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Python Data Science with Pandas: Master 12 Advanced Projects

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Python Data Science with Pandas: Master 12 Advanced Projects
Python Data Science with Pandas

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

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Udemy Coupon Code for Python Data Science with Pandas: Master 12 Advanced Projects Course. Work with Pandas, SQL Databases, JSON, Web APIs & more to master your real-world Machine Learning & Finance Projects

Created by Alexander Hagmann | 17 hours on-demand video course

Python Data Science with Pandas Course Overview

Python Data Science with Pandas: Master 12 Advanced Projects

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

Python Data Science: Unsupervised Machine Learning

Python Data Science: Unsupervised Machine Learning

Learn Python for data science & machine learning, and build unsupervised learning models w/ a top Python instructor!

This is Python Data Science: Unsupervised Machine Learning hands-on, project-based course designed to help you master the foundations for unsupervised learning in Python. We’ll start by reviewing the data science workflow, discussing the techniques & applications of unsupervised learning, and walking through the data prep steps required for modeling. You’ll learn how to set the correct row granularity for modeling, apply feature engineering techniques, select relevant features, and scale your data using normalization and standardization.

Python Data Science: Data Prep & EDA with Python

Python Data Science: Data Prep & EDA with Python

Learn Python + Pandas for data cleaning, profiling & EDA, and prep data for machine learning & data science with Python

This is Python Data Science: Data Prep & EDA with Python hands-on, project-based course designed to help you master the core building blocks of Python for data science. We’ll start by introducing the fields of data science and machine learning, discussing the difference between supervised and unsupervised learning, and reviewing the data science workflow we’ll be using throughout the course.

From there we’ll do a deep dive into the data prep & EDA steps of the workflow. You’ll learn how to scope a data science project, use Pandas to gather data from multiple sources and handle common data cleaning issues, and perform exploratory data analysis using techniques like filtering, grouping, and visualizing data.

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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Taught by Alexander Hagmann

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