CSC 405/605/705 - Fall 2026 - Course Information and Policy
Instructor: Xiaochen Li
Lectures: Mon/Wed 12:30-13:45, Petty Building, Room 150
Office: Petty 153
Office Hours： Monday/Wednesday 10:00 am - 11:00 am
Email: X_LI12@uncg.edu - I answer most emails within one business day – do not expect responses evenings or weekends
Course Description and Learning Outcomes:
Modern data science has evolved beyond traditional programming and statistical analysis. In today’s AI-driven industry, data scientists are expected not only to analyze data, but also to effectively collaborate with AI assistants to improve productivity, automate routine tasks, and focus on higher-level reasoning and decision-making. This course provides a practical introduction to the complete data science workflow, including data preprocessing, statistical analysis, visualization, predictive modeling, and AI-assisted data analysis. Students will learn fundamental programming and analytical techniques while gaining hands-on experience with modern AI tools to build interactive data science applications.
By the end of the course, students will be able to independently conduct data analysis, critically evaluate AI-generated results, and integrate AI into a responsible and effective data science workflow that reflects current industry practice.
Prerequisites: A grade of C or better in CSC 330 and (STA 271 or STA 290), or permission of instructor (prior programming and statistics experience is required).
Textbooks: There is no required text for the course. Class slides will be available for download.
Course Topics and Schedule (Tentative):
1. Introduction to Data Science (2 lectures)
- What is Data Science? The Data Science workflow
- Introduction to essential development tools:
- Jupyter Notebook
- Git and GitHub
2. Python Programming for Data Science (7 lectures)
- Python programming fundamentals
- Data manipulation with Pandas
- Data loading and inspection
- Data selection and indexing
- Descriptive statistics
- Data cleaning:
- Handling missing values
- Removing duplicates
- Detecting and treating outliers
- Correcting inconsistent data formats
- Numerical computing with NumPy
3. Statistics for Data Science (6 lectures)
- Descriptive statistical measures
- Probability distributions
- Distribution estimation methods
- Method of Moments (MoM)
- Maximum Likelihood Estimation (MLE)
- Kernel Density Estimation (KDE)
- Correlation analysis
- Statistical hypothesis testing
4. Data Visualization (3 lectures)
- Principles of effective data visualization
- Creating visualizations with Python
- Common chart types
- Plot customization
- Interactive and dynamic visualizations
5. Introduction to Applied Data Modeling (2 lectures)
- Decision Trees
- Regression models
- Model validation and performance evaluation
6. AI-Assisted Data Analysis (3 lectures)
- Introduction to AI-assisted Data Science
- Building interactive data applications with Streamlit
- Integrating LLMs using LangChain
- Building an end-to-end data science project with LLMs
7. Final Project Presentations (2 lectures)
- Student project presentations
Grading Policy
| Grade | Score Max % | Score Min % |
|---|---|---|
| A | 100% | 92% |
| A- | < 92% | 89% |
| B+ | < 89% | 86% |
| B | < 86% | 83% |
| B- | < 83% | 80% |
| C+ | < 80% | 77% |
| C | < 77% | 74% |
| C- | < 74% | 70% |
| D+ | < 70 % | 67% |
| D | < 67% | 64% |
| D- | < 64 % | 60 % |
| F | < 60% | 0 % |
For undergraduates:
| Category | |
|---|---|
| Assignments (2) | 26% (Each accounts for 13%) |
| In-class quizzes (3) | 24% (Each accounts for 8%) |
| Project | 50% |
For graduates:
| Category | |
|---|---|
| Assignments (2) | 26% (Each accounts for 13%) |
| In-class quizzes (3) | 24% (Each accounts for 8%) |
| Project | 40% |
| Research Report | 10% |
Attendance Policy:
Regular attendance is expected and strongly encouraged. Routine attendance will not normally be recorded. Students are responsible for managing their own learning and ensuring they remain current with all course materials, announcements, and in-class activities.
If overall class attendance or participation becomes unsatisfactory, the instructor may introduce attendance tracking and/or participation incentives during the semester. Any such changes will be announced in advance.
Late Policy and Makeup Exams:
- Quizzes must be completed in class as scheduled. Missing a quiz or failing to complete it during the scheduled class period will result in a score of zero. Quiz dates are provided at the beginning of the semester; therefore, personal travel or other foreseeable commitments are not acceptable excuses. Make-up quizzes will be granted only in documented cases of serious emergencies or other exceptional circumstances, and only with prior approval whenever possible.
- Assignments and the course project are due by 11:59 PM on the stated due date. Submissions received within 7 calendar days after the deadline will incur a 25% late penalty. Work submitted more than 7 calendar days late will not be accepted.
- No individual extra-credit assignments will be offered. To ensure fairness and consistency, additional assignments will not be provided to make up missed or low scores.
Midterm Grades:
A midterm grade will be assigned to all undergraduate students and made available in UNCGenie. This grade reflects your current performance based on completed coursework and is intended for feedback purposes only; it will not be used in calculating the final course grade. The midterm grade is meant to help you assess whether you are on track or need to make adjustments. Students receiving a D or F at midterm are encouraged to contact the instructor to discuss possible strategies and options for continuing in the course. Final grades will be determined based on all required work completed throughout the semester.
Academic Honesty Policy:
Academic honesty and integrity are fundamental expectations in this course. All quizzes, assignments, and course projects must represent your own individual work. Unless explicitly authorized by the instructor, collaboration, copying, sharing, or submitting another person’s work is strictly prohibited.
Any violation of this policy—including plagiarism, unauthorized collaboration, or other forms of academic misconduct—will result in a failing grade (“F”) for the course. If two or more students submit substantially similar work, all parties involved may receive a failing grade (“F”) for the course pending the instructor’s review. In addition, all cases of academic misconduct will be reported to the University and handled in accordance with the University’s academic integrity policies.
ADA Statement:
UNCG seeks to comply fully with the Americans with Disabilities Act (ADA). Students requesting accommodations based on a disability must be registered with the Office of Accessibility Resources and Services located in 215 Elliott University Center: (336) 334-5440 (or on the web at https://oars.uncg.edu).
Health and Wellness:
Health and well-being have a big impact on your learning and academic success. Throughout your time at UNCG, you may experience a range of concerns that impact your personal and academic success. These might include illnesses, strained relationships, anxiety, high levels of stress, alcohol or drug concerns, crime victimization, feeling down, loss of motivation, or death of a loved one. It is OK TO ASK FOR HELP!
- Student Health Services (SHS) (336-334-5340): For preventative and acute healthcare, SHS offers a primary medical clinic, full pharmacy, and over-the-counter medications.
- Counseling & Psychological Services (336-334-5874): free confidential mental health services
- Spartan Well-Being
- Campus Violence Response Center (336-334-9839)
- Spartan Recovery offers recovery support services (SRP@uncg.edu)