Phase 2: Data Analytics & Workflows
Days 21–39 | 19 Lessons
Learn to manipulate, visualize, and analyze data using industry-standard libraries like NumPy, Pandas, and Matplotlib. Connect to databases and APIs.
Lessons in This Phase
| Day | Title | Description |
|---|---|---|
| 21 | 📘 Day 21: Virtual Environments - Professional Project Management | As you work on more complex projects, you'll find they have different requirements. Project A might |
| 22 | 📘 Day 22: NumPy - The Foundation of Numerical Computing | While Python lists are flexible, they aren't efficient for large-scale numerical calculations. For |
| 23 | 📘 Day 23: Pandas - Your Data Analysis Superpower | If NumPy is the foundation, Pandas is the tool you will use every single day for practical data |
| 24 | 📘 Day 24: Advanced Pandas - Working with Real Data | You'll rarely create data from scratch. The most common workflow is to load data from external |
| 25 | 📘 Day 25: Data Cleaning - The Most Important Skill in Analytics | It's often said that data analysts spend about 80% of their time cleaning and preparing data. Messy, |
| 26 | 📘 Day 26: Practical Statistics for Business Analysis | On Day 26 you expand beyond data wrangling and apply core statistical tools to business datasets. |
| 27 | 📘 Day 27: Data Visualization - Communicating Insights | Visualising key business metrics makes it easier to communicate findings and uncover patterns. Day |
| 28 | 📘 Day 28: Advanced Visualization & Customization | Creating a basic chart is just the first step. To effectively communicate your story, you need to |
| 29 | 📘 Day 29: Interactive Visualization with Plotly | Static charts are good for reports, but in the modern era of business intelligence, users expect to |
| 30 | 📘 Day 30: Web Scraping - Extracting Data from the Web | Sometimes, the data you need isn't available in a clean CSV file or through an API. It's simply |
| 31 | 📘 Day 31: Working with Databases in Python | While CSV files are great for smaller datasets, most real-world business data is stored in |
| 32 | 📘 Day 32: Connecting to Other Databases (MySQL & MongoDB) | In the previous lesson, we used sqlite3, which is fantastic for learning and small projects |
| 33 | 📘 Day 33: Accessing Web APIs with requests | This lesson introduces a lightweight wrapper around the |
| 34 | 📘 Day 34: Building a Simple API with Flask | Consuming data from APIs is a core skill. But what if you need to provide data from your analysis to |
| 35 | 🌐 Day 35: Flask Web Framework | Welcome to Day 35! This lesson contains a small Flask project that analyses submitted text and |
| 36 | 📊 Day 36 – Capstone Case Study | Day 36 ties together the full analytics workflow. You will load the case_study_sales.csv dataset, |
| 37 | 🎉 Day 37: Conclusion & Your Journey Forward 🎉 | You did it! You have successfully completed the core analytics track of the 50-Day Python for |
| 38 | Day 38: Math Foundations - Linear Algebra | Linear algebra underpins much of machine learning. This lesson revisits the building blocks—vectors, |
| 39 | Day 39: Math Foundations - Calculus | Calculus powers the optimisation routines that train machine learning models. Derivatives, |