 0-3
Part One. Core Courses
Required
| CSCI 171 | The Nature of Computers and Computing | 3 |
| CSCI 172 | Introduction to Data Analysis | 3 |
| ENG 253 | Technical Writing in Computer Science, Math, and Science | 3 |
| MAT 151 | Calculus I | 4 |
| MAT 152 | Calculus II | 4 |
(The new calculus sequence
MAT 151,
MAT 152,
MAT 253 is equivalent to the former calculus sequence
MAT 241-
MAT 244. Please consult an advisor for proper placement if you have already completed any courses in the former calculus sequence.)
Total Credit Hours: 17
Part Two. Mathematics Core Courses
Required
| MAT 253 | Calculus III | 4 |
| MAT 265 | Elements of Mathematical Proof | 3 |
| MAT 301 | Probability & Mathematical Statistics I | 3 |
| MAT 302 | Probability and Mathematical Statistics II | 3 |
| MAT 310 | Linear Algebra | 3 |
| MAT 341 | Advanced Calculus 1 | 3 |
| MAT 351 | Introduction to Ordinary Differential Equations | 3 |
(The new calculus sequence
MAT 151-
MAT 152,
MAT 253 is equivalent to the former calculus sequence
MAT 241-
MAT 244. Please consult an advisor for proper placement if you have already completed any courses in the former calculus sequence.)
Total Credit Hours: 22
Part Three. Concentrations
Students must choose one concentration and complete three courses
Concentration A. Data Science
Data Science plays a critical role in analyzing large data sets which may have valuable information that is obscured by the sheer volume of the data itself. In the Data Science concentration, students will learn the principles of data representation, big data management, and statistical modeling. They will also be able to use computers to reveal hidden causal and temporal relationships in large data sets.
Learning outcomes for Data Science Concentration. Student will:
- Use mathematical methods to analyze and recognize the properties of large data sets as well as any anomalies.
- Use suitable models such as linear regression, logical regression, to analyze data and predict probability distributions.
- Recognize clustering in large data sets and explain its significance.
Required
Concentration B. Cryptography
Cryptography is the science of data security, both personal and institutional, and as such is also an important component of justice. In the Cryptography concentration, students will learn to secure information which is achieved by assuring privacy as well as other properties of a communication channel, such as data integrity, authenticity, and non-reputability, depending upon the application. They will devise systems for companies to resist the unwarranted intrusions of hackers, to protect internal company and consumer data, and to act as consultants to research staff concerning the implementation of cryptographic and mathematical methods.
Learning outcomes for the Cryptography Concentration. Students will:
- Use the mathematics upon which specific cryptographic algorithms are based to analyze the strengths and weaknesses of cryptographic schemes.
- Guarantee authenticity and integrity of data and ensure that transactions are non-repudiable, when appropriate.
- Develop cryptographic algorithms.
RequiredTotal Credit Hours: 9
Part Four. Electives
Choose two
Total Credit Hours: 6
Total Credit Hours: 54-57