Data Analytics Courses
Understanding Customers (SSD 120)
In this course students learn how to use and apply statistical machine learning techniques to understand population demographics and behaviors. Students develop a model for recommending products to specific customers. The student uses the Weka open source machine-learning package to investigate consumer behavior patterns, make relevant recommendations for complementary products and services, and create reports to senior management on data mining activities and lessons learned. This course is open only to students in the Data Analytics certificate program. Prerequisites: An analytical mindset, strong grounding in mathematics ideally including basic statistics, and basic computer user skills (no previous programming experience is required). Duration: 8 weeks. Pass/Fail only. 3 credits.
Customer Preferences & Strategies (SSD 121)
In this course, students apply statistical machine-learning techniques to analyze potential new products and make recommendations about offerings. Students build models to predict brand preferences based on customer characteristics using the Weka machine-learning package and/or the R statistical programming language. This course is open only to students in the Data Analytics certificate program. Prerequisite: SSD120. Duration: 8 weeks. Pass/Fail only. 3 credits.
Big Data: Web Mining (SSD 122)
Students learn how to mine and analyze extremely large data sets to provide insight to real-world business problems. Students conduct sentiment analysis utilizing cloud-based computing, machine learning tools, and the Common Crawl of the World Wide Web. Results are interpreted to make and communicate predictions of vital interest to stakeholders. This course is open only to students in the Data Analytics certificate program. Prerequisite: SSD121. Duration: 16 weeks. Pass/Fail only. 6 credits.
Deep Analytics and Visualization (SSD 123)
In this course students learn how to use the R statistical programming language and a variety of add-on packages to visualize data relationships and to implement classification and regression models for emerging applications and understanding behaviors in the “Internet of Things.” Students “work” for an “Internet of Things” analytics firm using Data Analytics to solve difficult problems in the physical world such as modeling patterns of residential energy usage and determining a person’s physical position in a multi-building indoor space with wifi fingerprinting. Students are required to present results to the company’s management, explaining strengths and weaknesses of the approaches, and making suggestions for further improvement. This course is open only to students in the Data Analytics certificate program. Prerequisite: SSD121. Duration: 12 weeks. Pass/Fail only. 4.5 credits.
Capstone Data Project (SSD 124)
In this capstone course, students explore a range of advanced statistical machine-learning topics and techniques to solve student-selected problems that are directly relevant to their interests and passions. This course is open only to students in the Data Analytics certificate program. Prerequisite: SSD123. Duration: 8 weeks. Pass/Fail only. 3 credits.
View courses and full requirements for this program in the current course catalog.