Data Science Graduate Certificate

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Graduate Certificate (GCDS)

This certificate program in Data Science aims to provide interdisciplinary training in the important aspects of the rapidly emerging area of Data Science. With large volumes of data being generated every day from multiple sources (including business data, biomedical data, educational data, science data, engineering data, and personal data), the importance of systematic and rigorous approaches to understanding and putting these large volumes of data to good use is now well recognized. Job growth in this area is in overdrive. The Data Science certificate is being proposed to help train a workforce of future data scientists able to tackle the challenges and reap the promising benefits of vast amounts of data available in all parts of the economy, society, and government. At the end of the program, students will exhibit a deep understanding of how to manage large volumes of data, discover patterns and make inferences from data, use models to make predictions about potential solutions, and quantify the reliability and effectiveness of the outcomes. Equipped with such knowledge and skills, the graduates will make significant contributions towards improving the products, processes, and services offered by commercial and non-commercial organizations as well as the government. The Data Science certificate program will be administered by the Computer Science Department, although some courses may be taught by different departments, and any graduate student meeting admission requirements to the certificate program will be eligible to join it from any other areas.

The specific objectives of the certificate program are to:

Provide training on data collection, storage, manipulation, visualization, and privacy;
Provide a strong background in programming, algorithms and methods for statistical analysis in data mining and machine learning;
Train students in the use of software tools and systems for processing big data;
Educate students on ethical, management, policies and legal requirements in the field of data science.

Admission Requirements

The certificate program in Data Science may be pursued concurrently with other graduate programs offered by the university. In particular, students currently admitted to any graduate program at the U of M may join this certificate program. To apply, students must submit the application form and transcripts of prior graduate study.

Non-degree graduate students may also pursue this certificate program of study. For example, professionals who have a BS in computer science, computer engineering, mathematics, or other fields and who are proficient with a programming language such as Java or a statistical package such as R, are eligible for admission into the program. Applicants satisfying these requirements are required to submit a brief (one- to two-page) statement of relevant educational and work experience in data analysis in addition to the requirements above. Prospective students must apply for admission both to the program and to the Graduate School.

GRE scores are not required for non-degree students’ admission into the Data Science certificate program. Acceptance to the certificate program does not imply acceptance into any other graduate in Computer Science.

Program Requirements

The certificate program requires completion of 12 semester credit hours (6 from the list of core courses and 6 from the list of electives) after admission into the certificate program.

Data Science Core Courses

COMP 7150 - Fundamentals of Data Science ** Credit Hours: (3)
COMP 8150 - Fundamentals of Data Science ** Credit Hours: (3)
COMP 7745 - Machine Learning ** Credit Hours: (3)
COMP 8745 - Machine Learning ** Credit Hours: (3)
or
COMP 6745 - Intro to Machine Learning Credit Hours: (3)

Note:

COMP 6001 - Intro to Python Programming or equivalent is required for taking any of the core courses.

Data Science Electives

COMP 6118 - Introduction to Data Mining Credit Hours: (3)
COMP 7115 - Database Systems Credit Hours: (3)
COMP 7116 - Adv Database Systems Credit Hours: (3)
COMP 8116 - Adv Database Systems Credit Hours: (3)
COMP 7118 - Data Mining ** Credit Hours: (3)
COMP 8118 - Data Mining ** Credit Hours: (3)
COMP 7130 - Inform Retrieval/Web Search Credit Hours: (3)
COMP 8130 - Inform Retrieval/Web Search Credit Hours: (3)
COMP 7740 - Neural Networks Credit Hours: (3)
COMP 8740 - Neural Networks Credit Hours: (3)
COMP 7747 - Adv Topics in Machine Learning Credit Hours: (3)
COMP 8747 - Adv Topics in Machine Learning Credit Hours: (3)
COMP 7780 - Natural Lang Processng Credit Hours: (3)
COMP 8780 - Natural Lang Processng Credit Hours: (3)
MATH 7670 - App Stochastic Models Credit Hours: (3)
MATH 8670 - App Stochastic Models Credit Hours: (3)
MATH 7680 - Bayesian Inference Credit Hours: (3)
MATH 8680 - Bayesian Inference Credit Hours: (3)
PSYC 7302 - Adv Statistics Psych I Credit Hours: (3)
PSYC 8302 - Adv Statistics Psych I Credit Hours: (3)

Retention

In order to continue in the program, students must maintain a GPA of at least 3.0.

Graduation

To obtain the certificate, a student must complete four of the above-mentioned courses (2 core courses and 2 electives), with an average grade of 3.0 (B) or higher, for a total of 12 credits, at least three of which must be from the 7000 level or higher.

Note:

A maximum of 15 credits may be transferred from certificate programs to non-related graduate degrees, subject to approval by the graduate program. Moreover, all credits from a certificate program may be transferred to a relevant graduate degree where the entire content of the given certificate could satisfy some of the requirements of a graduate degree. See specific department requirements for additional information and the Graduate School policy.