Python Roadmap 2026 — Zero to Data Science

Phase 1: Python Basics (Weeks 1–3)

Start with the fundamentals. Don't rush — these concepts build everything else.

• Variables, data types, operators
• Strings and string methods
• Lists, tuples, sets, dictionaries
• Conditionals (if/elif/else)
• Loops (for, while)
• Functions and scope
• File handling (read/write CSV, TXT)
• Error handling (try/except)

Project: Build a calculator, a number guessing game, and a file organizer script.

Phase 2: OOP & Intermediate (Weeks 4–5)

• Classes and objects
• Methods, inheritance, polymorphism
• Decorators and generators
• List comprehensions
• Lambda functions
• Modules and packages

Project: Build a contact management system using OOP with file storage.

Phase 3: Data Analysis with Python (Weeks 6–8)

• NumPy — arrays, operations, broadcasting
• Pandas — DataFrames, filtering, groupby, merge
• Data cleaning and preprocessing
• Matplotlib and Seaborn visualization
• Exploratory Data Analysis (EDA)

Project: Analyze a real-world dataset (sales, weather, or cricket stats) with visualizations.

Phase 4: Web Scraping & APIs (Weeks 9–10)

• BeautifulSoup for HTML parsing
• Selenium for dynamic websites
• Requests library for HTTP
• REST API consumption
• Data storage (CSV, JSON, SQLite)

Project: Build a web scraper that collects job listings from multiple sites and saves them to a database.

Phase 5: Automation & Productivity (Weeks 11–12)

• Automating Excel with openpyxl
• Email automation with smtplib
• PDF generation and reading
• Scheduling tasks with schedule/cron
• Building CLI tools with argparse

Project: Build an automated report generator that pulls data, creates charts, and emails a PDF.

Phase 6: Python for Data Science (Weeks 13–16)

• Statistics with Python (scipy, statsmodels)
• Machine Learning basics (scikit-learn)
• Data pipelines and workflows
• Git and version control
• Portfolio building

Python Career Paths