The Mission of the College of Charleston’s Master of Science in Data Science and Analytics degree program is to fill the growing demand for graduates with data-driven, quantitative, analytics, and computing skills - i.e., a data scientist.
There are several overlapping but distinct ways to identify and define a data scientist. At the heart of data science is the goal of knowledge discovery from data. This requires a core of specialized skills from the domains of computer science and mathematics complemented with significant exposure to a domain of specialization (e.g., business, science, social sciences, and humanities).
Graduates of this degree program will master the following core skills: Data Modeling, Data Wrangling, Experimental Design, Statistics, Optimization, Machine Learning, and Data Visualization. The core skills are complemented by domain-specific elective coursework. Recommended elective packages are provided which specifically prepare students for the following career goals: Machine Learning Data Scientist, Modeling and Software Engineering Data Scientist, Computational Data Scientist, Scientific Computing, and a Business Analytics Data Scientist. All students in this program will apply their core and domain-specific skills and knowledge to either an industry practicum or research thesis experience.
The specific learning objectives of the program are:
- Graduates will demonstrate advanced and applied knowledge of computer programming, data organisation, data mining, data visualization, and algorithms.
- Graduates will demonstrate an advanced understanding of the core area of mathematics and statistics, including optimization, machine learning, regression, and linear algebra.
- Graduates will demonstrate an application of their data science graduate coursework through the completion of a Practicum Experience or Research Thesis.
- CSIS 604 Distributed Computer Systems Architecture (3)
- CSIS 638 Implementation of Database Management Systems (3)
- DATA 505 Computational Data Science and Analytics (3)
- DATA 506 Mathematical Data Science and Analytics (3)
- DATA 507 Scientific Computing in Data Science (3)
- MATH 540 Statistical Learning I (3)
- DATA 510 Data Cleaning, Organization, and Visualization (3)
- DATA 534 Machine Learning, Data Mining, and Analytics (3)
- MATH 550 Linear Models (3)
- Complete 6 credit hours from the following:
- BIOL 612 Conservation Genetics (4)
- BIOL 649 Comparative Genomics (4)
- CSIS 602 Foundations of Software Engineering (3)
- CSIS 618 Programming Languages (3)
- CSIS 632 Data Communications and Networking (3)
- CSIS 654 Software Requirements Analysis and Specifications (3)
- CSIS 690 Special Topics in Computing (3)
- DATA 590 Special Topics in Data Science and Analytics (3)
- EVSS 549 Geographic Information Systems (4)
- EVSS 569 Advanced GIS: Environmental and Hazards Models (4)
- MATH 541 Statistical Learning II (3)
- MATH 545 Numerical Analysis I (3)
- MATH 551 Linear Programming and Optimization (3)
- MATH 552 Operations Research (3)
- MATH 555 Bayesian Statistical Methods (3)
- MBAD 503 Financial Management (3)
- MBAD 516 Financial Modeling (3)
- MBAD 521 Consumer Marketing Strategy (3)
- MBAD 522 Marketing Research and Analysis for Decision Making (3)
- MBAD 525 Marketing Management (3)
Thesis or Practicum Option
Complete 6 credit hours of either DATA 698 Practicum in Data Science and Analytics (3) or DATA 699 Thesis in Data Science and Analytics (3).
For the practicum option, the student is responsible for proposing a practicum project at a company conducting data science and analytics work. His/her practicum must directly relate to data science and analytics concepts at the graduate level. Students already employed in a data science field must perform additional data science tasks outside of their existing responsibilities. Practicum experiences must be approved by both the program director and the practicum course instructor prior to enrolling in DATA 698.
The thesis option requires a traditional research project characterized by a comprehensive paper on a research topic. A thesis faculty advisor must be identified by the student. The advisor chairs a Master’s Thesis Committee of at least three faculty members which must include the Data Science and Analytics Programme Director. The Committee must approve thesis proposals prior to a student enrolling in DATA 699. The Committee also ultimately decides if the student has successfully defended his/her thesis which is required to graduate.
Transfer Credit Policy
No program-specific guidelines.
- A completed application form with a nonrefundable application fee of $50.
- Official transcripts of all undergraduate and graduate coursework. An earned bachelor’s degree from an accredited college or university is required.
- International applicants should refer to the International Students area within the “Admissions Information” section of the catalogue for information on providing appropriate documentation with the application.
- GRE. Submission of an official Graduate Record Examination (GRE) test score. The test must be taken within five years of application. Acceptable GRE minimum score is a verbal and quantitative combination of 300 and 4.0 on the writing assessment.
- Pass an Entrance Exam. Prior to beginning their first graduate courses, all students entering this program must pass a proficiency test that demonstrates pre-requisite knowledge in the areas of fundamental programming, computer science, mathematics, and statistics. The test is administered by the program director. Computing topics covered on the proficiency test include: branching and iteration, String manipulation, guess and check, approximations, bisection, decomposition, abstractions, functions, tuples, lists, aliasing, mutability, cloning, recursion, dictionaries, testing, debugging, exceptions, assertions, object-oriented programming, classes and inheritance, understanding program efficiency, searching and sorting. Statistics topics covered on the test include: random variables, distributions, quantiles, mean-variance, conditional probability, Bayes’ theorem, base rate fallacy, joint distributions, covariance, correlation, independence, central limit theorem, Bayesian inference with known priors, probability intervals, conjugate priors, Bayesian inference with unknown priors, frequentist significance tests and confidence intervals, resampling methods: bootstrapping, linear regression. For more details on how to prepare for the entrance exam, contact the program director.
- Provide Statement of Purpose. A 300-500 word statement of purpose is required. Applicants should discuss their goals after obtaining the master’s degree and what the applicant believes he/she will contribute to the program.
- Provide Letters of Recommendation. Two letters of recommendation that should provide specifics on the applicant’s motivation and ability to complete the program are required.
- Fall: No Fall Admission
- Spring: No Spring Admission
- Summer: February 1
About the School
Located in the heart of historic Charleston, South Carolina, the College of Charleston is a nationally recognized public liberal arts and sciences university. Founded in 1770, the College is among the ... Read More