Data Analytics Track

This course provides an introduction to the field of business analytics, which is defined as the extensive use of data, statistical and quantitative analysis, exploratory and predictive models and fact-based management to drive decisions and actions. Topics include implementation of successful analytics platforms, big and little data, predictive analytics, social media analytics, mobile analytics and data visualization. Students use industry standard tools in practical projects.

More and more, organizations are collecting large amounts of data, much of it unstructured. Big Data technologies can be used to store, process and analyze large amounts of data using a distributed environment. This course introduces students to the world of big data and associated technologies. The focus of the course is Apache Hadoop, which is an open-source software project that enables distributed processing of large data sets across clusters of commodity servers. The objective of this course is to provide students a foundation for understanding big data technologies and Hadoop in particular.

Topics include Hadoop system architecture, Hadoop Distributed File System (HDFS), MapReduce programming model and design patterns and technologies surrounding Hadoop ecosystem such as Pig, Hive and Oozie. The course will also introduce big data science concepts and NoSQL database technologies.

This course explores strategic information technology management issues associated with doing business in digital times. It provides a framework to understand how information technology strategy aligns with business strategy and focuses on developing an understanding of the key information requirements for developing information technology strategy and systems architecture. Students are encouraged to think and behave strategically with respect to exploiting leading-edge technologies and deliver the right business value with information technology. The course will focus on digital technology trends transforming how business is done, information management and architecture, e-business models and strategies, mobile commerce, social networking, engagement and social metrics and business process innovation.

This course introduces the principles and procedures related to the design and management of data warehouse (DW) and business intelligence (BI) systems. The DW is the central data repository that is used for decision-support. BI refers to the analytical applications that users can interact with in making sense of the data. The course focuses on the data warehousing process including requirement collection, data warehouse architectures, dimensional modeling, extracting, transforming and loading strategies and creation of data marts. The course also uses data warehousing as a platform for BI applications, such as reporting, dashboards and online analytical processing (OLAP). By completing this course, students should understand the technologies used for decision-support and possess valuable analytical skills.

More and more organizations are collecting large amounts of data, much of it unstructured. Big data technologies can be used to store, process and analyze large amounts of data using a distributed environment. This course introduces students to the world of big data and associated technologies. The focus of the course is Apache Hadoop, which is an open source software project that enables distributed processing of large data sets across clusters of commodity servers. The objective of this course is to provide students a foundation for understanding big data technologies and Hadoop in particular. Topics include Hadoop system architecture, Hadoop Distributed File System (HDFS), MapReduce programming model and design patterns and technologies surrounding Hadoop ecosystem such as Pig, Hive and Oozie. The course will also introduce big data science concepts and NoSQL database technologies.

Internships are a valuable experiential learning tool where students engage in work with an organization on an approved topic. Students will write a comprehensive report based on their learning experience along with weekly logs and a managerial evaluation. The report will be evaluated and graded by the instructor.

Students will work on proposing, developing and implementing a comprehensive project based on concepts learned during their program. A project is a form of research aimed at creating or contributing new knowledge in a discipline or, an applied study that combines a specific topic with actual problems or issues within a setting.

A master’s in data analytics is a more narrow view into the statistics, modeling and machine learning from start to finish.

The data analytics track in Cal Lutheran’s MSIT program gives you that same analytical depth but connects it to the systems and strategies that drive business decisions. You’ll learn to manage the data, the technology and the teams that bring insights to life. It’s part problem-solving, part storytelling, and part technical craft, anchored in the broader world of IT leadership.

Because analysis matters when it leads to action. This track teaches you how to uncover insights and move organizations forward with them. You’ll sharpen your technical fluency in data tools while building the leadership and communication skills to turn numbers into strategy. It’s designed for people who want to analyze the story in the data and help write what happens next.

Most students finish the MSIT with a data analytics specialization in 12 to 24 months. You’ll complete eight core courses, four data analytics electives and an optional capstone project or internship.

Yes. Many students take one or two evening classes per term while working full time. The data analytics track courses are on campus, but they are offered in the evening, so you can build expertise in data analytics without putting your career (or life) on hold.

No. While experience can be a benefit, Cal Lutheran students come from a range of backgrounds. You’ll build a foundation in analytics, R programming, Python and visualization tools as part of the curriculum.

Yes. You’ll work directly with large, real-world datasets using tools like Hadoop and BI dashboards. On top of that, you’ll complete either an internship or an IT project, so you graduate with experience you can talk about and demonstrate in interviews.