Jasser Jasser
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About
I am Dr. Jasser (JJ) Jasser, a Professor of Data Analytics dedicated to advancing research and teaching in data science, machine learning, and statistical analysis.
Explore my website to learn more about my research, courses, publications, and how to get in touch.
Courses
Below is a list of courses I have taught or am currently teaching:
DTA 250 - Fundamentals of Data Science and Analytics
Description: Overview of core concepts and applications of data science and analytics. Topics include planning a quantitative study; formulating research questions; data management, preparation, and cleaning; model selection and validation; visualization; popular predictive modeling techniques; and issues of bias and ethics in data analytics. Substantial work with at least one professional data analysis package, such as SAS, Stata, or R.
Books used in this course:
- R for Data Science (2e) by Hadley Wickham and Garrett Grolemund link
- R for Everyone by Jared Lander link
- R from Scratch: An Intro to Data Analytics by Jasser Jasser link
DTA 350 - Advanced Data Visualization and Communication
Description: This advanced course teaches how to transform complex datasets into visually compelling narratives. Students gain expertise in high-quality data visualization tools, cutting-edge innovative design principles, and persuasive storytelling techniques. Through hands-on projects and real-world case studies, participants learn to communicate insights effectively, enabling more informed decision-making and impactful strategic recommendations.
Books used in this course:
- Storytelling with Data - Let’s Practice by Cole Nussbaumer Knaflic link
- Visualize This (2nd ed.) by Nathan Yau link
DTA 360 - Information Systems
Description: This course provides a comprehensive introduction to information systems and their role in modern organizations. Students will explore fundamental concepts of IS, including hardware, software, networking, databases, and cybersecurity. The course emphasizes the strategic importance of IS in business decision-making, operations, and competitive advantage.
Books used in this course:
- Information Systems: A Manager’s Guide to Harnessing Technology by John Gallaugher link
DTA 460 - Machine Learning
Description: This course provides a comprehensive introduction to the field of machine learning, a rapidly evolving area of artificial intelligence. The course covers key concepts such as linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. The course is designed to impart a deep understanding of the statistical aspects of machine learning and how to apply these concepts using the R programming language. Emphasis is placed on practical application, with students expected to work on real-world data sets and projects to reinforce the theoretical knowledge gained. This course is ideal for those who have a basic understanding of statistics and programming and are keen to explore the exciting world of machine learning.