Helzberg School of Management - Analytics and Insights Curriculum
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Click here for the course catalog page.
Classes within our 12-credit-hour Analytics and Insights certificate programs are taught in a unique workshop format. Each week, you’ll receive new datasets and learn methods to understand and analyze them. You’ll be encouraged to use your own company’s data for each project or the Helzberg School can supply datasets as needed.
Through this hands-on approach, you’ll learn to build predictive models, dynamic dashboards and work with large sets of data.
All three tracks help you move beyond Excel and standard SQL reporting. Not only will you learn how to examine and mine data, you will also learn how to communicate your findings to a non-technical audience.
Common Core Curriculum, 6 credit hours
- Business Intelligence, BIA 6300 (2 credit hours)
- Applied Data Mining, BIA 6301 (2 credit hours)
- Data Visualization, BIA 6302 (2 credit hours)
In addition to these core courses, you’ll choose from one of the tracks below. In total, you’ll earn 12 credit hours within this certificate program.
Data Science Track, 6 credit hours
- Predictive Models, BIA 6303 (2 credit hours)
- Text Mining, BIA 6304 (2 credit hours)
- Big Data Analytics, BIA 6305 (2 credit hours)
Business Intelligence Track, 6 credit hours
- Web and Social Media Analytics, BIA 6306 (2 credit hours)
- Performance Metrics and Dashboards, BIA 6307 (2 credit hours)
- Analytics and Strategy, BIA 6308 (2 credit hours)
Health Analytics Track, 6 credit hours
- Health Systems, HC 6150 (2 credit hours)
- Health Information Technology, HC 6400 (2 credit hours)
- Quality Improvement in Health Care, HC 6350 (2 credit hours)
For course descriptions, visit https://catalog.rockhurst.edu.
Software Exposure
You’ll be taught using the common, typically open-source, tools of data science, including:
- R and RStudio
- Python with Pandas
- Tableau
- Google Analytics
- Google Data Studio
- SQL
- Power BI
- Cloud Technologies (AWS/Azure)
Rockhurst University’s Helzberg School of Management prefers the following 6 credit hours of prerequisites are taken prior to Applied Data Mining, BIA 6301, a core course within the curriculum.
- Statistics and Machine Learning, BIA 6201 (2 credit hours)
- Databases for Analytics, BIA 6314 (2 credit hours)
- Analytics & Computational Programming, BIA 6202S (2 credit hours)
In some cases, equivalent knowledge of the prerequisite courses may be substituted.
Course Descriptions
BIA 6201. Statistics and Machine Learning (2 credit hours)
This intermediate level class covers multiple and logistic regression methods including correlation, residual analysis, analysis of variance, and robustness. These topics will be studied from a data analytic perspective using business examples. The class also explores multivariate models as they relate to problems encountered in data and text mining. Prerequisite: Introductory statistics and knowledge of the R computing Language.
BIA 6314. Databases for Analytics (2 credit hours)
This course that details database design, normalization and query methods that are pertinent for analytics. Topics will include relational databases, SQL, data warehouse architecture, data marts and data lakes. Further investigation will include cloud computing options, APIs and emerging forms of databases. The emphasis is placed on the use of these infrastructures and architectures for analytics. Prerequisite-introductory course in programming or computer science.
BIA 6202S. Analytics & Computational Programming (2 credit hours)
This is an introductory course in programming in the Python and R languages. Fundamentals of program design, data types, control structures, use of external libraries, integrated development environments, and notebook computing environments will be covered. The full programming design cycle of problem analysis, data gathering, coding, debugging, maintenance, and documentation will be included.
For more information about the suite of graduate business programs from Helzberg, please click here: School of Management Programs.
