课程大纲创建指导

Title: Data Literacy Fundamentals for New Employees

Audience

• New hires across functions (non-technical and technical)
• No prior analytics or coding experience required

Delivery

• 8 modules, 12–14 total hours (can run as 2 half-days or self-paced over 2 weeks)
• Blend short lectures, guided practice, and job-relevant exercises
• Provide a sandbox dataset and access to spreadsheet and BI/SQL tools

Course Outcomes By the end, learners will:

• Define key data concepts and the data lifecycle
• Find, understand, and request data appropriately
• Assess and improve basic data quality
• Analyze data responsibly, respecting privacy, security, and governance
• Summarize and visualize findings clearly and accurately
• Communicate data-supported insights to stakeholders

Required Materials

• Company data policies summary (privacy, security, governance, retention, classification, data request process)
• Sample datasets (CSV and XLSX) and a data dictionary
• Spreadsheet tool (e.g., Excel/Sheets) and optional BI/SQL sandbox
• Quick-reference guides (formulas, chart chooser, data quality checklist)

Module 1. Orientation: What Data Literacy Means Here (60–75 min) Objectives

• Define data literacy in the workplace context
• Identify where data lives and who owns it
• Follow the data lifecycle and your responsibilities at each stage

Do this

1. State the data lifecycle: create/collect → store → access → transform → analyze → share → retain/retire.
2. Map the people: data producers, stewards/owners, analysts, engineers, business stakeholders, compliance.
3. Locate data: navigate the catalog/wiki, BI dashboards, shared drives, ticketing system.
4. Apply roles and responsibilities: know what you can/cannot do with data you handle.

Practice

• Find a dataset in the catalog, locate its owner, and read its metadata (purpose, refresh cadence, definitions).

Assess

• 5-question check: identify the right owner and access path for a sample request.

Module 2. Data Foundations: Types, Formats, and Structure (75–90 min) Objectives

• Recognize common data types and file formats
• Read and interpret tables with keys, units, and metadata

Do this

1. Distinguish types: categorical (nominal/ordinal), numeric (continuous/discrete), datetime, text, boolean.
2. Recognize structures: tables with rows/columns, primary keys, foreign keys, wide vs long format.
3. Handle formats: CSV (delimiters/quotes/encoding), XLSX, JSON basics; mind decimal separators and time zones.
4. Use metadata: data dictionary, column definitions, units, valid ranges, refresh schedules.

Practice

• Open a CSV; set delimiter and encoding; verify headers; identify the primary key; spot mixed data types in a column.

Assess

• Label the data type for 8 example fields; choose correct file import settings in a scenario.

Module 3. Data Quality Essentials and Basic Cleaning (90 min) Objectives

• Evaluate data against quality dimensions
• Execute essential cleaning tasks in a spreadsheet

Do this

1. Check quality dimensions: accuracy, completeness, consistency, timeliness, validity, uniqueness.
2. Validate values: check ranges, allowed values, date formats, duplicates, missingness patterns.
3. Clean safely: create a copy, log changes, version files, avoid overwriting raw data.
4. Apply spreadsheet techniques: TRIM, CLEAN, UPPER/LOWER, TEXTSPLIT/Text to Columns, IFERROR, VLOOKUP/XLOOKUP, COUNTIF, Remove Duplicates, Data Validation.
5. Document assumptions and transformations.

Practice

• Use a checklist to fix common issues (extra spaces, inconsistent categories, missing dates, duplicate IDs) and write a 3-line change log.

Assess

• Before/after dataset comparison; identify which quality dimensions improved.

Module 4. Responsible Data Use: Privacy, Security, and Governance (75 min) Objectives

• Classify data and apply least-privilege access
• Handle personal and sensitive data ethically and securely

Do this

1. Classify information: public, internal, confidential, restricted (use your organization’s labels).
2. Identify personal data (PII) and sensitive attributes; minimize collection and use only for legitimate purposes.
3. Protect data: store in approved locations, share via secure channels, avoid emailing raw datasets unless permitted.
4. Follow access rules: request access via ticketing; do not share credentials; review permissions regularly.
5. Respect retention: follow approved retention and deletion schedules.
6. Report incidents: escalate suspected data exposure or phishing immediately via the defined process.

