Phase 1: Python Foundations
Days 1–20 | 20 Lessons
Master Python fundamentals from first principles. Build a solid foundation in syntax, data structures, and control flow essential for business analytics.
Lessons in This Phase
| Day | Title | Description |
|---|---|---|
| 1 | 📘 Day 1: Python for Business Analytics - First Steps | Welcome, future business leader! You're about to take your first step into a larger world of |
| 2 | 📘 Day 2: Storing and Analyzing Business Data | In Day 1, we performed basic calculations. Now, we'll learn how to store data in variables and |
| 3 | 📘 Day 3: Operators - The Tools for Business Calculation and Logic | An operator is a symbol that tells the computer to perform a specific mathematical or logical |
| 4 | 📘 Day 4: Working with Text Data - Strings | In business analytics, text data is everywhere—customer names, product reviews, addresses, and |
| 5 | 📘 Day 5: Managing Collections of Business Data with Lists | In business, you often work with collections of data: lists of customers, quarterly sales figures, |
| 6 | 📘 Day 6: Tuples - Storing Immutable Business Data | While lists are great for data that changes, sometimes you need to store data that shouldn't |
| 7 | 📘 Day 7: Sets - Managing Unique Business Data | We've seen lists for ordered data and tuples for immutable data. Now we'll learn about sets, |
| 8 | 📘 Day 8: Dictionaries - Structuring Complex Business Data | Real-world business data is structured. A customer has a name, an email, and a location. A product |
| 9 | 📘 Day 9: Conditionals - Implementing Business Logic | Business is full of rules and decisions. "If a customer spends over $100, they get a 10% discount." |
| 10 | 📘 Day 10: Loops - Automating Repetitive Business Tasks | What if you have a list of 10,000 sales transactions? You won't write code for each one. This is |
| 11 | 📘 Day 11: Functions - Creating Reusable Business Tools | As you perform more complex analysis, you'll write the same code repeatedly. Functions are |
| 12 | 📘 Day 12: List Comprehension - Elegant Data Manipulation | In data analysis, you constantly create new lists by transforming or filtering existing ones. While |
| 13 | 📘 Day 13: Higher-Order Functions & Lambda | A higher-order function is a function that takes another function as an argument or returns a |
| 14 | 📘 Day 14: Modules - Organizing Your Business Logic | As your projects grow, you need a way to organize your code. In Python, we do this with modules. |
| 15 | 📘 Day 15: Exception Handling - Building Robust Business Logic | In the real world, data is messy and operations can fail. A file might be missing, a user might |
| 16 | 📘 Day 16: File Handling for Business Analytics | A huge part of data analysis involves reading data from files and writing results to them. Whether |
| 17 | 📘 Day 17: Regular Expressions for Text Pattern Matching | Often, the text data you need to analyze isn't perfectly structured. You might need to find all the |
| 18 | 📘 Day 18: Classes and Objects - Modeling Business Concepts | So far, we've organized our code with functions. But what if you want to model a real-world concept, |
| 19 | 📘 Day 19: Working with Dates and Times | Time-series analysis is at the heart of business analytics. Whether you're tracking daily sales, |
| 20 | 📘 Day 20: Python Package Manager (pip) & Third-Party Libraries | The real power of Python for data analysis comes from its vast ecosystem of third-party libraries. |