Helzberg School of Management - Data Science Curriculum

Classes within our 12-credit-hour data science 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.

Both data certificate tracks help you move beyond Excel and standard SQL reporting. Not only will you learn how to examine and mine data, we’ll also cover 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 and Business Analytics Curriculum, 6 credit hours

  • Predictive Models, BIA 6303 (2 credit hours)
  • Text Mining, BIA 6304 (2 credit hours)
  • Preparation and Analysis for Big Data, BIA 6305 (2 credit hours)

Business Intelligence Curriculum, 6 credit hours

  • Web and Social Media Analytics, BIA 6306 (2 credit hours)
  • Dashboard Creation and Implementation, BIA 6307 (2 credit hours)
  • Analytics and Strategy, BIA 6308 (2 credit hours)

For course descriptions, visit rockhurst.edu/catalog.

Software Exposure

You’ll be taught using the common, typically open-source, tools of data science, including:

  • R and RStudio
  • Python with Pandas
  • Tableau
  • MicroStrategy
  • Google Analytics
  • MySQL/Oracle

 

Prerequisites

Rockhurst University’s Helzberg School of Management prefers the following five credit hours of prerequisites are taken prior to Applied Data Mining, BIA 6301, a core course within the curriculum.

  • Linear and Multivariate Models, BIA 6309 (2 credit hours)
  • Databases for Analytics, BIA 6314 (2 credit hours)
  • Introduction to R, BIA 6311 (.5 credit hours)
  • Introduction to Python, BIA 6312 (.5 credit hours)

In some cases, equivalent knowledge of the prerequisite courses may be substituted.

Course Descriptions

BIA 6309. Linear and Multivariate Models (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 is 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 6311. Introduction to R (.5 credit hours)
One-day workshop on the fundamentals of the R programming language. Only taken if student does not have experience and proficiency in this language. Prerequisite- introductory course in programming or computer science.

BIA 6312. Introduction to Python (.5 credit hours)
One-day workshop on the fundamentals of the Python programming language with emphasis on working with data frames (Pandas), arrays (Numpy) and visualization (Matplotlib). Prerequisite- introductory course in programming or computer science.