课程发布公告撰写

课程发布公告：《Python入门与进阶》

一、课程简介与办学宗旨 本课程系统覆盖Python编程从零基础到中级应用所需的核心知识与能力，兼顾语言特性、数据处理与可视化、软件工程实践及并发与网络等进阶主题。课程以学习科学与计算机教育研究的循证证据为设计依据，通过主动学习、形成性评价、即时反馈与项目化学习等策略，提高学习成效与迁移能力（Freeman et al., 2014; Prince, 2004; Black & Wiliam, 1998）。课程目标在于培养学习者编写稳健、可维护、可测试且符合风格规范的Python程序之能力，并能在真实情境中整合数据与工程实践解决问题。

二、课程定位与目标受众

• 定位：编程入门至进阶的桥梁型课程，面向跨学科初学者与有一定编程经验者的系统进阶需求。
• 受众：对数据分析、自动化脚本、科研计算或软件开发感兴趣的高校学生与在职学习者。
• 先修：无需编程背景；建议具备基本逻辑与高中数学素养。

三、可测量学习目标（完成课程后，学习者能够）

• 知识与理解：
  • 解释Python基本语法、内置数据结构与执行模型，阐述命名空间、可变性与异常处理的原理。
  • 阐明迭代器/生成器、面向对象、上下文管理器与类型标注的用途与局限。
• 技能与应用：
  • 使用控制流、函数式抽象与数据结构实现中小型程序；遵循PEP 8编写可读、可维护代码（van Rossum et al., PEP 8）。
  • 以pytest进行单元测试与测试驱动的迭代开发（Erdogmus et al., 2005），应用logging与错误处理提升健壮性。
  • 利用NumPy/pandas/matplotlib完成数据清洗、分析与可视化，遵循可重复研究与可追溯实践（Kluyver et al., 2016; Rule et al., 2018）。
• 迁移与评估：
  • 设计并实现含命令行接口或简易API的应用，权衡并发模型（多线程、多进程、asyncio）以满足性能与可维护性要求。
  • 运用代码审查与同侪反馈改进方案质量，反思并阐明设计取舍与测试充分性。

四、课程结构与模块（建议12周，约36学时＋课后实践）

• 模块A：编程基础
  • 主题：数据类型与表达式、控制流、函数与作用域、基本数据结构（列表/字典/集合/元组）、文件I/O。
• 模块B：Pythonic实践
  • 主题：迭代器与生成器、列表推导式、错误与异常、上下文管理器、模块与包、虚拟环境与依赖管理、类型标注与静态检查。
• 模块C：数据处理与可视化
  • 主题：NumPy数组运算、pandas数据帧操作、数据清洗与转换、基本可视化。
• 模块D：软件工程与质量保障
  • 主题：版本控制（Git）、代码风格与自动格式化、单元测试与TDD（pytest）、日志与配置管理、包发布入门。
• 模块E：并发、网络与数据库入门
  • 主题：多线程/多进程/asyncio、HTTP与简单API、SQLite与ORM（入门）、基本性能分析与优化。
• 模块F：期末综合项目
  • 形式：数据分析报告或小型应用（脚本/CLI/API），要求代码审查、测试覆盖与技术说明书。

五、教学与学习策略（循证设计）

• 主动学习与现场编码演示：通过问题驱动练习与现场演示降低认知负荷、增强可视化思维与即时纠错（Freeman et al., 2014; Prince, 2004）。配合“范例–变式–迁移”的任务梯度，结合示例学习有效促进技能内化（Atkinson et al., 2000; Sweller, 1988）。
• 形成性评价与检索练习：以短测验、在线练习与代码单测促进“测试效应”和间隔练习，提高长期保持与迁移（Roediger & Karpicke, 2006）。
• 结对编程与同侪评审：提升问题求解质量与代码可读性，并促进元认知与沟通协作（Salleh et al., 2011）。提供明确评审准则与示例以保障质量与公平。
• 工具支持与可重复性：Jupyter/VS Code与自动化测试、持续集成示例，强化可追溯与复现规范（Kluyver et al., 2016; Rule et al., 2018）。
• 包容性与通用学习设计：多模态材料、分层挑战与可及性规范，支持不同基础与需求的学习者（CAST, 2018）。

