The Complete Pandas Bootcamp 2021: Data Science with Python - Pandas fully explained | 150+ Exercises | Must-have skills for Machine Learning & Finance | + Scikit-Learn and Seaborn
- Created by Alexander Hagmann
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What you'll learn
- Bring your Data Handling & Data Analysis skills to an outstanding level.
- Learn and practice all relevant Pandas methods and workflows with Real-World Datasets
- Learn Pandas based on NEW Version 1.x (the days of versions 0.x are over)
- Import, clean, and merge messy Data and prepare Data for Machine Learning
- Master a complete Machine Learning Project A-Z with Pandas, Scikit-Learn, and Seaborn
- Analyze, visualize, and understand your Data with Pandas, Matplotlib, and Seaborn
- Practice and master your Pandas skills with Quizzes, 150+ Exercises, and Comprehensive Projects
- Import Financial/Stock Data from Web Sources and analyze them with Pandas
- Learn and master the most important Pandas workflows for Finance
- Learn how to best transition from Versions 0.x to new Version 1.x
- Learn the Basics of Pandas and Numpy Coding (Appendix)
- Learn and master important Statistical Concepts with scipy
######### UPDATE (November 2020) ###########
Added: Introduction to Machine Learning with Pandas and scikit-learn - incl. a comprehensive ML Project A-Z
Added: Another comprehensive Final Project (Explanatory Data Analysis) to test your skills
Updated to latest Pandas Version 1.1! This is the first course that covers Pandas 1.x. It gives optimal guidance on how to transition from version 0.x to version 1.x!
Welcome to the web´s most comprehensive Pandas Bootcamp with 34 hours of video content, 150+ exercises, and two large and comprehensive Final Projects that test your skills! This course has one goal: Bringing your data handling skills to the next level to build your career in Data Science, Machine Learning, Finance & co.
This course has five parts:
Pandas Basics - from Zero to Hero (Part 1).
The complete data workflow A-Z with Pandas: Importing, Cleaning, Merging, Aggregating, and Preparing Data for Machine Learning. (Part 2)
Two Comprehensive Project Challenges that are frequently used in Data Science job recruiting/assessment centers: Test your skills! (Part 3).
Application 1: Pandas for Finance, Investing and other Time Series Data (Part 4)
Application 2: Machine Learning with Pandas and scikit-learn (Part 5)
Why should you learn Pandas?
The world is getting more and more data-driven. Data Scientists are gaining ground with $100k+ salaries. It´s time to switch from soapbox cars (spreadsheet software like Excel) to High Tuned Racing Cars (Pandas)!
Python is a great platform/environment for Data Science with powerful Tools for Science, Statistics, Finance, and Machine Learning. The Pandas Library is the Heart of Python Data Science. Pandas enables you to import, clean, join/merge/concatenate, manipulate, and deeply understand your Data and finally prepare/process Data for further Statistical Analysis, Machine Learning, or Data Presentation. In reality, all of these tasks require a high proficiency in Pandas! Data Scientists typically spend up to 85% of their time manipulating Data in Pandas.
Can you start right now?
A frequently asked question of Python Beginners is: "Do I need to become an expert in Python coding before I can start working with Pandas?"
The clear answer is: "No! Do you need to become a Microsoft Software Developer before you can start with Excel? Probably not!"
You require some Python Basics like data types, simple operations/operators, lists and numpy arrays. In the Appendix of this course, you can find a Python crash course. This Python Introduction is tailor-made and sufficient for Data Science purposes!
In addition, this course covers fundamental statistical concepts (coding with scipy).
As a Summary, if you primarily want to use Python for Data Science or as a replacement for Excel, this course is a perfect match!
Why should you take this Course?
It is the most relevant and comprehensive course on Pandas.
It is the most up-to-date course and the first that covers Pandas Version 1.x. The Pandas Library has experienced massive improvements in the last couple of months. Working with and relying on outdated code can be painful.
Pandas isn´t an isolated tool. It is used together with other Libraries: Matplotlib and Seaborn for Data Visualization | Numpy, Scipy and Scikit-Learn for Machine Learning, scientific and statistical computing. This course covers all these Libraries.
In real-world projects, coding and the business side of things are equally important. This is probably the only Pandas course that teaches both: in-depth Pandas Coding and Big-Picture Thinking.
It serves as a Pandas Encyclopedia covering all relevant methods, attributes, and workflows for real-world projects. If you have problems with any method or workflow, you will most likely get help and find a solution in this course.
It shows and explains the full real-world Data Workflow A-Z: Starting with importing messy data, cleaning data, merging and concatenating data, grouping and aggregating data, Explanatory Data Analysis through to preparing and processing data for Statistics, Machine Learning, Finance, and Data Presentation.
It explains Pandas Coding on real Data and real-world Problems. No toy data! This is the best way to learn and understand Pandas.
It gives you plenty of opportunities to practice and code on your own. Learning by doing. In the exercises, you can select the level of difficulty with optional hints and guidance/instruction.
Pandas is a very powerful tool. But it also has pitfalls that can lead to unintended and undiscovered errors in your data. This course also focuses on commonly made mistakes and errors and teaches you, what you should not do.
Guaranteed Satisfaction: Otherwise, get your money back with 30-Days-Money-Back-Guarantee.
I am looking forward to seeing you in the course!
Who this course is for:
Everyone who want to step into Data Science. Pandas is Key to everything.
Data Scientists who want to improve their Data Handling/Manipulation skills.
Everyone who want to switch Data Projects from Excel to more powerful tools (e.g. in Research/Science)
Investment/Finance Professionals who reached the limits of Excel.
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