Statistics and Data Science (MS) - Data Science Track

Program Overview

The Master's in Statistics and Data Science with a concentration in Data Science equips graduates with the skills to analyze large datasets, uncover trends, and make data-driven decisions. The program emphasizes practical application with industry-standard software and offers opportunities for real-world project work with industry partners. Graduates are prepared for in-demand careers as data scientists, analysts, and engineers across various industries.

Program Outline

Degree Overview:

The Master of Science in Statistics and Data Science, Data Science track, is a program designed to equip students with the skills and knowledge necessary to analyze massive datasets, uncover trends and associations, and make informed decisions in various fields.

Objectives:

The program aims to:

• Provide students with a comprehensive understanding of data science principles and methodologies.
• Develop students' ability to analyze large datasets and extract meaningful insights.
• Equip students with the skills to apply data science techniques to solve real-world problems in various domains.
• Prepare students for successful careers in the growing field of data science.

Description:

The program is particularly suited for individuals who have completed an undergraduate program in mathematics, statistics, economics, business, or related fields. It is designed to meet the increasing demand for data scientists, a profession with high job demand and limited qualified professionals. The program emphasizes the practical application of data science techniques, making it relevant to the current needs of the industry.

Outline:

Program Structure:

• The Data Science track in the Statistics and Data Science MS program consists of 36 credit hours.
• This includes 24 credit hours of required courses and 6 credit hours of restricted electives.
• Students must also complete a thesis or research project, along with an additional elective.

Course Schedule:

• Required Courses (24 credit hours):
• STA5104 - Advanced Computer Processing of Statistical Data (3)
• STA6714 - Data Preparation (3)
• STA6238 - Logistic Regression (3)
• STA6326 - Theoretical Statistics I (3)
• STA6327 - Theoretical Statistics II (3)
• STA6236 - Regression Analysis (3)
• STA6704 - Data Mining Methodology II (3)
• Elective Courses (6 credit hours):
• Students must select electives from a list of courses, including options from Computer Science (COP prefix) and other departments.
• At least 2 courses must be selected from the provided list.
• Thesis/Nonthesis Option (6 credit hours):
• Thesis Option:
Students complete a thesis under the guidance of a thesis advisor and committee.
• Theoretical Statistics: These modules provide a foundation in statistical theory, including probability, distributions, and inference.
• Regression Analysis: This module explores various regression techniques for modeling relationships between variables.
• Other Elective Courses: The program offers a wide range of elective courses covering topics such as parallel and distributed database systems, advanced database systems, experimental design, categorical data methods, stochastic processes, statistical computing, sampling theory, nonlinear regression, nonparametric statistics, multivariate statistical methods, applied time series analysis, and more.

Assessment:

Thesis Option:

• Students must complete a thesis under the guidance of a thesis advisor and committee.
• An oral defense of the thesis is required.

Nonthesis Option:

• Students must complete a research project under the supervision of a research advisor.
• An oral presentation of the research project is required.

Teaching:

Teaching Methods:

• The program utilizes a variety of teaching methods, including lectures, discussions, group projects, and hands-on exercises.
• The focus is on practical application and real-world problem-solving.

Faculty:

• The program is taught by experienced faculty members with expertise in data science and related fields.

Unique Approaches:

• The program emphasizes the use of industry-standard software and tools.
• Students have opportunities to work on real-world projects with industry partners.

Careers:

Potential Career Paths:

• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Business Analyst
• Research Scientist
• Statistician

Opportunities:

• The program prepares students for careers in a wide range of industries, including technology, finance, healthcare, and research.
• Graduates are highly sought after by employers in the data science field.

Outcomes:

• Graduates of the program are equipped with the skills and knowledge to succeed in data science careers.
• They are able to analyze complex datasets, develop data-driven solutions, and contribute to organizational decision-making.

Other:

• The program requires students to have a strong foundation in mathematics and statistics.
• Students must maintain a minimum GPA of 3.0 in their Plan of Study.
• Financial assistance is available to eligible students through fellowships, assistantships, tuition support, or loans.