### 1. Course Architecture, Transformation Journey & High-Level Milestones * **Course Headline:** Python for Data Analysis: From Raw CSV to Executive Dashboard * **Primary Transformation:** Takes students from absolute zero programming knowledge to confidently building reproducible data analysis scripts in Python. * **Core Learning Philosophy:** Active coding over passive lectures—students write executable code within the first 15 minutes of Module 1. | Milestone | Target Week | Core Competency Unlocked | Student Deliverable | | :--- | :---: | :--- | :--- | | **Milestone 1** | Week 1–2 | Python fundamentals & Jupyter execution | First automated CSV transaction summary script | | **Milestone 2** | Week 3–4 | Data cleaning & wrangling with Pandas | Cleaned real-world e-commerce sales dataset | | **Milestone 3** | Week 5–6 | Exploratory data visualization & storytelling | Interactive portfolio dashboard on GitHub | --- ### 2. Module-by-Module Curriculum Syllabus & Lesson Breakdown #### Module 1: The Foundations of Code (Zero to First Script) * **Lesson 1.1:** Setting Up Your Cloud Environment (Google Colab with Zero Local Setup) * **Lesson 1.2:** Variables, Data Types & Avoiding Syntax Dread * **Lesson 1.3:** Lists, Dictionaries, and Parsing Real Business Data * **Hands-on Lab:** Build a script that calculates monthly utility expenses from a messy receipt list. #### Module 2: Ingesting & Taming Messy Data with Pandas * **Lesson 2.1:** Loading CSVs and SQL tables into Pandas DataFrames * **Lesson 2.2:** Filtering, Slicing, and Handling Missing/Null Values * **Lesson 2.3:** Groupby, Aggregations, and Multi-Level Indexing * **Hands-on Lab:** Triage a 10,000-row customer churn dataset and isolate high-risk customer accounts. #### Module 3: Visual Storytelling & Capstone Deployment * **Lesson 3.1:** Chart Selection Strategy: Matplotlib vs. Seaborn for Business Metrics * **Lesson 3.2:** Translating Technical Data into C-Suite Executive Summaries * **Lesson 3.3:** Packaging Your Project into a Clean GitHub Portfolio * **Hands-on Lab:** Publish a public GitHub analysis notebook dissecting global housing trends. --- ### 3. Practical Hands-On Project & Assessment Rubric * **Capstone Assignment:** The Real-World Customer Retention Audit * **Brief:** Ingest a raw, uncleaned transactional dataset of 25,000 orders. Identify missing values, calculate monthly customer retention rates, and produce three publication-grade charts explaining revenue leakage. | Evaluation Criterion | Beginning (1 Pt) | Proficient (3 Pts) | Exemplary (5 Pts) | | :--- | :--- | :--- | :--- | | **Code Cleanliness** | Unformatted code; missing comments. | Readable PEP8 code with basic docstrings. | Modular functions with defensive error handling. | | **Data Integrity** | Null values ignored or deleted blindly. | Missing values handled logically with explanations. | Outliers and null values validated via statistical logic. | | **Insight Clarity** | Generic charts with missing axis labels. | Clear visualizations with defined metrics. | Executive-ready charts with clear written takeaways. | --- ### 4. Learner Engagement, Accountability & Completion Optimization Plan * **The "First 15 Minutes" Win:** Replace local Python installation steps with browser-based Google Colab notebooks so students run code instantly without installation errors. * **Weekly Accountability Circles:** Pair students into 3-person peer accountability pods for weekly code check-ins. * **Automated Milestone Celebrations:** Trigger automated Slack bot celebrations and digital badges upon successful completion of weekly coding assignments. * **Red-Flag Intervention:** Automated alert triggers if a student misses a Sunday code submission, prompting a friendly 1-on-1 direct message from a teaching assistant within 24 hours.