Udemy Coupon - The Complete Pandas Bootcamp 2021: Data Science with Python
Pandas fully explained | NEW Version 1.0 | 150+ Exercises | Must-have skills for Machine Learning & Finance | +Seaborn
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- Created by Alexander Hagmann
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+++++ UPDATE: Pandas Version 1.0 is finally here! This is the first course that covers Pandas 1.0. It gives optimal guidance on how to transition from versions 0.X to 1.0! +++++
Welcome to the web´s most comprehensive Pandas Bootcamp with 30+ hours of structured video content and 150+ exercises!
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 is structured in four parts, beginning from Zero with all the Pandas Basics (PART 1). PART 2 is the heart of this course and shows the complete data workflow: Importing, Cleaning, Merging, Aggregating, Grouping and Preparing Data for Statistics & Machine Learning. Finally, you can test your new skills in a Comprehensive Project Challenge that is frequently used in Data Science job applications / assessment centres (PART 3). In the last part of this course (PART 4), you will learn how to import, handle and work with (financial) Time Series Data.
Why to take a course on Pandas?
The world is getting more and more Data-Driven. New professions like Data Scientist are gaining ground with $100k+ salaries. It´s time to switch from Soap Box 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 and Machine Learning. And 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 high proficiency in Pandas! Data Scientist typically spend up to 85% of their time with manipulating Data in Pandas.
A frequently asked question of Python Beginners is: "Do I need to become a Python Coding Expert before I can start working with Pandas?"
The clear answer is: "No! Do you need to become a Microsoft Software Developer before you can use 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 4 hours of Python Basics. This Python Intro is tailor-made and more than sufficient for Data Science purposes!
As a Summary, if you primarily want to use Python for Data Science or as a replacement for Excel, then this course is a perfect match!
Why to 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 1.0. Pandas Library has experienced massive improvements in the last couple of months. From my own experience, working with and relying on outdated code can be painful.
- It can serve as a Pandas Encyclopedia covering all relevant Methods, Attributes and workflows for real life 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 life Data Workflow A-Z, starting from 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 and Data Presentation.
-It explains Pandas Coding on real Data and real life Problems. No Toy Data! This is the best way to learn and understand Pandas.
-It gives you plenty of opportunities to practise and code on your own. Learning by doing. In the exercises, you can select your individual 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.
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