MBA - DATA SCIENCE COURSEWORK
Concentration must include the MBA Core plus a minimum of 12 credits in analytics. Completion of the MBA with Data Analytics Concentration requires the completion of at least 36 credits.
DATA SCIENCE CONCENTRATION:
(students with previous experience or demonstrated mastery in the fundamentals courses may apply for a waiver with the program director.)
A graduate level introduction to data science through a focus on the language R. Support
tools and libraries such as Rstudio and the tidyverse will be emphasized. Students
will complete the data science boot camp (a weekend in person intensive or online
equivalent) at the start of this online course. 4 Credits
DS-500 Data Science Fundamentals
A graduate level introduction to data science through a focus on the language R. Support tools and libraries such as Rstudio and the tidyverse will be emphasized. Students will complete the data science boot camp (a weekend in person intensive or online equivalent) at the start of this online course.
DS-516 Mathematics Fundamentals
Selected topics of discrete mathematics and linear algebra related to data science analysis techniques and algorithms.
DS-510 Computer Science Fundamentals
A graduate-level introduction to Computer Science Fundamentals through a focus on the Python language. Students will complete the data science boot camp (a weekend in-person intensive or online equivalent) at the start of this online course.
DS-520 Statistics Fundamentals
Overview of basic statistical techniques including descriptive statistics, hypothesis testing, and regression.
DS-525 Data Acquisition & Visualization
A graduate-level introduction to retrieving, cleaning, and visualizing data from widely varied sources and formats. The student will use common data science languages and tools for extraction, transformation, loading and visualizing data sets. Project presentations will have an emphasis on communication skills. Tableau visualization tools and Python libraries are used.
DS-530 Multivariate Techniques
Multivariate statistical techniques including multivariate regression, logistic regression, and dimension reduction techniques. Students will get hands-on experience applying the topics covered to real datasets using R, a powerful and popular open-source statistical computing language.
3.00 CreditsPrereqs: DS-516 and DS-520.
DS-552 Data Mining
This course considers the use of machine learning (ML) and data mining (DM) algorithms for the data scientist to discover information embedded in wide-ranging datasets, from the simple tables to complex data sets and big data situations. Topics include ML and DM techniques such as classification, clustering, predictive and statistical modeling using tools such as R, Python, Matlab, Weka and others.
3.00 CreditsPrerequisite: DS-500, DS-510, or by permission
DS-570 Database Systems
This course focuses on database design and relational structures, data warehousing and access through SQL. Students will use SQL to create and pull data from database systems. NoSQL and data warehousing are also covered to give students the necessary background in database systems.
3 Credits Pre-Req: DS-510
DS-575 Big Data Techniques
This course considers the management and processing of large data sets, structured, semi-structured, and unstructured. The course focuses on modern, big data platforms such as Hadoop and NoSQL frameworks. Students will gain experience using a variety of programming tools and paradigms for manipulating big data sets on local servers and cloud platforms.
3 Credits Prerequisite: DS-500 or DS-510