In this course, learners develop the skills needed to apply the early aspects of the life cycle of analytics. Learners review the different types of data sources and explore various data models and algorithms. Learners also use basic tools to complete an analysis and collaborate within teams to evaluate case studies and explore ways in which stakeholders’ needs are met through data intelligence. Must be taken during the first quarter by learners who have been admitted to the MS in Analytics degree program. Cannot be fulfilled by transfer or credit for prior learning.

Learners in this course apply data management fundamentals to data models. Learners examine the concepts of data mining, ETLs, and data warehouses and also evaluate applied analytics in professional domains such as finance, marketing, and health care.  Prerequisite(s): Completion of or concurrent registration in ANLT5002 or HMSV5002 or PM5018 and ITEC5020​.

In this course, learners explain database methodologies including relational databases, flat files, dimensional modeling, RSS feeds, and multi-dimensional modeling. Learners examine the impact of data quality on analytics and apply ETL techniques and processes. Finally, learners evaluate the application of data warehouses, data marts, and multi-dimensional cubes to decision-making and action. Prerequisite(s): Completion of or concurrent registration in ANLT5010.

In this course, learners study the collection, organization, presentation, analysis, and interpretation of data using statistical methods. Learners practice using appropriate tools to obtain a result using statistical methods and collaborate with team members to compare processes, techniques, and conclusions to understand various perspectives. Prerequisite(s): Completion of or concurrent registration in ANLT5020.

Learners in this course develop and demonstrate their skill in the role of leadership in analytics and explore change management theories and models as they relate to the field of analytics. Learners examine the ethical issues and practices of the analytics field to gain an understanding of how personal ethical frameworks shape the decision-making process. Learners also evaluate project management skills needed for successful analytic projects.

In this course, learners develop their skills in creating a predictive model. Learners apply data mining algorithms, models, and data mining modeling techniques to test, fit, and implement an algorithm and/or model with appropriate tools. Learners practice interpreting results to find an application for those results. Finally, learners apply control, feedback, and evaluation approaches to enhance, continue, or retire the algorithm or model using big data. Prerequisite(s): ANLT5030. Graduate certificate learners in Advanced Analytics Using SAS® are exempt from this prerequisite.

In this course, learners evaluate forecast model outcomes to solve organizational problems. Learners examine the impact of time and data latency on forecasting, and practice identifying patterns in the output of forecast models. Learners also apply forecasting techniques in their communication with stakeholders.  Prerequisite(s): ANLT5030.

Learners in this course gain an understanding of the early stages of text mining. Learners examine document management practices, text-scraping techniques, and various methods for modeling their findings as they solve text-based mining problems. Prerequisite(s): ANLT5030. Graduate certificate learners in Advanced Analytics Using SAS® are exempt from this prerequisite.

Learners in this course demonstrate advanced practice in applying the analytic life cycle. Learners examine approaches to visual analytics and are introduced to geospatial data techniques. Learners also apply their analytic skills to current organizational problems and apply analytic solution scoring and project management skills for effective team performance. Prerequisite(s): ANLT5050.

In this course, learners examine reporting solutions that use analytics. Learners analyze, select, and apply reporting solutions to fit an organizational need and evaluate different reporting frameworks. Prerequisite(s): ANLT5030.

In this course, learners articulate the value of visualization to telling the analytic story to stakeholders. Learners explore the appropriate presentation of types of data and apply best practices for the design of effective visualizations. Learners also develop skills for presenting data to stakeholders in a succinct and relevant manner. Prerequisite(s): ANLT5030. 

This is an integrative course for learners in the MS in Analytics degree program. Learners synthesize and integrate the knowledge, competencies, and skills acquired throughout the program by developing and implementing a final project that demonstrates practical application of program content. For MS in Analytics learners only. Must be taken during the learner’s final quarter. Prerequisite(s): Completion of all required coursework. Cannot be fulfilled by transfer or credit for prior learning.