CSC 405/605/705 - Spring 2026 - Syllabus

Instructor: Xiaochen Li

Lectures: Mon/Wed 12:30-13:45, Petty Building, Room 303

Office: Petty 153

Office Hours： Monday/Wednesday 10:00 - 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: In today’s world, where data generation is prolific, both from human and machine sources, the realm of computer science has shifted from focusing primarily on computation-intensive methods to embracing data-intensive strategies. This change underscores the potential to solve real-world problems by analyzing diverse, complex, and unstructured datasets through Data Science methodologies. This course offers a highly interactive environment where students will delve into the theories, techniques, and tools essential for extracting meaningful insights from large datasets. Adopting a problem-based learning approach, participants will utilize these technologies to create data-driven solutions capable of processing and analyzing real-world data for addressing various challenges in scientific, social, and environmental domains. The core topics addressed by the course will be:

  • Programming for Data Science
  • Data Mining, Munging, Wrangling
  • Introduction to Applied Data Modeling
  • Statistics, Analytics, Representation, Visualization

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. Suggested textbooks are: 1) Building Machine Learning Systems with Python (Richert and Coelho), 2) Data Science from Scratch (Joel Grus)

Student Learning Outcomes: In this course, students will delve into the fundamentals of Data Science, exploring a wide array of tools, techniques, and foundational concepts. Throughout the course, students will engage in a detailed examination of the entire data science workflow. This includes the processes of data collection, cleansing, exploration, and feature engineering. Additionally, students will gain hands-on experience in building models, validating results, and interpreting data insights. By the conclusion of the course, students will have acquired a thorough understanding of how to apply data science principles effectively in practical scenarios.

Course Topics and Schedule (Tentative) :

  1. Introduction to Data Science: (Week 1-3)
    • Data Science Introduction
    • Class Project discussion and team formed
    • Programming preparation 1). Re/Introduction to Python 2). Jupyter-Notebook
    • Data Science Reproducibility 1). Setting up your Repository – Data, Code, and Documentation 2). Using Version Control with Git
  2. Data Munging, Wrangling, Cleaning (Week 4-5)
    • Data Structures
    • Data Manipulation 1). Selection - Indexing 2). Handling Missing Data 3). Aggregation 4). Descriptive Statistics 5). Merging / Join 6). Working with Date-Time
  3. Data and Statistics (Week 6-9)
    • Distributions
    • Estimates
    • Statistical Hypothesis Testing
    • Correlation
    • Distribution Estimators: MoM, MLE, KDE
  4. Introduction to Applied Data Modeling: (Weeks 10-12)
    • Applied Machine Learning
    • Regression and Feature Selection
    • Bias versus Variance
    • Clustering and Dimensionality Reduction
    • Validation and Model Performance
    • Mathematical optimization (if time allowed)
    • Stochastic thinking (if time allowed)
    • Invited talk (if time allowed)
  5. Data Visualization (Week 13-14)
    • Graph Generation 1). Types of Graphs 2). Customizing Plots 3). Visualizing Errors 4). Interactive / Dynamic Graphs
    • Visualization Best Practices
  6. Project Presentations: (Week 15–16)
    • Project Review - Final presentation
    • Report submission

Grading Policy (No curve in the final grade)

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 %

  1. [Class Participation: 10%]

    Regular attendance is required for all class meetings. Students who are unable to attend an in-person class must notify the instructor in advance via email with a valid reason; otherwise, the absence will not be credited. Attendance may be recorded at any time during the class session, and the total number and timing of attendance checks are at the instructor’s discretion. Final participation grades will be based on the overall attendance record.

  2. [In class quizzes (2): 20%]

    Two in-class quizzes will be conducted during the semester, each contributing 10% to the final grade. Quizzes must be completed in class as scheduled; failure to attend or complete a quiz during class will result in no credit.

  3. [Assignments (3): 30%]

    Three programming-based assignments will be given covering the utilization of the tools learned in class. Each assignment accounts for 10 points. Absolutely no collaboration on assignments. Students must upload individual assignments to canvas before deadline. Later submission (per day) will have a 50% deduction, late for more than 2 days will directly have zero grade.

  4. [Project: 40% (30% for Graduate Students)]

    This course includes a group project focused on the complete end-to-end development of an analytical model. The project will be conducted in teams of 3–4 students and organized into the following stages:

    • Stage I: Dataset Selection and Project Setup
    • Stage II: Data Analysis, Distributions, and Hypothesis Testing
    • Stage III: Machine Learning and Deep Learning Model Development
    • Stage IV: Visualization, Final Report, and Presentation

    The project will progress alongside the course schedule, and the instructor will provide reminders and guidance on project milestones during relevant class sessions. Each group is required to submit a final written report and deliver an in-class presentation of approximately 15–20 minutes.

    Detailed project requirements and evaluation criteria are available at: link.

  5. [Research Report: 10% ( Graduate Students Only)]

    Graduate students are required to submit a project report using the IEEE Conference double-column format. Please use the official IEEE template.

    Students should choose a project topic relevant to the course and conduct a brief literature review by reading 2–4 related research papers, including at least one paper published after January 2025. The report should clearly reference and discuss these papers.

    The report should be at least 3 pages for a single author and 5 pages for two or more authors, inclusive of figures and references. The final report must be submitted before the final week of the course.

Academic Honesty Policy: The instructor will deal strictly with any violations of academic honesty and integrity in this course. Absolutely no discussion, collaboration, copying, and sharing on assignments. This includes coping from the internet. Any student who violates this policy will receive “F” directly in the course. The instructor will report the case to the university. Special Needs and/or Disabilities: Students with disabilities should have documentation from the Office of Accessibility Resources & Services. This documentation should be provided to the instructor for review. In the case of major provisions such as separate testing environment or test-readers, the student must make arrangements with Office of Accessibility Resources & Services so that suitable accommodations can be provided. 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. 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)