Certificate in Data Science
The Certificate in Data Science program is intended for professionals working in healthcare or related industries. It is designed for students who have strong interests in developing professional data science skills that can be applied to public health or other challenges
Eligibility for the certificate program will include a minimum of a Bachelor's degree from a regionally accredited school. The application requirements consist of a cumulative GPA of 3.0 or greater transcripts, goal statements, and a reference letter. The prerequisites for this program include previous knowledge of calculus, linear algebra and basic knowledge of computer programming. GRE is not required for the Certificate program.
1 Online Application: SOPHAS EXPRESS (except Public Health Informatics)
2 Application Fee $50
International Graduate Certificate Applicants
For students with international transcripts:
- Foreign transcripts must be evaluated by World Education Services (WES) ONLY. A course-by-course transcript ICAP evaluation is required
- TOEFL/IELTS scores (within last two years)-
All international applicants must take the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS) . This requirement applies even if you attended a US undergraduate or graduate institution, or had postsecondary education conducted in English. Our institution code is 5688. No department code is needed.
A minimum acceptable score of:
- TOEFL: 600 on paper-based, 250 on computer-based, or 100 on internet-based
- IELTS: Overall Score 7.5
Exception to TOEFL/IELTS requirement:
- If you are a Permanent Resident or Citizen of the United States
- The applicant is a citizen of a country where the sole official language is English
We recommend that international applicants who hold F-1 visas should contact the Office of International Affairs to verify eligibility for enrollment as a non-degree seeking student.
NOTE: UTSPH does not sponsor student visas for international non-degree applicants (Please e-mail firstname.lastname@example.org to verify visas that are eligible for the Non-Degree program)
This non-degree program consists of coursework in data science, data analytics and predictions, analytic methods, and data management (14 graduate-level semester credit hours) designed to meet the needs of students and employers. Classes are available at all School of Public Health six campuses.
A certificate is awarded to students who pass all courses. The Certificate in Data Science program is designed to be completed in one (1) year.
PH 1690 Foundations of Biostatistics.
PH 1700 Intermediate Biostatistics.
PH 1975 Introduction to Data Science.
PH 1976 Fundamentals of Data Analytics and Predictions.
Students in the certificate program can also consider applying to one of the degree programs at the school but it does not guarantee admissions into a degree-seeking program.
Mission of Data Science Track
Data Science is an emerging field from (bio)statistics and computer science. Training of Data Science professionals requires courses of both fundamental statistics and computational approaches. The Department of Biostatistics & Data Science has experienced faculty with a strong computer science background and is offering a variety of Data Science courses. In addition, the Data Science track further strengthens and expands our existing education programs in MPH, MS and PhD in biostatistics, making our programs more competitive among its peers. Furthermore, our program produces professionals with the up-to-date skills to accomplish our school’s mission and to improve the health of the people of Texas, the nation, and the world by providing the highest quality graduate education of biostatistics and data science, and by supporting translational research and services to the profession and community via quantitative and data science approaches.
Background and Rationale
A huge amount of data is generated every day, in particular from biomedical and health science research projects and practices, including genomics, epidemiological studies, behavioral science, wearable device data, internet, mobile health, EHR/EMR and insurance claim data. The lack of expertise in data science, such as skills of Big Data management, processing, analysis and integration, will be a bottleneck in both Big Data research and health care practice. This makes our established Data Science Track a supremely valuable asset that prospective students can leverage within the job market.
- Students will be provided the opportunity to develop their skills and thinking in identifying attractive research questions and effective practical solutions. They will learn the essential data science languages and computing skills to work with data and understand the complete process or cycle of study design, data collection, data management, data analysis and data integration.
- Students will gain hands-on experiences with the latest database systems and data storage, management, query and retrieval techniques, and learn how to visualize, summarize, interpret, and communicate both data and findings.
- In addition to fundamental statistical principles and data analysis methodologies, students will also learn classic and up-to-date machine learning as well as deep learning methods, predictive models and tools that can be directly applied to real-world data (e.g., text, audio, video, EHR, imaging and streaming data).
- Students are also expected to gain experience with mainstream machine learning packages and libraries to be able to analyze health science data and problems.
Students enrolled in masters and doctoral degree programs in Biostatistics may choose the Data Science track. They must, however, fulfill the required prerequisite courses before taking the core courses. Direct admission to the Data Science Track will follow the same procedure as our current process for admission to the MS/PhD programs in Biostatistics.
The MS degree in Biostatistics with Data Science track requires completion of a total of 36 credit hours (including maximum of 6 credit hours for MS thesis). In addition to the required minor courses and other required public health courses (4 credit hours), eight (8) major courses, including 4 basic biostatistics courses and 4 data science courses, are required. More biostatistics and data science elective courses are also available. The Department of Biostatistics & Data Science will also expand its minor course of study to include a data science track for MS students who are majoring in other public health disciplines. Courses required for the minor in Data Science track include PH 1690 Foundations of Biostatistics, PH 1700 Intermediate Biostatistics, and at least two (for MS) or three (for doctoral) additional courses in Data Science chosen from PH1975, PH1976, PH1977 and PH1978.
The PhD degree in Biostatistics with the Data Science track requires completion of a total of 48 credit hours after completing a MS degree in Biostatistics, Data Science, or relevant majors (it requires 72 credit hours for those who do not have a MS degree in relevant majors). In addition to the required Minor/Breadth courses and other required public health courses (3 credit hours), ten (10) major courses, including 6 fundamental biostatistics methodology and theory courses and 4 advanced data science courses, are required. A seminar course and teaching method course are also required for PhD students. More advanced biostatistics and data science courses are also available as elective courses for doctoral students. The Department of Biostatistics & Data Science will also expand its minor course of study to include a data science track for PhD students who are majoring in other public health disciplines. Courses required for the minor in Data Science track include PH 1690 Foundations of Biostatistics, PH 1700 Intermediate Biostatistics, and at least two (for MS) or three (for doctoral) additional courses in Data Science chosen from PH1975, PH1976, PH1977 and PH1978.