Python Data Science with Pandas: Master 12 Advanced Projects
Python Data Science with Pandas: Master 12 Advanced Projects
Work with Pandas, SQL Databases, JSON, Web APIs & more to master your real-world Machine Learning & Finance Projects
Created by Alexander Hagmann | 15.5 hours on-demand video course
Welcome to the first advanced and project-based Pandas Data Science Course! This Course starts where many other courses end: You can write some Pandas code but you are still struggling with real-world Projects because Real-World Data is typically not provided in a single or a few text/excel files -> more advanced Data Importing Techniques are required. Real-World Data is large, unstructured, nested and unclean -> more advanced Data Manipulation and Data Analysis/Visualization Techniques are required. many easy-to-use Pandas methods work best with relatively small and clean Datasets -> real-world Datasets require more General Code (incorporating other Libraries/Modules) No matter if you need excellent Pandas skills for Data Analysis, Machine Learning or Finance purposes, this is the right Course for you to get your skills to Expert Level! Master your real-world Projects!
What you’ll learn
- Advanced Real-World Data Workflows with Pandas you won´t find in any other Course.
- Working with Pandas and SQL-Databases in parallel (getting the best out of two worlds)
- Working with APIs, JSON and Pandas to import large Datasets from the Web
- Bringing Pandas to its Limits (and beyond…)
- Machine Learning Application: Predicting Real Estate Prices
- Finance Applications: Backtesting & Forward Testing Investment Strategies + Index Tracking
- Feature Engineering, Standardization, Dummy Variables and Sampling with Pandas
- Working with large Datasets (millions of rows/columns)
- Working with completely messy/unclean Datasets (the standard case in real-world)
- Handling stringified and nested JSON Data with Pandas
- Loading Data from Databases (SQL) into Pandas and vice versa
- Loading JSON Data into Pandas and vice versa
- Web-Scraping with Pandas
- Cleaning large & messy Datasets (millions of rows/columns)
- Working with APIs and Python Wrapper Packages to import large Datasets from the Web
- Explanatory Data Analysis with large real-world Datasets
- Advanced Visualizations with Matplotlib and Seaborn
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