六、评价方式与学术诚信

• 评价构成（建议权重，可由教学安排微调）：
  • 形成性测验与在线练习：20%（即时反馈，不计入成绩的练习亦用于诊断）
  • 编程作业（阶段性4–6次）：40%（配评分量表，包含正确性、风格、文档与测试覆盖）
  • 同侪评审与代码审查：10%（依据建设性反馈质量与修订效果）
  • 期末综合项目（含演示与技术文档）：30%
• 通过标准：项目与作业均需达到合格线；未通过任一核心任务者不授予结业。
• 学术诚信：采用版本历史审查与代码相似性检测工具（如MOSS）进行学术规范核验，提供澄清与申诉通道。强调独立完成与恰当引用第三方库与示例。

七、学习资源与平台

• 编程环境：Python 3.12或更新版本；建议使用venv进行环境隔离；编辑器推荐VS Code或JupyterLab。
• 版本控制与协作：Git（提供基础工作流与提交规范）。
• 风格与文档：PEP 8规范、docstring与README模板、示例性项目骨架。
• 参考资料（官方优先）：Python官方教程与标准库文档；入门数据分析的官方指南与社区教程（经筛选与标注难度）。

八、学习时程与工作量预期

• 建议学时：每周3学时课堂＋每周4–6小时课后实践。
• 节奏安排：每周“预习微课＋课堂实践＋课后巩固”，采用周度学习目标与检查清单，提供可视化进度追踪。

九、技术与支持

• 作业自动评测与即时反馈平台，提供失败用例与最小复现指引。
• 办公时段与线上答疑：固定时段助教与教师支持。
• 可及性支持：按需提供无障碍材料与替代评估安排。

十、报名与名额

• 名额与开班细则将依据注册需求与教学资源进行配置；报名开放后将发布选课说明与候补机制。

本课程坚持基于证据的课程设计，兼顾可学性、可教性与可评性，欢迎关注并报名参与。

参考文献

• Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of Educational Research, 70(2), 181–214. https://doi.org/10.3102/00346543070002181
• Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education, 5(1), 7–74. https://doi.org/10.1080/0969595980050102
• CAST. (2018). Universal Design for Learning Guidelines version 2.2. https://udlguidelines.cast.org
• Erdorgmus, H., Morisio, M., & Torchiano, M. (2005). On the effectiveness of the test-first approach to programming. IEEE Transactions on Software Engineering, 31(3), 226–237. https://doi.org/10.1109/TSE.2005.37
• Freeman, S., et al. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111
• Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
• Kluyver, T., Ragan-Kelley, B., et al. (2016). Jupyter Notebooks—a publishing format for reproducible computational workflows. In F. Loizides & B. Schmidt (Eds.), Positioning and Power in Academic Publishing: Players, Agents and Agendas (pp. 87–90). IOS Press. https://doi.org/10.3233/978-1-61499-649-1-87
• Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223–231. https://doi.org/10.1002/j.2168-9830.2004.tb00809.x
• Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning. Psychological Science, 17(3), 249–255. https://doi.org/10.1111/j.1467-9280.2006.01693.x
• Rule, A., Tabard, A., & Hollan, J. D. (2018). Exploration and explanation in computational notebooks. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–12. https://doi.org/10.1145/3173574.3173606
• Salleh, N., Mendes, E., & Grundy, J. (2011). Empirical studies of pair programming: A systematic literature review. IEEE Transactions on Software Engineering, 37(4), 509–525. https://doi.org/10.1109/TSE.2010.59
• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
• van Rossum, G., Warsaw, B., & Coghlan, N. (n.d.). PEP 8 — Style Guide for Python Code. Python Software Foundation. https://peps.python.org/pep-0008/
• Python Software Foundation. (n.d.). The Python Tutorial. https://docs.python.org/3/tutorial/

Course Announcement: Data Visualization Basics

Purpose and Rationale Data visualization is a foundational competency for evidence-informed decision-making across scientific, public, and private sectors. Empirical research demonstrates that specific visual encodings differentially support accurate quantitative judgment (e.g., position and length outperform area and color for magnitude comparison), making principled design knowledge essential for valid analysis and communication (Cleveland & McGill, 1984; Ware, 2013). Further, task-centric design frameworks and layered design processes improve fitness-for-purpose and interpretability in applied contexts (Munzner, 2014; Heer, Bostock, & Ogievetsky, 2010). Ethical and accessible visualization practices are equally critical to ensure truthful representation, audience trust, and inclusive use, particularly with public-facing visuals (Cairo, 2016; Tufte, 1983; World Wide Web Consortium [W3C], 2018).

Data Visualization Basics is a new, introductory-level course that equips learners with theory-grounded, practice-oriented skills for designing effective charts, selecting appropriate encodings, and communicating quantitative insights with rigor and integrity. The course emphasizes perceptual principles, task abstraction, evaluation through critique, and ethical standards, ensuring that students can create visuals that are accurate, comprehensible, and accessible.

