Learn Data Analysis with Python in 2025 – Master Pandas, NumPy & Visualization Tools

Start your journey into data science and data analysis with this beginner-friendly full course using Python! 🚀 This comprehensive 2025 tutorial teaches you how to manipulate, analyze, and visualize data using essential Python libraries like NumPy, Pandas, Matplotlib, and Seaborn.

💻 Course created by Santiago Basulto from DataWars

🔍 What you'll learn in this course:
✅ NumPy for efficient numerical computation
✅ Pandas for data manipulation and cleaning
✅ Matplotlib & Seaborn for beautiful data visualization
✅ Exploratory Data Analysis (EDA) techniques
✅ Real-world data projects and datasets
✅ Best practices for Python in data workflows

Perfect for beginners, this course helps you build a solid foundation in Python-based data analysis — a skill in high demand in today's job market.

🎯 By the end, you’ll be able to handle real datasets, generate insights, and visualize your results like a pro.

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⭐ Course Contents ⭐
⌨ Part 1: Introduction
What is Data Analysis, why Python?, what other options are there? what's the cycle of a Data Analysis project? What's the difference between Data Analysis and Data Science?

⌨ Part 2: Real Life Example of a Python/Pandas Data Analysis project (00:11:11)
A demonstration of a real life data analysis project using Python, Pandas, SQL and Seaborn. Don't worry, we'll dig deeper in the following sections

⌨ Part 3: Jupyter Notebooks Tutorial (00:30:50)
A step by step tutorial to learn how to use Juptyer Notebooks
🔗 Twitter Cheat Sheet: / 1122176794696847361

⌨ Part 4: Intro to NumPy (01:04:58)
Learn why NumPy was such an important library for the data-processing world in Python. Learn about low level details of computations and memory storage, and why tools like Excel will always be limited when processing large volumes of data.

⌨ Part 5: Intro to Pandas (01:57:08)
Pandas is arguably the most important library for Data Processing in the Python world. Learn how it works and how its main data structure, the Data Frame, compares to other tools like spreadsheets or DFs used for Big Data

⌨ Part 6: Data Cleaning (02:47:18)
Learn the different types of issues that we'll face with our data: null values, invalid values, statistical outliers, etc, and how to clean them.

⌨ Part 7: Reading Data from other sources (03:25:15)

⌨ Part 8: Python Recap (03:55:19)