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Master of Science in Analytics Analytics courses

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Analytics coursework overview

Coursework tailored to your learning preferences

  • Capella sets the deadlines
  • Weekly online interactions
  • Learn on your time
  • Online
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Planning for your degree

  • total quarter credits: 48
  • Quarter length: 10 weeks
  • Course length: 10 weeks
  • Break: 3 weeks between quarters
Example sequence Core Capstone

Course requirements

  • Core 11 courses
  • Capstone 1 course
Show all descriptions
ANLT5002 Required Course Basic Applications of Analytics 4 quarter credits In this course, students develop the skills needed to apply the early aspects of the life cycle of analytics. Students review the different types of data sources and explore various data models and algorithms. Students also use basic tools to complete an analysis and collaborate within teams to evaluate case studies and explore ways in which stakeholder's needs are met through data intelligence. Must be taken during the first quarter by students who have been admitted to the MS in Analytics degree program. Cannot be fulfilled by transfer or credit for prior learning. . 4 quarter credits
ANLT5010 Required Course Foundations in Analytics 4 quarter credits Students in this course apply data management fundamentals to data models. Students 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 ITEC5020. 4 quarter credits
ANLT5020 Required Course Data Sources for Analytics 4 quarter credits In this course, students explain database methodologies including relational databases, flat files, dimensional modeling, RSS feeds and multi-dimensional modeling. Students examine the impact of data quality on analytics and apply ETL techniques and processes. Finally, students 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 or HMSV5500. 4 quarter credits
ANLT5030 Required Course Statistical Methods in Analytics 4 quarter credits Students analyze the collection, organization, presentation, analysis, and interpretation of data using statistical methods. Students 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 or HMSV5510. 4 quarter credits
ANLT5045 Required Course Applied AI and Data Analytics for Business Leaders 4 quarter credits Students in this course develop and demonstrate their skill in the role of analytics and the applications of artificial intelligence as they relate to the field of analytics. Students examine modern analytics practices, ranging from descriptive statistics and data visualization to predictive modeling, text mining, optimization, and the latest trends in generative AI. Emphasis is placed on how AI and analytics are applied in real business contexts such as marketing, customer engagement, logistics, human resources, and financial services. 4 quarter credits
ANLT5050 Required Course Concepts of Data Mining 4 quarter credits In this course, students develop their skills in creating a predictive model. Students apply data mining algorithms, models, and data mining modeling techniques to test, fit, and implement an algorithm and/or model with appropriate tools. Students practice interpreting results to find an application for those results. Finally, students apply control, feedback, and evaluation approaches to enhance, continue, or retire the algorithm or model using big data. Prerequisite(s): ANLT5030. Graduate certificate students in Advanced Analytics Using SAS® are exempt from this prerequisite. 4 quarter credits
ANLT5060 Required Course Applied Forecasting 4 quarter credits Students evaluate forecast model outcomes to solve organizational problems. They examine the impact of time and data latency on forecasting and practice identifying patterns in the output of forecast models. Students also apply forecasting techniques to effectively communicate insights and recommendations to stakeholders. Prerequisite(s): ANLT5030 4 quarter credits
ANLT5070 Required Course Text Mining 4 quarter credits Students in this course gain an understanding of the early stages of text mining. Students 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 students in Advanced Analytics Using SAS® are exempt from this prerequisite. 4 quarter credits
ANLT5080 Required Course Advanced Analytics and Modeling 4 quarter credits Students demonstrate advanced practice in applying the analytic life cycle to real-world organizational problems. Students use modern analytics tools to explore data, prepare and transform datasets, build and evaluate predictive models, and score analytic solutions to support decision-making. Emphasis is placed on applied modeling, interpretation of results, ethical use of analytics, effective communication of insights to stakeholders, and project management skills. Prerequisite(s): ANLT5050. 4 quarter credits
ANLT5090 Required Course Reporting Solutions with Analytics 4 quarter credits In this course, students examine reporting solutions that use analytics. Students analyze, select, and apply reporting solutions to fit an organizational need and evaluate different reporting frameworks. Prerequisite(s): ANLT5030. 4 quarter credits
ANLT5100 Required Course Visual Analytics 4 quarter credits Students demonstrate the value of analytic storytelling to stakeholders using visualization. Students investigate the appropriate presentation of types of data and apply best practices for the design of effective visualizations. Students also develop skills for presenting data to stakeholders in a succinct and relevant manner. Throughout the course, students utilize AI as a support tool for coding, troubleshooting, and understanding outputs. Students remain responsible for verifying results and ensuring their analysis is accurate. Prerequisite(s): ANLT5030. 4 quarter credits