Practice

• Classify 10 examples; choose the correct sharing method; draft a short purpose statement for a data request.

Assess

• Scenario quiz on appropriate handling, access, and incident response steps.

Module 5. Framing Questions and Defining Metrics (60–75 min) Objectives

• Translate business questions into measurable metrics and data requirements
• Avoid vanity metrics; define clear operational definitions

Do this

1. Use SMART framing: Specific, Measurable, Achievable, Relevant, Time-bound.
2. Define metrics: name, formula, unit, scope, filters, and refresh cadence; distinguish metric vs KPI vs OKR.
3. Guard against pitfalls: vanity metrics, undefined denominators, mixed cohorts, shifting definitions.
4. Plan data needs: identify sources, grain, time windows, and necessary joins.

Practice

• Convert “Improve user engagement” into 2–3 SMART questions; write operational definitions for each metric.

Assess

• Peer-review metric definitions for clarity and measurability.

Module 6. Basic Analysis and Statistics for Everyone (90 min) Objectives

• Summarize data correctly and recognize common biases and errors
• Interpret results cautiously (correlation ≠ causation)

Do this

1. Describe data: count, sum, mean/median, min/max, percent, rate; use pivot tables for group summaries.
2. Explore distributions: histograms, box plots; identify skew and outliers.
3. Sample wisely: understand sampling bias and nonresponse; prefer representative cuts.
4. Interpret relationships: scatterplots; correlation vs causation; confounding variables.
5. Understand experiments at a high level: A/B basics (randomization, control, sample size, outcome metric); avoid peeking and multiple-comparison pitfalls.

Practice

• Build a pivot to compare conversion rate by channel; visualize distribution; note potential biases.

Assess

• Short interpretation quiz with plots and summary tables.

Module 7. Finding and Querying Data (Spreadsheet, BI, and Intro SQL) (90 min) Objectives

• Locate data and answer basic questions using BI or SQL
• Read metadata to construct correct filters and joins

Do this

1. Navigate BI: filter, segment, drill down, export responsibly; verify dashboard refresh times.
2. Read data catalogs: owners, lineage, last update, definitions.
3. Use intro SQL (optional track): SELECT, WHERE, ORDER BY, LIMIT; GROUP BY with aggregates; INNER JOIN on keys; watch for duplication when joining.
4. Validate results: row counts, sanity checks, reconcile to known totals.

Practice

• Answer 3 business questions using either BI filters or simple SQL; show query or filter steps; include validation checks.

Assess

• Submit answers with method and validation notes; facilitator spot-checks results and logic.

Module 8. Visualization and Data Storytelling (75–90 min) Objectives

• Choose appropriate charts and design for clarity
• Communicate insights with minimal bias and maximum transparency

Do this

1. Match chart to task: comparison (bar), trend (line), distribution (histogram/box), part-to-whole (stacked bars with care), relationship (scatter).
2. Design clearly: informative titles, labeled axes and units, consistent scales, avoid chartjunk and 3D effects, use colorblind-safe palettes.
3. Provide context: define metric and period, show baselines or targets, annotate anomalies, include caveats and data quality notes.
4. Avoid misleads: do not truncate axes for bars; disclose data exclusions and methods.

Practice

• Create a 1-slide chart with title, annotation, and takeaway; get peer feedback using a checklist.

Assess

• Revise chart based on feedback; submit final with a 3-sentence narrative.

Capstone Project (2–3 hours total, spread across week) Prompt

• Investigate a realistic question (e.g., “Which onboarding channels yield the highest 30-day activation rate, controlling for region and plan?”).

Steps

1. Confirm the question and define metrics and cohorts.
2. Locate data and obtain access; log data classification and approvals.
3. Assess data quality; document cleaning steps.
4. Analyze and validate; include at least one check against known totals.
5. Visualize results; write a 1-page brief with recommendations, risks, and next steps.
6. Present a 5-minute readout; answer stakeholder questions.

