The Department of Statistics at Texas A&M University offers an Applied Statistics Certificate, designed to provide graduate students in disciplines other than statistics and professionals in the workforce with basic knowledge in statistics which will provide them with the training to design experiments, properly collect data, and finally to apply the appropriate models and procedures to summarize data, test hypotheses, and build forecasting models. The participants in the certificate program can select from several areas of emphasis including, but not limited to, biostatistics, data mining and statistical computations. The Applied Statistics Certificate accepts enrollment by the local students in disciplines other than statistics while they continue to pursue their current degree program with no additional application process. It is also designed for working professionals who need statistical knowledge but are not seeking a formal degree in statistics or another field.
Steps to receiving a certificate include:
- Be admitted to the university.
- Take at least 12 credit hours from the TAMU catalog listing of statistics graduate courses.
- Have an overall 3.00 GPA for the statistics courses taken towards the certificate as well as at least an overall 3.00 GPA for all courses taken at TAMU.
- Prepare a 5-10 page document in a professional format (example: https://typeset.io/formats/taylor-and-francis/journal-of-applied-statistics/1d6e9b6dbe1acfaa7e851d10dae0c542) describing the analysis of a data set. The document should contain the following:
- Description of the research goal or purposes of the study generating the data set.
- Detailed discussion of the data set and include data set in appendix to the document.
- Describe the advanced statistical modeling (at least three predictors) and methodology used to analyze the data set.
- Detailed analysis of the data set, including tests of hypotheses, confidence intervals, graphs, tables, assumptions, fix of assumptions, etc for all advanced methods.
- Discussion of the analysis with detailed conclusions concerning the degree to which the data supports the research hypotheses.