SAS® and KSU Data Mining Certificate Program

Rapid development in data collection and storage technology has enabled us to accumulate vast amounts of data. Extracting useful information from the emerging big data that typically has non-obvious relationships, includes non-relational data types has becomes more and more important for decision making in all kinds of business. In order to better prepare our students to meet the growing market demand, KSU has worked with SAS® – a leading provider of business analytics and data mining software and service – to develop this program. The certificate will document students' course work utilizing SAS®'s award-winning data mining technology, and will give students a competitive advantage in the marketplace.

Eligibility

The program welcomes all interested full-time or part-time KSU graduate students with a technical background (such as MSAS, Mathematics, Information Systems, Computer Science, etc...).

Requirements

In order to get the certificate, students must:

  • Complete 12 credit hours of courses listed in the Curriculum, including two core courses and at least two elective courses.
  • Receive a grade of “B” or higher, or “Pass”, in each of these courses.

Curriculum

Core Courses

  • Data Mining is an information extraction activity whose goal is to discover hidden facts contained in databases and perform prediction and forecasting through interaction with the data. The process includes data selection, cleaning and coding, using statistical pattern recognition and machine learning techniques, and reporting and visualizing the generated structures. The course will cover all these issues and will illustrate the whole process by examples of practical applications. Students will use SAS Enterprise Miner in this course.
  • Students will participate in the annual Data Mining Shootout, which gives student teams the opportunity to solve a real-world data-mining problem. Teams will be given a hypothetical, but common business problem to solve. Data will be provided and the solution will require the selection and use of appropriate data mining methods using SAS software. Students will present their results in written and oral forms. This course will be pass/fail basis.

Elective Courses

  • This course will cover advanced programming techniques using the SAS® system for data management and statistical analysis. The topics covered include macro programming, using SQL with SAS® and optimizing SAS® programs. Upon completion of this course students will be prepared to take and pass the certification test and obtain the Advanced Programmer for SAS® 9 certification.
  • This course introduces basic statistical modeling ideals. Topics include simple linear regression, inferences, diagnostics and remedies, matrix representations, multiple regression models, generalized linear model, multicollinearity, polynomial models, qualitative predictor variables, model selection and validation, identifying outliers and influential observations, diagnostics for multicollinearity, and logistic regression.
  • Survey course in statistical analysis techniques. Through a combination of textbook and real-world data sets, students will gain hands-on experience in understanding when and how to utilize the primary multivariate methods: Data Reduction techniques, including Principal components Analysis and Common Factor Analysis, ANOVA/MANOVA/MANCOVA, Cluster Analysis, Survival Analysis and Decision Trees.
  • This course is a heavily used concept in Statistical Modeling. Common applications include credit worthiness and the associated development of a “FICO-esque” credit score, fraud detection or the identification of manufacturing units which fail inspection. Students will learn how to use Logistic Regression, Odds, ROC curves, maximization functions to apply binary classification concepts to real-world datasets. This course will heavily use SAS®-software and students are expected to have a strong working knowledge of SAS®.
  • Students will work with a Department faculty member on an analysis approach using real data. The data may be generated from a problem in their workplace or from any other source that illustrates the data mining method being studied. In the first part of the semester, the theory of the method will be studied to obtain a solid foundation in the methodology. Later, data will be analyzed using one or more statistical software packages. Students will prepare a written report that will become part of their Statistical Methods Portfolio. This course will be pass/fail basis.

Suggestions

After the completion of this certificate, students are strongly encouraged to take the credential exam for SAS® Certified Predictive Modeler using SAS® Enterprise Miner. Most of the topics required in the exam are covered in the curriculum.

See the http://support.sas.com/certify/creds/pm.html for more detail about the credential: 

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