Rubric

• Accuracy and validation (40%)
• Responsible data handling (20%)
• Clarity of definitions and methods (20%)
• Communication and recommendations (20%)

Assessment Plan

• Pre-course self-assessment and 10-question baseline quiz
• Module quizzes (5–8 questions each)
• Capstone scored with rubric
• Post-course quiz and reflection on how to apply in role

Implementation Steps for L&D

1. Localize with company policies, classification labels, and data request workflows.
2. Prepare datasets with a data dictionary and seeded quality issues for practice.
3. Set up access to BI/SQL sandboxes and ensure safe, non-production data.
4. Train facilitators; provide answer keys and checklists.
5. Schedule office hours and a discussion channel for questions.
6. Track outcomes: completion, quiz improvement, capstone quality, and 60-day on-the-job application survey.

Job Aids (deliver as PDFs or wiki pages)

• Data quality checklist
• Metric definition template
• Chart chooser and design checklist
• Data handling and sharing quick guide
• SQL and spreadsheet formula cheatsheets

Common Pitfalls to Address Explicitly

• Mixing time zones or date formats
• Using inconsistent metric definitions across teams
• Joining at the wrong grain and inflating counts
• Ignoring missing data patterns
• Sharing data via unapproved channels
• Drawing causal claims from observational data

Tips for Inclusive and Accessible Learning

• Add alt text to charts and avoid color-only encoding
• Offer keyboard-accessible files and captions for recordings
• Provide examples from multiple functions (sales, product, HR, ops)

Suggested Schedule (example)

• Day 1: Modules 1–4 (with breaks)
• Day 2: Modules 5–8 + Capstone kickoff
• Week after: Capstone work + presentations

Next Steps for Learners

• Save the job aids; bookmark the data catalog and policies
• Identify one team metric to redefine clearly
• Schedule a short meeting with your data steward to understand available data and best practices

Python 程序设计基础课程大纲（可用于8–12周入门课程）

一、课程定位与目标

• 明确目标：
  • 掌握 Python 基本语法、核心数据结构与控制流
  • 能编写清晰、可维护的小型脚本与命令行程序
  • 能使用标准库和虚拟环境管理依赖
  • 能进行基础调试、测试与代码质量控制
• 完成标准：
  • 独立完成一个含文件读写、异常处理与测试的CLI小项目
  • 代码符合基本风格规范（PEP 8），含必要文档与类型注解

二、目标学员与先修

• 适合对象：零基础或有少量编程经验的学习者
• 先修要求：基本计算机操作、命令行基础（可在第1模块补充）

三、教学组织与评估

• 建议节奏：每模块2–4学时；每两模块设置一次阶段测评
• 学习闭环（每模块执行）：
  1. 预习概念与示例
  2. 跟做演示与分步练习
  3. 独立完成小作业
  4. 通过单元测验与代码走查反馈纠错
• 评估构成：
  • 过程作业与小测：40%
  • 阶段项目（中期Mini Project）：20%
  • 期末项目：40%

四、课程模块大纲与指令化步骤

模块1 环境搭建与工具链

• 目标：在本地搭建可用的Python 3.10+开发环境，掌握脚本运行与REPL
• 核心点：Python安装、VS Code/IDE、终端、虚拟环境、pip
• 步骤：
  1. 安装 Python 3.10+，在终端运行 python --version 验证
  2. 安装 VS Code 并启用 Python 扩展
  3. 在项目目录创建虚拟环境：python -m venv .venv
  4. 激活环境：Windows .venv\Scripts\activate；macOS/Linux source .venv/bin/activate
  5. 安装一个包以验证：pip install requests；导出依赖：pip freeze > requirements.txt
  6. 运行脚本：python hello.py；进入交互式解释器：python
• 练习：打印“Hello, Python”，从命令行读取一个姓名并问候
• 检查点：能创建/激活venv、安装包、运行.py

