| Time | Topic | Step-by-Step Plan |
|---|---|---|
| 09:00 – 09:15 | Welcome & Intro | - Welcome and goals - Salesforce context: "Why intermediate Python is critical in automation, integrations, and backend processing." |
| 09:15 – 10:45 | Advanced Data Types ( namedtuple, defaultdict, deque, OrderedDict) |
1. Explain problem: handling CRM-like customer data. 2. Live code: customer records with namedtuple.3. defaultdict for grouping related data (like accounts, contacts).4. Exercise: Build a simple contact management structure. 5. Q&A. |
| 10:45 – 11:00 | Break | |
| 11:00 – 12:30 | Functional Programming ( map, filter, reduce, lambda, operator) |
1. Why FP? Data transformations. 2. Live code: filter inactive leads, map emails, reduce revenue totals. 3. Exercise: Process a list of opportunities (filter closed, sum totals). 4. Demo: Decorators – add logging to functions. 5. Exercise: Decorator that tracks execution time. |
| 12:30 – 13:30 | Lunch | |
| 13:30 – 15:00 | Generators and Iterators | 1. Show large datasets (Salesforce reports, logs). 2. Live code: generator to stream records. 3. itertools demo: chaining daily reports.4. Exercise: Build a generator that processes pages of API results. 5. Connect to next: use generator inside a context manager. |
| 15:00 – 15:15 | Break | |
| 15:15 – 16:30 | Context Managers | 1. Why? Resource handling (APIs, files). 2. Live code: mock API session with with statement.3. Exercise: Context manager to simulate opening/closing Salesforce session. 4. Use context manager wrapping generator from previous session. |
| 16:30 – 17:00 | Intro to Testing | 1. Why tests matter in Salesforce ecosystem (deployments, releases). 2. Demo: basic test for data processing function. 3. Small exercise: write a test for the generator from earlier. |
| 17:00 | Wrap-up | Recap building blocks and link to Day 2: "We'll fully test and log this!" |
| Time | Topic | Step-by-Step Plan |
|---|---|---|
| 09:00 – 09:15 | Recap Day 1 | Q&A, recap how components connect. |
| 09:15 – 10:30 | Testing (continued) | 1. Test suites and coverage. 2. Live code: refactor yesterday's tests. 3. Exercise: Build full test coverage around data processing functions. 4. Tie-in: logging failures. |
| 10:30 – 10:45 | Break | |
| 10:45 – 11:45 | Logging | 1. Why logs matter (SF integrations, error tracking). 2. Live code: logging in processing pipeline. 3. Exercise: add logs to existing decorators, generators. 4. Use different log levels (INFO, ERROR). |
| 11:45 – 12:30 | NumPy | 1. Why math? Stats on Salesforce reports (revenue, lead scoring). 2. Demo: array of monthly revenue. 3. Exercise: calculate average deal size, growth rates. |
| 12:30 – 13:30 | Lunch | |
| 13:30 – 14:30 | Pandas | 1. Load CSV of mock Salesforce leads. 2. Group by industry, sum revenues. 3. Exercise: clean dataset (missing values), aggregate by region. |
| 14:30 – 15:45 | Scraping (Full Session) | 1. Show Salesforce Help API / public data pages (mock). 2. Live code: scrape table from HTML. 3. Exercise: scrape JSON from public API and push into Pandas. 4. Build pipeline: Scrape → Clean (Pandas) → Log steps. |
| 15:45 – 16:00 | Break | |
| 16:00 – 17:00 | Day task | Build a mini ETL pipeline: Scrape -> Clean -> Log -> Test. |
| 17:00 | Wrap-up | Tease Day 3: "Now we'll scale and serve this data!" |
| Time | Topic | Step-by-Step Plan |
|---|---|---|
| 09:00 – 09:15 | Recap Day 2 | Quick demo of best ETL solution from yesterday. |
| 09:15 – 10:30 | Multiprocessing and Threading | 1. Why? Salesforce batch jobs, parallel API calls. 2. Live code: threaded scraper. 3. Exercise: scrape multiple pages concurrently. 4. Discuss shared state (rate limits, etc.). |
| 10:30 – 10:45 | Break | |
| 10:45 – 12:00 | Networking | 1. Socket basics. 2. Live code: simple server to accept data (simulate Salesforce data hook). 3. Exercise: build a client to push data. 4. Optional: JSON-REST API calls to public data. |
| 12:00 – 13:00 | Lunch | |
| 13:00 – 14:30 | Django Intro | 1. Why web? Internal dashboards for Salesforce data. 2. Live demo: Django project scaffold. 3. Show how to build a view to display cleaned Pandas data. 4. Exercise: add a route and view to render dummy data. |
| 14:30 – 15:45 | Final Project | Group exercise: 1. Scrape some data. 2. Clean and process. 3. Store in Pandas. 4. Serve via Django. 5. Log all steps and write minimal tests. |
| 15:45 – 16:00 | Break | |
| 16:00 – 17:00 | Presentations + Wrap-up | - Groups show final project. - Final Q&A. - Feedback and next steps. |