Dec 21, 2024  
2021-2022 Graduate Catalog (As of 09-20-21) 
    
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2021-2022 Graduate Catalog (As of 09-20-21) [ARCHIVED CATALOG]

Data Science and Analytics, M.S.

Navid Hashemi, Program Director
843.953.0428
hasemin@cofc.edu
https://datascience.cofc.edu

Mission Statement

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.

Program Description

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:

  1. Graduates will demonstrate advanced and applied knowledge of computer programming, data organization, data mining, data visualization, and algorithms.
  2. Graduates will demonstrate an advanced understanding in the core area of mathematics and statistics, including optimization, machine learning, regression, and linear algebra.
  3. Graduates will demonstrate an application of their data science graduate coursework through the completion of a Practicum Experience or Research Thesis.

Admissions Requirements and Application Deadlines


Institutional Admissions Requirements

  • A completed application form with a nonrefundable application fee of $60.
  • 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 catalog for information on providing appropriate documentation with the application.

Program Admissions Requirements

  • GRE. Submission of an official Graduate Record Examination (GRE) test score. 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.

Application Deadlines

  • Fall: No Fall Admission
  • Spring: No Spring Admission
  • Summer: January 15, priority*; April 1, final

*Candidates who submit a completed application by the priority deadline will automatically be considered for Graduate School and/or graduate program funding.

 

Transfer Credit Policy

No program specific guidelines. Please refer to the Graduate School’s transfer credit policy .

 

Program Requirements
Program Learning Outcomes