模块2 基础语法与数据类型

• 目标：理解变量、基本类型与运算
• 核心点：int/float/bool/str、赋值、运算符、f-string、注释
• 步骤：
  1. 声明变量并做四则运算
  2. 使用 input() 获取字符串并转型为 int
  3. 用 f-string 输出格式化文本
  4. 使用 type() 与 isinstance() 检查类型
• 练习：简单计算器（加减乘除，包含输入校验）
• 误区提示：避免用内置名作变量名（如 list、str）

模块3 控制流：条件与分支

• 目标：能用 if/elif/else 控制执行路径
• 核心点：比较/布尔运算、嵌套条件、三元表达式
• 步骤：
  1. 编写基于分数的等级判定
  2. 使用 and/or/not 组合条件
  3. 使用条件表达式在一行内设置状态
• 练习：登录校验（用户名/密码匹配，次数限制）
• 检查点：分支覆盖所有情况并有合理默认分支

模块4 迭代与序列操作

• 目标：掌握 for/while、range、enumerate
• 核心点：迭代协议、break/continue、列表切片
• 步骤：
  1. 用 for 遍历列表并带索引输出
  2. 用 while 实现倒计时，处理退出条件
  3. 使用切片 [start:stop:step] 提取子序列
• 练习：求列表最大值与平均值（不使用内置 max/sum）
• 误区提示：防止死循环；注意可变序列切片是新对象

模块5 核心容器：list/tuple/dict/set

• 目标：熟悉常用容器的特性与操作
• 核心点：增删改查、遍历模式、浅拷贝/深拷贝
• 步骤：
  1. 对列表做增删改，演示可变性
  2. 用字典存储学生成绩，遍历键值对
  3. 使用集合去重与集合运算（交并差）
  4. 讲解浅拷贝（list.copy()/copy.copy）与深拷贝（copy.deepcopy）
• 练习：词频统计（输入一段文本，输出Top N词）
• 检查点：能根据需求选对容器类型

模块6 函数与作用域

• 目标：编写可复用函数，理解参数与返回值
• 核心点：def、默认参数、可变参数、文档字符串、LEGB作用域
• 步骤：
  1. 定义函数并写docstring（说明参数/返回/例子）
  2. 使用默认参数与关键字参数
  3. 演示 *args/**kwargs 收集参数
  4. 说明可变默认参数的陷阱与正确写法（默认值用None）
• 练习：重构“词频统计”为函数库并编写简单单元测试
• 检查点：函数单一职责，命名清晰

模块7 推导式与迭代器/生成器

• 目标：编写简洁高效的数据处理代码
• 核心点：列表/字典/集合推导式、生成器函数与yield、生成器表达式
• 步骤：
  1. 用推导式对列表筛选与映射
  2. 写生成器逐行读取大文件
  3. 对比生成器与列表的内存占用
• 练习：处理大型日志文件，只统计满足条件的行数
• 误区提示：避免过度嵌套推导式降低可读性

模块8 字符串处理与文件I/O

• 目标：进行文本处理与稳健的文件读写
• 核心点：常用字符串方法、with上下文、编码、路径处理
• 步骤：
  1. 使用 split/join/strip/replace 清洗文本
  2. 使用 with open(..., encoding="utf-8") 读写文件
  3. 用 pathlib 进行跨平台路径处理
• 练习：清洗CSV的一列并输出到新文件
• 检查点：文件操作使用 with；明确编码UTF-8

模块9 异常与健壮性

• 目标：处理运行时错误并保持程序稳定
• 核心点：try/except/else/finally、raise、自定义异常、输入校验
• 步骤：
  1. 捕获并分类处理常见异常（ValueError、FileNotFoundError）
  2. 使用 else/finally 实现成功与收尾逻辑
  3. 定义并抛出自定义异常以表达业务错误
• 练习：为前面“计算器”补齐异常处理与错误提示
• 检查点：只捕获必要范围的异常，避免裸 except

