📊 Master Data Analysis with Python: Full Beginner Course (Numpy, Pandas, Matplotlib, Seaborn) 🚀

Unlock the power of Data Analysis with Python in this comprehensive full-course tutorial for beginners! Whether you're stepping into data science for the first time or brushing up on the fundamentals, this course covers everything you need to know.

🔍 What You’ll Learn:

🛠 Numpy: Master numerical computations and matrix operations.
📂 Pandas: Work with datasets, clean data, and manage DataFrames like a pro.
📊 Matplotlib: Create stunning visualizations to tell compelling stories.
🌟 Seaborn: Enhance your plots with beautiful and customizable statistical graphics.
💡 Perfect For:
✔️ Beginners in Python and data science.
✔️ Aspiring data analysts and scientists.
✔️ Students preparing for data-driven projects.
✔️ Anyone looking to break into the world of analytics.

👉 By the end of this course, you’ll be equipped to perform real-world data analysis tasks and create insights that matter!

Don’t forget to like, comment, and subscribe for more data-driven content. Let’s dive into the world of Python and analytics together! 🐍📈

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This video includes a segment by Santiago Basulto Tutorials under the 'fair use' policy for educational purposes.

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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)
If your Python or coding skills are rusty, check out this section for a quick recap of Python main features and control flow structures.