Python Data Science With Pandas: Master 12 Advanced Projects

Python Data Science With Pandas: Master 12 Advanced Projects
Last updated 10/2024
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

Work with Pandas, SQL Databases, JSON, Web APIs and more to master your real-world Machine Learning and Finance 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
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

Requirements
You should be familiar with Python (Standard Library, Numpy, Matplotlib)
You should have worked with Pandas before (at least you should know the basics)
A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
An internet connection capable of streaming HD videos.
Some high school level math skills would be great (not mandatory, but it helps)

Description
Who this course is for:
Everyone who really want to master large, messy and unclean Datasets.,Everyone who want to improve skills from "I can write some Pandas Code" to "I can master my real-word Data Projects with Pandas",Data Scientists,Machine Learning Professionals,Finance & Investment Professionals,Researchers

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