This repository serves as a foundational and bilingual curriculum template for mastering core Data Wrangling and Data Analysis techniques using the Pandas library in Python. It provides organized documentation and hands-on Google Colab exercises, following a structured Data Mining methodology.
🌟 Unique Feature: To support deeper technical understanding, the repository includes Turkish-English technical study notes created during the Masterschool curriculum, designed to clarify complex concepts and terminology in both languages.
The core curriculum documentation is organized into sequential Markdown files (M.D. files). These files correspond to the main phases of a data project, from initial setup to final cleaning.
| File Path | Description |
|---|---|
| 🐼 01. Pandas Foundations.md | Introduction to Pandas: Fundamental concepts, including the Series and DataFrame structures, essential indexing (.loc[], .iloc[]), and basic aggregation. |
| 🔗 02. Data Wrangling.md | Data Wrangling Overview: Defines the process, its importance within the CRISP-DM framework, and the crucial steps of Assessment and Cleaning. |
| 🤝 03. Data Integration.md | Combining Datasets: Techniques for joining and merging data, including Concatenation (pd.concat()) for stacking, and SQL-style Joins (pd.merge(): Inner, Left, Right, Outer). |
| 🔎 04. Data Assessment.md | Identifying Data Quality Issues: Methods for checking data types, reviewing dimensionality (.shape), and detecting early signs of errors (nulls, duplicates, inconsistencies). |
| 🧼 05. Data Cleaning.md | Data Transformation and Correction: Comprehensive techniques for handling duplicates, managing missing values (Imputation and Deletion), and performing complex String Manipulation (e.g., .split(), .replace()). |
| 🔢 06. Aggregating information and applying.md | Data Summarization: Methods for calculating statistics across the dataset, focusing on aggregation functions (.sum(), .mean()) and preparing for grouping (.groupby()). |
To get the most out of this material, you should have:
- A basic understanding of Python syntax.
- Access to a Google account for using the Colab notebooks.