ANLT5900 Capstone Capstone in Analytics 4 quarter credits This is an integrative course for students in the MS in Analytics degree program. Students 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 students only. Must be taken during the student's final quarter. Cannot be fulfilled by transfer or credit for prior learning. Prerequisite(s): Completion of all required coursework. 4 quarter credits
Show all descriptions
ANLT5002 Required Course Basic Applications of Analytics 4 quarter credits In this course, students develop the skills needed to apply the early aspects of the life cycle of analytics. Students review the different types of data sources and explore various data models and algorithms. Students also use basic tools to complete an analysis and collaborate within teams to evaluate case studies and explore ways in which stakeholder's needs are met through data intelligence. Must be taken during the first quarter by students who have been admitted to the MS in Analytics degree program. Cannot be fulfilled by transfer or credit for prior learning. . 4 quarter credits
ANLT5020 Required Course Data Sources for Analytics 4 quarter credits In this course, students explain database methodologies including relational databases, flat files, dimensional modeling, RSS feeds and multi-dimensional modeling. Students examine the impact of data quality on analytics and apply ETL techniques and processes. Finally, students 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 or HMSV5500. 4 quarter credits
ANLT5045 Required Course Applied AI and Data Analytics for Business Leaders 4 quarter credits Students in this course develop and demonstrate their skill in the role of analytics and the applications of artificial intelligence as they relate to the field of analytics. Students examine modern analytics practices, ranging from descriptive statistics and data visualization to predictive modeling, text mining, optimization, and the latest trends in generative AI. Emphasis is placed on how AI and analytics are applied in real business contexts such as marketing, customer engagement, logistics, human resources, and financial services. 4 quarter credits
ANLT5060 Required Course Applied Forecasting 4 quarter credits Students evaluate forecast model outcomes to solve organizational problems. They examine the impact of time and data latency on forecasting and practice identifying patterns in the output of forecast models. Students also apply forecasting techniques to effectively communicate insights and recommendations to stakeholders. Prerequisite(s): ANLT5030 4 quarter credits
ANLT5080 Required Course Advanced Analytics and Modeling 4 quarter credits Students demonstrate advanced practice in applying the analytic life cycle to real-world organizational problems. Students use modern analytics tools to explore data, prepare and transform datasets, build and evaluate predictive models, and score analytic solutions to support decision-making. Emphasis is placed on applied modeling, interpretation of results, ethical use of analytics, effective communication of insights to stakeholders, and project management skills. Prerequisite(s): ANLT5050. 4 quarter credits
ANLT5100 Required Course Visual Analytics 4 quarter credits Students demonstrate the value of analytic storytelling to stakeholders using visualization. Students investigate the appropriate presentation of types of data and apply best practices for the design of effective visualizations. Students also develop skills for presenting data to stakeholders in a succinct and relevant manner. Throughout the course, students utilize AI as a support tool for coding, troubleshooting, and understanding outputs. Students remain responsible for verifying results and ensuring their analysis is accurate. Prerequisite(s): ANLT5030. 4 quarter credits
ANLT5010 Required Course Foundations in Analytics 4 quarter credits Students in this course apply data management fundamentals to data models. Students 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 ITEC5020. 4 quarter credits
ANLT5030 Required Course Statistical Methods in Analytics 4 quarter credits Students analyze the collection, organization, presentation, analysis, and interpretation of data using statistical methods. Students 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 or HMSV5510. 4 quarter credits
ANLT5050 Required Course Concepts of Data Mining 4 quarter credits In this course, students develop their skills in creating a predictive model. Students apply data mining algorithms, models, and data mining modeling techniques to test, fit, and implement an algorithm and/or model with appropriate tools. Students practice interpreting results to find an application for those results. Finally, students apply control, feedback, and evaluation approaches to enhance, continue, or retire the algorithm or model using big data. Prerequisite(s): ANLT5030. Graduate certificate students in Advanced Analytics Using SAS® are exempt from this prerequisite. 4 quarter credits
ANLT5070 Required Course Text Mining 4 quarter credits Students in this course gain an understanding of the early stages of text mining. Students 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 students in Advanced Analytics Using SAS® are exempt from this prerequisite. 4 quarter credits
ANLT5090 Required Course Reporting Solutions with Analytics 4 quarter credits In this course, students examine reporting solutions that use analytics. Students analyze, select, and apply reporting solutions to fit an organizational need and evaluate different reporting frameworks. Prerequisite(s): ANLT5030. 4 quarter credits
Show all descriptions
ANLT5900 Capstone Capstone in Analytics 4 quarter credits This is an integrative course for students in the MS in Analytics degree program. Students 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 students only. Must be taken during the student's final quarter. Cannot be fulfilled by transfer or credit for prior learning. Prerequisite(s): Completion of all required coursework. 4 quarter credits

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