Register for a Data Analytics Graduate Course

Introduces the key concepts of data analytics and data science as applied to solving data-centered business problems in many industries. Emphasizes principles and methods covering the process from envisioning the problem to applying data science techniques to deploying the results to improve a business and help make decisions. Topics include an introduction to data-analytic thinking; application of data science solutions to business problems; achieving and sustaining competitive advantage with data science. Students will read and analyze data analytics case studies in various industries.

This course will meet on campus on Tuesdays, 4–6:30 p.m.; Saturday, September 7, 9 a.m.–5 p.m. It runs from August 27 through October 13.

An intermediate statistics course focusing on techniques used in data analytics. Introduces key statistical methods for applying data analytics. Introduces statistical thinking – starting with a question and using data and software tools to form a reasonable conclusion. Covers statistical analysis of both categorical and quantitative data. Most analysis will be performed using SAS software. Topics include statistical distributions, probability density functions, model accuracy analysis, bootstrapping, and sampling techniques.

This course will meet on campus on Tuesdays,  7–9:30 p.m.; Saturday, September 14, 9 a.m.–5 p.m. It runs from August 27 through October 13.

Practical survey course covering database and data warehouse fundamentals. Emphasizes SQL (simple and complex queries), the Extract-Transformation-Load (ETL) process, relational versus non-relational databases (and why relational databases can be a problem for analysis), an exploration of different database systems (Oracle, Microsoft SQL Server, etc.), data warehousing concepts, normalization/de-normalization, and cloud data warehousing. Course provides practical skills for database querying and provides a foundational knowledge of database concepts so that students can work better with the database administration staff.

This course will meet on campus on Tuesdays,  4–6:30 p.m.; Saturday, October 26, 9 a.m.–5 p.m. It runs from October 21 through December 15.

A hands-on data analytics course for structured data using the Python programming language. Covers the skills that are required to explore and prepare data prior to analysis, create several types of predictive models, and perform data clustering. It also covers skills that are required for model assessment and implementation. Models covered include decision trees, regressions, neural networks, K-means, market basket analysis, and others. Upon completion, students will have a set of practical data analytics skills and know how to apply these skills in a variety of business environments and with many types of structured data. 

This course will meet on campus on Tuesdays,  7–9:30 p.m.; Saturday, November 2, 9 a.m.–5 p.m. It runs from October 21 through December 15.

Introduces the key concepts of data analytics and data science as applied to solving data-centered business problems in many industries. Emphasizes principles and methods covering the process from envisioning the problem to applying data science techniques to deploying the results to improve a business and help make decisions. Topics include an introduction to data-analytic thinking; application of data science solutions to business problems; achieving and sustaining competitive advantage with data science. Students will read and analyze data analytics case studies in various industries.

This course will meet online. It runs from August 27 through October 13. 

An intermediate statistics course focusing on techniques used in data analytics. Introduces key statistical methods for applying data analytics. Introduces statistical thinking – starting with a question and using data and software tools to form a reasonable conclusion. Covers statistical analysis of both categorical and quantitative data. Most analysis will be performed using SAS software. Topics include statistical distributions, probability density functions, model accuracy analysis, bootstrapping, and sampling techniques.

This course will meet online. It runs from August 27 through October 13. 

Practical survey course covering database and data warehouse fundamentals. Emphasizes SQL (simple and complex queries), the Extract-Transformation-Load (ETL) process, relational versus non-relational databases (and why relational databases can be a problem for analysis), an exploration of different database systems (Oracle, Microsoft SQL Server, etc.), data warehousing concepts, normalization/de-normalization, and cloud data warehousing. Course provides practical skills for database querying and provides a foundational knowledge of database concepts so that students can work better with the database administration staff.

This course will meet online. It runs from October 21 through December 15. 

A hands-on data analytics course for structured data using the Python programming language. Covers the skills that are required to explore and prepare data prior to analysis, create several types of predictive models, and perform data clustering. It also covers skills that are required for model assessment and implementation. Models covered include decision trees, regressions, neural networks, K-means, market basket analysis, and others. Upon completion, students will have a set of practical data analytics skills and know how to apply these skills in a variety of business environments and with many types of structured data. 

This course will meet online. It runs from October 21 through December 15.