The LBJ School Dean's Certificate program, launched during the 2019–20 academic year, gives students an opportunity to earn an additional credential by exploring in-depth policy areas that are increasingly critical in the public affairs arena. The program combines academic knowledge, analytical skills and practical application to help give students a leg up. The inaugural certificate for the Ph.D. program is Data Science and Policy Analysis.
Dean's Certificate in Data Science and Policy Analysis
The recent explosion of "Big Data" has led to a rapidly expanding market for graduate degree holders with data analysis and modeling skills, not only in the private sector, but also in government organizations and nonprofits. Many LBJ students are interested in options that allow them to acquire these new analytical skills at the LBJ School, in a format that will provide them with a clear pathway into these new jobs. The Dean's Certificate in Data Science and Policy Analysis is an exciting new opportunity that assists our graduates in finding great employment opportunities after graduation.
The DSPA Certificate has a unique, innovative structure designed to certify acquisition of a particular set of data science skills and demonstrate to prospective employers that the certificate recipient can apply those skills in defining, organizing and executing an original data science project. It does not require completion of a particular fixed selection of courses. Selection of the three approved courses in which those skills can be acquired is flexible by design, and can utilize both qualifying sections of advanced methods classes or qualifying sections of policy-oriented classes with an empirical analysis project component. Recipients of the LBJ DSPA certificate must also complete an original, individual data science project demonstrating proficiency in all required skills.
A total of three approved courses must be completed which, collectively, demonstrate competence in the following five skills:
- Use of a modern data science software platform for empirical data analysis
- Demonstrated knowledge of current methods of acquiring, merging and cleaning multiple sources of unprocessed digital data; knowledge and use of principled methods to deal with missing, miscoded and outlier data
- Ability to create compelling and effective summaries and visualizations of large-scale data sets;
- Demonstrated proficiency in application of statistical models or machine learning methods to the analysis of empirical data sets using software tools
- Successful completion of an original, individual policy analysis project using statistical and data science tools applied to a real-world empirical dataset, demonstrating proficiency in all the above skills
For Ph.D. students:
- The data science project must be either a chapter in a Ph.D. dissertation, or a paper submitted for publication in a scholarly journal.
- The Ph.D. student's thesis advisor must also approve the DSPA certificate application.
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