Imagine a project involving numerous, linked Excel spreadsheets with over 1,000,000 rows of data with a dozen columns of attributes. Google processes over one million gigabytes of data daily. Tens of billions of financial shares are traded every day. Every week, astronomers collect over 100,000 terabytes of data from the heavens. Health professionals enter over terabyte of data monthly in the USA. This is big data.
The University of Houston – Downtown, offers an evening and weekend Master of Science in Data Analytics. This is an application-based master’s program that will allow motivated, ambitious students to learn the statistical and computation tools to assemble, structure, and analyze large data sets; learn to manage large projects involving big data; and effectively communicate findings, visually, orally, and in writing.
The Master of Science (MS) in Data Analytics is a theory and an application-based program that will provide students with a broad education in advanced statistics, digital data acquisition, digital data management, data analysis, and data presentation. The MS in Data Analytics is designed to meet the increasing need for highly skilled data analysts who can analyze the growing amount of data confronting in a variety of disciplines, and transform it into usable information for use in decision-making. The program is a university-wide collaboration that delivers rigorous training in statistical analysis and computational techniques, and provides mastery of data analysis tied to interdisciplinary applications. Students in the program study 21 hours of foundation courses with topics in regression analysis, multivariate analysis, experimental design, nonparametric analysis, statistical modeling, simulation, mathematical theory for data analysis, and statistical computing. Students also take 12 hours of interdisciplinary application courses in business management, science, criminal justice, education, communication, and social sciences. In the final 3 hours students will have the opportunity to engage in research or real-world applications with faculty members at UHD and other collaborators of UHD faculty and/or internships with partnering businesses, industry, and government agencies.
Program Learning Outcomes:
Students who complete the program will be able to:
Organize, manipulate, and summarize data in various formats.
Convert a data analytic problem and related information into proper mathematical representation and select appropriate methodologies for analysis based on attributes of the available data sets.
Implement security measures and ethical practices for collection and storage of data.
Transfer (and transform) data from different platforms into usable contexts.
Communicate and summarize results of data analysis in written, oral and visual form.
Select the appropriate methods and tools for data analysis in specific organizational contexts.
The Master of Science (MS) Degree Requires:
The Master of Science in Data Analytics requires a minimum of 36 semester credit hours that include a choice of a capstone or internship experience. Students who did not earn a Bachelor's degree with some emphasis in statistics may need to complete the leveling course STAT 5301 Foundations of Data Analytics with SAS Program. Likewise, students who did not earn a Bachelor's degree with some emphasis in computer science may need to complete the leveling course CS 1410 CSI - Introduction to Computer Science with C++.
|STAT 5301 ||Statistical Foundations Data Analytics|
|CS 5301||Programming Foundation of Data Analytics|
|STAT 5307||Time Series Analysis|
|STAT 5310||Applied Regression Analysis|
|STAT 5311||Multivariate Analysis and Nonparametic Statistics|
|ENG 5340||Project Management|
|CS 5310||Data Mining|
|CS 5318||Database Management Systems|
|CS 6302||Predictive Analytics|
|one approved graduate level capstone or internship course or 1 approved graduate level directed study.|||
*MBA Graduate tuition will apply.
|CJ 6321||Quantitative Analysis and Criminal Justice|
|CS 6301||Information Visualization|
|CS 6303||Big Data Analytics|
|MATH 5302||Mathematical Statistics|
|*MBA 6211||Managerial Decision Making|
|*MBA 6325||Decision Modeling|
|*MBA 6362||Financial Forecasting|
|ENG 5330||Visual Design Theory|
|STAT 5309||Design and Analysis of Experiments|
Graduation is dependent upon satisfactory completion of all course work with a minimum graduate grade point average of 3.0 and completion of a capstone course, internship course or approved directed study. Satisfactory completion of graduate courses includes completing all course work with a grade of "B" or better.
Minimum Grade Point Average to remain in the MSDA program
No more than two grades below a "B" in any graduate level courses will be accepted toward the MS in Data Analytics degree. At most two grades of "C" will be allowed and then only if the cumulative GPA is 3.0 or higher. Upon receipt of the third grade below a "B" in any three required graduate courses, or if the GPA drops below 3.0, the student will be academically dismissed from the program once all appeals processes are exhausted.
Transfer of Graduate Credits
A maximum of two approved courses may be transferred from another accredited university. A minimum grade of "B" is required for the course to transfer. All petitions for transfer credit must be submitted to the UHD Graduate Admissions Committee at the time of application for the committee's approval.
Admission requirements for the MS in data analytics are designed to identify applicants who have the ability, interest, and qualities necessary to complete the program. Admission is competitive and selective. Applicants must demonstrate that they possess the abilities, interests, and qualities necessary to successfully complete the program.
Fall 2017 applications will be available February 2017
accepted until July 15, 2017
Applicants seeking admission will provide the following application materials for review by the Graduate Curriculum Committee:
- A completed Apply Texas application form which will include a 1000-word essay that addresses why you want to study data analytics
- Bachelor degree conferred by a regionally accredited institution.
- Official University transcript(s) from which the applicant earned Bachelor's degree and any advanced degree (if applicable). Official transcripts must reflect
- the last 60 semester credit hours of course work and evidence of Bachelor's degree awarded and
- the GPA. As admission to the degree program is competitive, candidates with a cumulative GPA of 3.0 or higher will be preferred.
- Graduate Record Exam (GRE) scores for applicants with a GPA below 3.0 in their last 60 hours of college coursework.
- Resume documenting any work experience that emphasizes personal and professional accomplishments.
- Two letters of recommendation with accompanying recommendation forms from individuals well-acquainted with your work and who are able to address your academic potential, for example, work supervisors and professors. Fill out theMSDA Application Reference Form.
- Test of English as a Foreign Language (TOEFL) score, if you are a graduate of a university where English is not the primary language of instruction (preferred TOEFL scores are: an internet-based score of 81, a paper-based score of 553 or an IELTS score of 6.5 or higher.
During the MS Data Analytics information session, you will have the chance to visit with faculty and administrators from the program, learn about financial aid options, discuss schedules and curriculum, and turn in documents for on-the-spot processing.
Welcome Center Tour Room GSB302
The Graduate Advisory Committee will evaluate applications using a holistic matrix based on GPA, GRE score, relevant course work or experience, recommendations, and other written materials in the applicant's file. The Graduate Advisory Committee will use the results of this evaluation to determine if an applicant is admitted. The Graduate Coordinator will notify students, in writing, of the committee's decision.
Questions about the Graduation Admissions
For any other information about our the new MS in Data Analytics, contact Dr. Ermelinda Delavina at email@example.com
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