模块10 模块化、包与标准库

• 目标：组织代码并复用生态
• 核心点：import/模块搜索路径、init.py、venv、pip、常用标准库
• 步骤：
  1. 按功能拆分成模块与包，验证相对/绝对导入
  2. 使用标准库：math、random、datetime、json、csv、collections、itertools
  3. 用 requirements.txt 固化依赖；演示 pip install -r requirements.txt
• 练习：读取JSON配置驱动程序行为
• 误区提示：避免循环导入；将可执行逻辑放入 if name == "main":

模块11 面向对象编程基础

• 目标：能定义类并创建对象以建模问题
• 核心点：class、实例/类属性、方法、init、repr、简单继承
• 步骤：
  1. 定义类并封装状态与行为
  2. 实现 repr 便于调试
  3. 演示继承与方法重写，何时选择组合优于继承
• 练习：为“词频统计”设计一个 TextAnalyzer 类，提供统计与导出方法
• 检查点：避免滥用继承；保证类的单一职责

模块12 测试、调试与代码质量

• 目标：建立可靠开发习惯
• 核心点：assert、unittest/pytest、pdb/VS Code 调试、logging、类型注解、PEP 8、格式化与静态检查
• 步骤：
  1. 为关键函数编写单元测试与边界用例
  2. 使用断点与变量监视调试一段代码
  3. 引入 logging 代替随意的 print
  4. 添加类型注解与基础检查（mypy 可选）
  5. 使用 black/ruff 或 flake8 统一风格与检查
• 练习：为项目核心模块达到最小测试覆盖（如 >70%）
• 检查点：测试可重复、独立；日志分级合理

模块13 综合实战项目：命令行小工具

• 目标：整合所学，完成功能完整、可维护的CLI应用
• 项目建议：
  • 选题示例：待办清单管理器、日志过滤器、文本清洗器、简单数据汇总器
• 交付要求（按步骤执行）：
  1. 规划需求与用例（用README列出目标、使用示例）
  2. 设计模块结构与数据模型
  3. 实现核心功能与文件持久化（JSON/CSV）
  4. 加入参数解析（argparse），提供--help
  5. 添加异常处理、日志与配置
  6. 编写测试与持续集成脚本（可选GitHub Actions）
  7. 使用 black/ruff 格式化并通过静态检查
  8. 打包发布到私有仓库或生成可执行脚本
• 评分标准：
  • 功能完整性与正确性（40%）
  • 代码质量与文档（30%）
  • 测试与健壮性（20%）
  • 工程化细节（10%）

五、里程碑与阶段测评

• 里程碑1（完成模块1–4）：编写一个数字猜谜或计分脚本；检查控制流正确性
• 里程碑2（完成模块5–8）：完成“词频统计”工具，支持文件输入与Top N输出
• 里程碑3（完成模块9–12）：为“词频统计”添加异常处理、日志、测试与类型注解
• 期末项目（模块13）：交付完整CLI工具

六、教学与学习建议

• 采用“示例驱动+小步快跑”：每节课引入1–2个可运行示例，立即改写与扩展
• 强化“及时反馈”：提交代码通过自动化测试与风格检查获取即时结果
• 管理认知负荷：一次只引入一个新概念，配对最小可行练习
• 促进迁移：每个新模块要求复用前面函数/模块以构建更大程序
• 鼓励读文档：在练习中附上标准库文档入口，引导查阅与自学

七、资源清单（建议指引）

• 官方文档：docs.python.org（鼓励查询对应章节：教程、标准库、PEP 8）
• 工具：VS Code + Python 扩展、black、ruff、pytest、mypy（可按需启用）
• 数据集与练习：使用公开文本或自制小CSV/JSON，确保权责与隐私合规

实施提示

• 在每个模块开始前，写下本节“完成后你将能够……”的3–5条可检验目标
• 在每个练习后，提供“自测用例与期望输出”，让学习者本地验证
• 在作业说明中明确提交格式、截止时间、评分细则与可用资源
• 对常见错误准备“错误案例+修复步骤”，让学习者对照排查

按以上大纲逐步实施，确保每节课都有明确目标、可操作步骤与可度量产出，最终引导学习者从零实现一个可用、可维护的Python小项目。