Intended Audience and Prerequisites

• Audience: Early-career analysts, researchers, data-curious professionals, and students seeking a principled introduction to visualization.
• Prerequisites: Comfort with basic descriptive statistics (e.g., distributions, summary measures) and spreadsheets. No prior programming is required; optional pathways in R or Python are provided for those wishing to code.

Learning Outcomes By the end of the course, students will be able to:

• Map data types to effective visual encodings and chart forms, grounded in perceptual evidence (Cleveland & McGill, 1984; Ware, 2013).
• Apply a task-driven design process (from problem characterization to encoding selection and annotation) to produce fit-for-purpose visuals (Munzner, 2014).
• Critically evaluate charts for accuracy, efficiency, ethical integrity, and accessibility, using established criteria and empirical findings (Cairo, 2016; Bateman et al., 2010; W3C, 2018).
• Construct simple, reproducible charts using at least one toolchain (e.g., spreadsheet+tableau-style tools, R/ggplot2, or Python/Altair), with clear design rationales.
• Communicate insights through concise, audience-appropriate narratives and visual annotations, minimizing cognitive load and visual clutter (Tufte, 1983; Few, 2012).

Course Design and Pedagogy The course blends short, evidence-based lectures with active learning labs and guided critiques. Active learning structures (e.g., worked examples, peer review, and retrieval practice) are employed given their demonstrated efficacy in improving performance in STEM education (Freeman et al., 2014). Weekly modules proceed from foundational perceptual principles and chart taxonomies to comparative design decisions, narrative techniques, and accessibility.

Representative Topics

• Visual perception and cognition for design: preattentive features, Gestalt, color theory; effectiveness of encodings (Ware, 2013; Cleveland & McGill, 1984).
• Chart families and task alignment: comparison, distribution, composition, and relationship tasks; when not to use a chart type (Heer et al., 2010).
• Design process and critique: problem/task abstraction, encoding/mark selection, annotation, iteration, and evaluation (Munzner, 2014).
• Truthful and ethical visualization: avoiding misleading scales, cherry-picking, and spurious emphasis; transparency and uncertainty communication (Cairo, 2016; Tufte, 1983).
• Accessibility and inclusion: color-vision-safe palettes, contrast, text alternatives, and screen-reader considerations (W3C, 2018).

Assessment and Evidence of Learning

• Low-stakes quizzes to reinforce key perceptual and design principles.
• Lab assignments that require implementing and justifying design choices for specific analytic tasks, with structured peer feedback.
• Critique memos that apply empirical criteria to real-world visualizations.
• A capstone project: end-to-end design of a small visualization portfolio grounded in task analysis and supported by scholarly rationale. Rubrics prioritize correctness of data-to-visual mapping, perceptual effectiveness, ethical clarity, accessibility, and communication quality, consistent with established guidance (Cleveland & McGill, 1984; Ware, 2013; Cairo, 2016; W3C, 2018).

Tools and Resources Students may complete assignments using:

• Spreadsheet and GUI-based tools (e.g., Tableau Public) for rapid prototyping and communication.
• R with ggplot2 or Python with Altair for reproducible, grammar-of-graphics workflows. Curated datasets and templates are provided to scaffold skill development while emphasizing design reasoning over tool-specific mechanics.

Quality and Accessibility Commitments The course integrates inclusive design practices and provides guidance aligned with WCAG 2.1 for color contrast, alternative text, and descriptive annotation (W3C, 2018). Examples and datasets are selected to reflect diverse application domains and audiences.

Enrollment Enrollment is now open. Prospective learners should have basic statistical literacy and an interest in rigorous, communicative data practices. Further details on schedule and delivery modality will be provided upon registration.

References Bateman, S., Mandryk, R. L., Gutwin, C., Genest, A., McDine, D., & Brooks, C. (2010). Useful junk? The effects of visual embellishment on comprehension and memorability of charts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2573–2582). ACM.

Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. New Riders.

Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531–554.

Few, S. (2012). Show me the numbers: Designing tables and graphs to enlighten (2nd ed.). Analytics Press.

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415.

Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Communications of the ACM, 53(6), 59–67.

Munzner, T. (2014). Visualization analysis and design. CRC Press.

Tufte, E. R. (1983). The visual display of quantitative information. Graphics Press.

Ware, C. (2013). Information visualization: Perception for design (3rd ed.). Morgan Kaufmann.

World Wide Web Consortium. (2018). Web Content Accessibility Guidelines (WCAG) 2.1. W3C. https://www.w3.org/TR/WCAG21/