Computing and Data Science in Practice, CSCE 386, 001
Kiewit Hall A445, Tue/Thu, 11:00 AM – 12:15 PM
Instructor Name(s): Juan Cui
Instructor Contact Information: 122E, Avery Hall, 402-472-5023, jcui@unl.edu
Hours of Availability: Tue/Thu 2:30 PM – 3:30 PM
Course Prerequisites: Grade of "Pass" or "C" in CSCE 311.
Catalog Description: Studies in data science practice and professional development. Data science topics include data-centric and model-driven approaches; information and knowledge structures, organization, and access; searching and mining; and visualization. Professional development involves instruction in career development, entrepreneurship, professional ethics, and professional communications.
Learning Outcomes: By the end of this course, students will be able to:
- Understand Core Principles of data generation, management, analysis, and modeling.
- Evaluate Governance & Ethics in data science, including stewardship, privacy, and responsible use.
- Explore Real-World Applications in biomedical research, economics, geospatial analysis, power trading, energy, agriculture, and public policy.
- Analyze Case Studies to understand how data is collected, visualized, and applied in decision-making.
- Apply Concepts Practically through reflective essays, project proposal development, and peer-reviewed group presentations.
Course Required Materials: No required textbook. Reading materials and seminar slides will be posted on Canvas.
Course Expectations and Rules Specific to Course
Include course-specific attendance policy and other course rules here.
Continuity of Instruction Policy
If in-person classes are canceled, you will be notified of the instructional continuity plan for this class via Canvas.
Course Grading Policy
- Reflection Essays (30%): Students will write short reflective essays on selected seminars or lectures. Essays should summarize key takeaways, connect the content to personal experiences or prior knowledge, and highlight new insights gained.
- Group Project Proposal (30%): Students will work in teams of 2-3 to select a data science–related topic and develop a research-style proposal applying knowledge from the seminars to real-world problems. Each group is responsible for submitting a written proposal and participating in the peer review process.
- Peer Review and Discussion (20%): Proposals and discussion prompts will be shared on the Canvas forum. Students are expected to provide constructive feedback to peers and actively engage in meaningful dialogue, either anonymously or openly.
- Group Presentation (20%): Each team will deliver an oral presentation of their project proposal, followed by a Q&A session with the class.
- Incentive Bonus Points: Bonus points (up to 10) may be awarded for completion of approved professional certificates and participation in class activities and post-talk surveys.
- Late Policy: All assignments turned in within 24 hours after the due will be receiving a penalty of 20%. After 24 hours, no assignments will be accepted.
| Letter Grade | Score |
| A+ | >100 |
| A | 93-99 |
| A- | 90-92 |
| B+ | 87–89 |
| B | 83–86 |
| B- | 80–82 |
| C+ | 77–79 |
| C | 73–76 |
| C- | 70–72 |
| D+ | 67–69 |
| D | 63–66 |
| D- | 60-62 |
| F | <60 |
AI Use Policy
Students may use AI tools for brainstorming, grammar checking, and initial research and coding assistance, provided that: (1) all AI assistance is disclosed in an AI Use Statement, (2) the work demonstrates substantial personal contribution and original analysis, (3) all content is fact-checked and revised, and (4) the use of AI does not bypass essential learning objectives. Submitting AI-generated work without personal contribution constitutes academic misconduct. If you are uncertain about appropriate AI use, please consult with me.
UNL Course Policies and Resources
Students are responsible for knowing the university policies and resources found on this page (https://go.unl.edu/coursepolicies):
University-wide Attendance Policy
Academic Honesty Policy
Services for Students with Disabilities
Mental Health and Well-Being Resources
Final Exam Schedule
Fifteenth Week Policy
Emergency Procedures
Diversity & Inclusiveness
Title IX Policy
Other Relevant University-Wide Policies