Welcome to the first advanced and project-oriented Pandas data science course (Python Data Science with Pandas: Master 12 Advanced Projects)! This course starts where many other courses end: you can write pandas code but still struggle with real-world projects because 1. Real-world data is usually in one or more Text/Excel files not provided -> more advanced data entry techniques are required.
2- Real-world data is large, unstructured, nested, and messy -> more advanced techniques for data manipulation and data analysis/visualization are required. 3- Many easy-to-use Pandas methods work best with relatively small and clean datasets -> real-world datasets require more generic code (including other libraries/modules)
Whether you need excellent pandas skills for data analysis, machine learning, or finance purposes, this is the course for you to take your skills to the expert level! Master your real-world projects! I’m Alexander Hagman, finance specialist and data scientist (over 7 years of industry experience) and best-selling trainer for Pandas, Data Science and Finance (Finance) with Python. I look forward to meeting you in this course!
What you will learn in Python Data Science with Pandas: Master 12 Advanced Projects
- Application of Machine Learning: Real Estate Price Prediction
- Doing web-scraping with the Pandas library
- Advanced visualizations with Matplotlib and Seaborn
- Cleaning large and messy datasets (millions of rows/columns)
- Working with large data sets (millions of rows/columns)
This course is suitable for people who
- Everyone who really wants to master big, messy, uncleaned data sets.
- Everyone who wants to improve their skills from “I can write pandas code” to “I can master my real data projects with pandas”.
- Data scientists
- Machine learning experts
- Finance and investment specialists
Lecturer: Alexander Hagmann
Training level: advanced
Number of courses: 195
Training duration: 15 hours and 35 minutes
Python Data Science Headlines of the course on 2022-9
Python Data Science prerequisites
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