- Mulham Fetna | Technical Portfolio/
- Welcome to Your Data Science & AI Journey/
- Python from Zero to Data Engineering Mastery/
Python from Zero to Data Engineering Mastery
Table of Contents
Python Mastery Bootcamp 2026 — Course overview#
Neurobotics Academy · Lead instructor: Eng. Mulham Fetna
Mission#
Take beginners to production-style Python: fundamentals, data work, databases, dashboards, OOP, Git, scraping, APIs/async, and a graduation capstone (“Data Intelligence Platform” pattern).
Level: Beginner → job-ready portfolio (Python + data + shipping).
Sessions: 24 (see SCHEDULE for exact titles and artifacts).
Pacing (per cohort)#
- Option A: 2 sessions/week → ~12 weeks (~3 months)
- Option B: 3 sessions/week → ~8 weeks (~2 months)
Session curriculum#
Phases match SCHEDULE.
Phase 1 — Foundations (sessions 1–6)#
- Introduction and Python Setup
- Python Data Types, Variables, and Basic Arithmetic
- Conditional Statements & Math Operations
- Loops + Data Structures + JSON CRUD
- Functions + Nested Loops + Time Complexity
- Lists + Modular Functions + Package Intro
Phase 2 — Data in Python (sessions 7–11)#
- Dictionaries + JSON Databases
- NumPy + Virtual Environments + Statistics
- Matplotlib + Linear Regression
- File I/O + OS Navigation + Error Handling
- Tkinter GUI + Student Attendance System
Phase 3 — Databases & analytics (sessions 12–17)#
- SQL + SQLite + Database Relationships
- Pandas DataFrames + Functional Programming
- Advanced Pandas + Export + Streamlit Dashboards
- Web UI - Markdown + Dash + Streamlit
- SQLite + Advanced Pandas Data Cleaning
- Data Pipeline QA, Logging & Reproducible Runs
Phase 4 — Software design & shipping (sessions 18–20)#
- Complete OOP Mastery
- Functional Programming Mastery
- Git & GitHub Version Control Mastery
Phase 5 — Integration & production (sessions 21–24)#
- Complete Web Scraping → Production Dashboard
- Complete REST APIs → Production Data Pipeline
- Complete Data Science Portfolio Project
- GRADUATION PROJECT — Complete Python Mastery Showcase
Technologies#
- Python: stdlib, venv, OOP, functional patterns,
logging, testing basics - Data: NumPy, Pandas, Matplotlib; SQLite; Streamlit / Dash
- Shipping: Git/GitHub,
requirements.txt, structured projects - Integration: requests, BeautifulSoup, asyncio / async APIs (as covered in sessions)
Graduation deliverable#
Capstone integrates scraping/APIs, Pandas processing, SQLite, Streamlit (or similar UI), tests, and GitHub-ready layout. Session 24 defines the full architecture checklist. Fetna.md`
License#
Designed for use within Neurobotics Academy. Rights reserved by Neurobotics Academy and the course developer unless otherwise agreed in writing.
Python Mastery Bootcamp 2026 — session schedule#
Single source of truth for order and titles.
| Session | Title | Primary stack |
|---|---|---|
| 1 | Introduction and Python Setup | Python, VS Code |
| 2 | Python Data Types, Variables, and Basic Arithmetic | Core Python |
| 3 | Conditional Statements & Math Operations | Control flow |
| 4 | Loops + Data Structures + JSON CRUD | Loops, JSON |
| 5 | Functions + Nested Loops + Time Complexity | Functions |
| 6 | Lists + Modular Functions + Package Intro | Lists, modules |
| 7 | Dictionaries + JSON Databases | dict, JSON IO |
| 8 | NumPy + Virtual Environments + Statistics | NumPy, venv |
| 9 | Matplotlib + Linear Regression | Matplotlib, sklearn intro |
| 10 | File I/O + OS Navigation + Error Handling | pathlib, exceptions |
| 11 | Tkinter GUI + Student Attendance System | Tkinter, JSON |
| 12 | SQL + SQLite + Database Relationships | SQL, SQLite |
| 13 | Pandas DataFrames + Functional Programming | Pandas, FP |
| 14 | Advanced Pandas + Export + Streamlit Dashboards | Pandas, Streamlit |
| 15 | Web UI - Markdown + Dash + Streamlit | Dash, Streamlit |
| 16 | SQLite + Advanced Pandas Data Cleaning | SQLite, Pandas ETL |
| 17 | Data Pipeline QA, Logging & Reproducible Runs | logging, validation |
| 18 | Complete OOP Mastery | OOP, dataclasses |
| 19 | Functional Programming Mastery | map/filter/reduce |
| 20 | Git & GitHub Version Control Mastery | Git |
| 21 | Complete Web Scraping → Production Dashboard | BeautifulSoup, requests |
| 22 | Complete REST APIs → Production Data Pipeline | requests, asyncio |
| 23 | Complete Data Science Portfolio Project | Capstone prep |
| 24 | GRADUATION PROJECT - Complete Python Mastery Showcase | Full-stack capstone |
Pacing (sper cohort)#
- 24 sessions total.
- Option A: 2 sessions/week → ~12 weeks (~3 months)
- Option B: 3 sessions/week → ~8 weeks (~2 months)
Phase grouping#
Aligned with the table above (not legacy overview docs).
- Foundations (1–6): setup through lists and modular code
- Data in Python (7–11): dicts/JSON, NumPy, visualization, files, GUI
- Databases & analytics (12–17): SQL/SQLite, Pandas, Streamlit/Dash, ETL-style SQLite+Pandas, pipeline QA
- Software design & shipping (18–20): OOP, functional style, Git/GitHub
- Integration & production (21–24): scraping, APIs/async, portfolio project, graduation platform
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