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Data Science Program

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Program Overview/Mission

Like electricity and the Internet in the past, data has increasingly become a core fuel for current and future development. Data science jobs are well-paid, plentiful, satisfying, meaningful, and respected. Demands for data science professionals are very strong and keep growing. Data science jobs are challenging, exciting, and purposeful. Data science is also versatile and applicative. As an emerging (or re-emerging) technology, there are strong opportunities and potentials for successfully applying data science on areas you are passionate about. 

The Master of Science (MS) degree program in Data Science (DS) is designed to prepare students to become effective data science professionals both in general and in specific domain areas. The program covers core data science knowledge areas, as well as practical data science applications. Students can select from three specialization areas or create their own selection of elective courses for their favorite application domains.

Why Choose Data Science at UHCL?

When it comes to earning a graduate degree, we know you have options, but not all universities offer the same advantages. Here are a few benefits of UHCL's Data Science program: 

  • It is a unique program in the Greater Houston area, with a curriculum that aligns with the needs of the surrounding industry. 
  • It is designed as a Master program for diverse students with varied undergraduate backgrounds. Success in data science goes beyond science and technology. A keen interest in solving problem using data in your favored areas is equally important. 
  • The program provides three rewarding specializations: business analytics, bioinformatics, and big data and cloud solutions. Students can also work with their faculty advisors to take elective courses beneficial to their specific interests and career paths. 
  • It is designed as a truly multi-disciplinary degree program for providing a broad background necessary for accomplishment in data science, with required and supportive courses from many areas. 
  • The program is practical-oriented. Its faculty partners with the surrounding industry to ensure program’s relevance and applicability. Students are required to take a practical capstone project usually sponsored by our industrial partners. 
  • Expertise and publications of the faculty members of the program not only include core areas of data science, such as data mining, data analytics, machine learning, deep learning, statistical methods, big data, data visualization, etc. They also include other leading converging technologies that complement data science, such as Internet of Things, cybersecurity, and blockchain. 
  • The program has small class sizes, and prides itself on being student-oriented and helpful.

Program Mission 

The mission of the Data Science program is to equip students with the capability of integrating a wide spectrum of interdisciplinary knowledge and skills to uncover and utilize data in order to produce, apply, and communicate value-adding intelligence for organizations and the society, in various key technical, analytical, architectural, and managerial positions. 

Goals and Objectives

Program Educational Outcomes (PEO)

The Program Educational Objectives (PEO) of the MS in Data Science (DS) program are defined as what graduates are expected to attain within a few years following graduation:  

  1. DS graduates will be professionally employed, solving data problems and providing data-driven solutions in diverse application domains to satisfy the rapid changing industrial and organizational needs in the local region, state, and nation.  
  2. DS graduates will hold up to a lofty professional standard, with high regards for the ethical, environmental, societal, economic, safety and global considerations in their professional pursuits. 
  3. DS graduates will work effectively as a member or leader of multidisciplinary teams in a diverse environment. 
  4. DS graduates will continue to grow professionally through life-long learning activities such as pursuing formal study, research, or continuing education. 
  5. DS graduates will be considered valuable resources for their participation and service to the society and communities.

Student Learning Outcomes (SLO)

At the time of graduation, Data Science graduates are expected to demonstrate the following abilities.

  1. Critical Thinking: DS students will be able to integrate and apply critical thinking theories and skills into the construction of effective data-driven insights and solutions to substantial problems. 
  2. Ethics and Professionalism: DS students will be able to demonstrate a broad and deep understanding of ethical issues in DS, adhere to DS professional standards, and engage in life-long learning in DS. 
  3. Data Science Theory and Methodology: DS graduates will be able to apply DS theory and technology to effectively acquire, store, process, and analyze data, communicate data analysis results, and construct data-driven solutions. 
  4. Range of Application Domains: DS students will be able to demonstrate the capability to apply DS theory and practices to understand and specify problems in a range of application domains and construct data solutions. 
  5. Communications: DS students will be able to clearly convey, articulate, and present actionable analytics, including the use and creation of data communications and exploration tools. 
  6. Teamwork: DS students will be able to work in a team environment effectively. 

Program Degree

Admission Requirements

Take your education to new heights in University of Houston-Clear Lake's College of Science and Engineering. Learn the next steps to being admitted now.

  • Data Science M.S.
  • Admission Deadline
    Standard Graduate Admission Deadline
  • Application
    A standard university application is required.
  • Requirements
    • GPA of 3.0 in an undergraduate degree.
    • GRE Total Score of 300 (155 Quantitative and 140 Verbal).
      • GRE is not required for students with a Master or Ph.D. degree or a Bachelor degree from an accredited US university.
    • Foundation Courses (C or better in the following courses or their equivalent): DASC 4301 - Python Programming for Data Science Credit Hours: 3, and STAT 3334 - Probability and Statistics for Scientists and Engineers Credit Hours: 3

Additional Admission Information

For more admission information, visit the Admissions page.

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