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Master of Science in Information Technology IT Analytics
IT Analytics coursework overview
Coursework tailored to your learning preferences
- Capella sets the deadlines
- Weekly online interactions
- Learn on your time
- Online
Planning for your degree
- total quarter credits: 48
- Quarter length: 10 weeks
- Course length: 10 weeks
- Break: 3 weeks between quarters
Course requirements
- Core 5 courses
- Elective 5 courses
- Capstone 2 courses
Example program course sequence
This example course sequence takes course prerequisites into account, so it can be helpful as a general guide when you register for courses. The number of courses you take in a quarter and the time it takes for you to complete your program can vary. Your academic coach can help answer any course registration questions.
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ANLT5002
Required Course Basic Applications of Analytics
4 quarter credits
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 stakeholder's 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.
4 quarter credits
ANLT5010
Required Course Foundations in Analytics
4 quarter credits
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 ITEC5020.
4 quarter credits
ANLT5020
Required Course Data Sources for Analytics
4 quarter credits
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 or HMSV5500.
4 quarter credits
ANLT5030
Required Course Statistical Methods in Analytics
4 quarter credits
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 or HMSV5510.
4 quarter credits
ANLT5040
Required Course Leadership for Analytics
4 quarter credits
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.
4 quarter credits
ANLT5050
Required Course Concepts of Data Mining
4 quarter credits
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.
4 quarter credits
ANLT5060
Required Course Applied Forecasting
4 quarter credits
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.
4 quarter credits
ANLT5070
Required Course Text Mining
4 quarter credits
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.
4 quarter credits
ANLT5080
Required Course Advanced Analytics and Modeling
4 quarter credits
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.
4 quarter credits
ANLT5090
Required Course Reporting Solutions with Analytics
4 quarter credits
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.
4 quarter credits
ANLT5100
Required Course Visual Analytics
4 quarter credits
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.
4 quarter credits
ANLT5900
Capstone Capstone in Analytics
4 quarter credits
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.
4 quarter credits
What can I expect in the courseroom?
Each unit consists of readings, discussions and other activities you will be expected to complete throughout the week. Most deadlines for coursework occur on Thursdays and/or Sundays but may vary by program and course. In each course, you will receive a detailed scoring guide that describes expectations for every graded assignment.
How will I be graded?
Grades are based on your participation in weekly reading discussions and completion of assignments, based on criteria outlined in the rubric. You will also be assessed on your ability to demonstrate an understanding of expected outcomes for your program or specialization. These outcomes are based on the needs and performance standards of your field or discipline.
Total
At least 48 quarter credits
One or more courses in this program may require a prerequisite(s). Refer to the course descriptions for details.
Learners who do not complete all program requirements within quarter credit/program point minimums will be required to accrue such additional quarter credits/program points as are associated with any additional or repeat coursework necessary for successful completion of program requirements.
Multiple specializations available (must be within the same degree program)
Capstone courses
In your final quarter, complete a capstone course for a total of 8 quarter credits.
Show all descriptions
ANLT5900
Capstone Capstone in Analytics
4 quarter credits
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.
4 quarter credits
Total
At least 48 quarter credits
One or more courses in this program may require a prerequisite(s). Refer to the course descriptions for details.
Learners who do not complete all program requirements within quarter credit/program point minimums will be required to accrue such additional quarter credits/program points as are associated with any additional or repeat coursework necessary for successful completion of program requirements.
Multiple specializations available (must be within the same degree program)
Core course requirements
Complete at least 20 quarter credits in the core courses category. Choose from the courses shown below.
Show all descriptions
ANLT5002
Required Course Basic Applications of Analytics
4 quarter credits
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 stakeholder's 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.
4 quarter credits
ANLT5020
Required Course Data Sources for Analytics
4 quarter credits
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 or HMSV5500.
4 quarter credits
ANLT5040
Required Course Leadership for Analytics
4 quarter credits
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.
4 quarter credits
ANLT5060
Required Course Applied Forecasting
4 quarter credits
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.
4 quarter credits
ANLT5080
Required Course Advanced Analytics and Modeling
4 quarter credits
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.
4 quarter credits
ANLT5100
Required Course Visual Analytics
4 quarter credits
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.
4 quarter credits
ANLT5010
Required Course Foundations in Analytics
4 quarter credits
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 ITEC5020.
4 quarter credits
ANLT5030
Required Course Statistical Methods in Analytics
4 quarter credits
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 or HMSV5510.
4 quarter credits
ANLT5050
Required Course Concepts of Data Mining
4 quarter credits
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.
4 quarter credits
ANLT5070
Required Course Text Mining
4 quarter credits
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.
4 quarter credits
ANLT5090
Required Course Reporting Solutions with Analytics
4 quarter credits
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.
4 quarter credits
Total
At least 48 quarter credits
One or more courses in this program may require a prerequisite(s). Refer to the course descriptions for details.
Learners who do not complete all program requirements within quarter credit/program point minimums will be required to accrue such additional quarter credits/program points as are associated with any additional or repeat coursework necessary for successful completion of program requirements.
Multiple specializations available (must be within the same degree program)