As artificial intelligence continues to advance at a rapid pace, computing courses at the University of Nebraska–Lincoln are similarly evolving to prepare students for an AI-driven future. Courses previously centered on foundational topics are being updated and expanded to combine classic computing principles with cutting-edge technologies.
CSCE 320, currently titled Data Analysis, has been offered in the School of Computing for several years, but it was recently redesigned to accommodate new technological advancements and shift focus onto modern methods. To align with the new direction, it will soon be renamed Data Analysis with Machine Learning. Students in the course, taught by Professor Ashok Samal, learn how to analyze data using current algorithms, machine learning tools, and hands-on approaches.
“Two years ago, we completely changed the content to go from a traditional database focus to a machine learning focus,” Samal said. “We have courses where they learn the basic underlying foundations of machine learning, but this is more about practice. We focus on how to use machine learning techniques, what they are, and best practices.”
Samal said unlike many other computing courses, a high level of coding experience is not required for CSCE 320, since the curriculum focuses on leveraging tools rather than creating them.
“We have a lot of programming assignments, but they’re about how to use the existing APIs rather than starting from scratch,” Samal said. “They’ll still have some coding—not trying to code the machine learning tasks but learning how to use the APIs available already in all kinds of packages to solve the tasks.”
While the course is required for data science majors, the emphasis on application rather than development makes it accessible to students of all majors and offers particular value to non-computing majors in adjacent disciplines such as mathematics and engineering. According to results of the first assignment, which surveyed the majors of students enrolled in the course, about a third of the students in last semester’s class were non-computing majors.
“Machine learning is AI, and it has become ubiquitous in our daily life, so it's important not only for data science or computer science majors, but across all disciplines,” Samal said. “I think there is interest across different disciplines because they all have large data sets, and they are all looking for techniques to understand how to use them and perform machine learning tasks.”
Students in CSCE 320 can expect to explore a variety of topics through their assignments and learn how different techniques can be applied to a wide array of fields. Additional data set examples have included medical images of tumors, species of penguins, length of sleep cycles, IMDB movie details, and Bob Ross paintings.
The varied subject matter is reflective of the School of Computing’s data science major, which is uniquely structured to be an interdisciplinary major. Students are encouraged to choose a second major or additional focus areas in complementary subjects that will enhance their data science knowledge and can be applied to another area of personal interest.
“It definitely opens up pathways for people in different disciplines as long as they have large data sets and they want to leverage those data sets to understand different aspects of their problems in their domain,” Samal said. “Whether it's sociology, psychology, arts, or engineering, machine learning tools can be helpful.”
Coursework, which includes interactive ZyBooks lessons, lectures, in-class quizzes, and labs, covers classification, clustering, cleaning, visualization, and evaluation. Students learn how to identify the appropriate models, select the right software packages, evaluate their results, and adjust models as necessary.
“They train a model, test it, and if it doesn’t perform well, they go back and adjust parameters or try a different technique,” Samal said. “Evaluation and refinement are very important parts of the course.”
Samal said an equally critical component is understanding how to correctly apply concepts when using tools in order to generate and ensure accurate results.
“With all these tools, it’s become just as important to see how to frame questions to get what you want,” Samal said. “Sometimes it requires skill to ask the right questions.”
The course’s curriculum will prepare many students well for future data-centric careers, but it also offers an additional professional opportunity: the option to complete an IBM Professional Certificate through Coursera. Students who choose to complete the certificate receive both a resume credential and reduced weight on their final exam at no additional cost.
“Hopefully that will be useful and something they can post on LinkedIn, but it is also complementary, since there are some topics we don't cover in that much detail,” Samal said. “They can cover more of those concepts and show they’ve completed a professional certificate.”
Samal said by mastering these concepts and learning how to analyze not only data sets but machine learning problems themselves, students will be capable of discovering solutions and adapting to changes no matter how technology transforms in the future.
“They can solve a variety of machine learning problems using currently available tools, but it's not the tools that are important. It’s that they understand the core machine learning problem and how that can be leveraged to solve real-world problems.”
CSCE 320: Data Analysis (or Data Analysis with Machine Learning) will be offered in the fall 2026 semester. CSCE 155: Computer Science I is a prerequisite for enrollment. Students can begin enrolling after priority registration opens on Monday, March 23.
Interested students with questions can contact Ashok Samal at samal@unl.edu.
More details at: https://computing.unl.edu/news/updated-computing-course-teaches-students-machine-learning-approach-data-analysis/