The Complete Pandas Bootcamp 2024: Data Science with Python from Udemy

(Latest course update and full review in November 2023. Now with ChatGPT for Pandas and more than 20 Udemy Online Coding Exercises - NEW Feature. )

Welcome to the web´s most comprehensive Pandas Bootcamp. This is the only Pandas course you´ll ever need:

most comprehensive course with 36+ hours of video content
new AI features like Pandas Coding and Advanced Data Analysis with ChatGPT
150+ Coding Exercises (Online and Offline Exercises)
Practical Case Studies for Data Scientists and Finance Professionals
Fully updated to Pandas 2.1

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. It 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).

In 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 2.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.
ChatGPT for Pandas Coding and advanced Data Analytics included.
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 a 30-Days-Money-Back-Guarantee.

I am looking forward to seeing you in the course.

What's inside

Learning objectives

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 2.x
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 the basics of pandas and numpy coding (appendix)
Learn and master important statistical concepts with scipy
Show more
Show less

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 2.x
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 the basics of pandas and numpy coding (appendix)
Learn and master important statistical concepts with scipy
Show more
Show less

Syllabus

Getting Started

Overview / Student FAQ

Tips: How to get the most out of this course

Did you know that...?

More FAQ / Important Information

Installation of Anaconda

Opening a Jupyter Notebook

How to use Jupyter Notebooks

Downloads (Get all Course Materials here!) **UPD Nov 23**

---- PART 1: PANDAS FROM ZERO TO HERO (BUILDING BLOCKS) ----

Intro to Tabular Data / Pandas

Download Course Materials Part 1 (Reminder)

**NEW** Pandas Coding with your personal assistant - ChatGPT

Introduction

Coding assistance for Pandas Coding using GPT 3.5 (free)

Pandas Data Analysis using GPT 4 (Plus Subscription)

Pandas Basics (DataFrame Basics I)

Create your very first Pandas DataFrame (from csv)

Loading a CSV-file into Pandas

How to read CSV-files from other Locations

Pandas Display Options and the methods head() & tail()

First Data Inspection

Summary Statistics

Built-in Functions, Attributes and Methods with Pandas

Make it easy: TAB Completion and Tooltip

First Steps

Explore your own Dataset: Jupyter Coding Exercise 1 (Intro)

Explore your own Dataset: Jupyter Coding Exercise 1 (Solution)

Selecting Columns

Selecting one Column with the "dot notation"

Zero-based Indexing and Negative Indexing

Selecting Rows with iloc (position-based indexing)

Slicing Rows and Columns with iloc (position-based indexing)

Position-based Indexing Cheat Sheets

Position-based Indexing 1

Position-based Indexing 2

Selecting Rows with loc (label-based indexing)

Slicing Rows and Columns with loc (label-based indexing)

Label-based Indexing Cheat Sheets

Label-based Indexing 1

Label-based Indexing 2

Indexing and Slicing with reindex()

Summary, Best Practices and Outlook

Indexing and Slicing

Jupyter Coding Exercise 2 - Intro

Jupyter Coding Exercise 2 - Solution

**NEW** Coding Exercises with ChatGPT

Advanced Indexing and Slicing (optional)

Excursus: How to avoid and debug Coding Errors (incl. ChatGPT)

Test your debugging skills!

Major reasons for Coding Errors

The most commonly made Errors at a glance

Omitting cells, changing the sequence and more

IndexErrors

Indentation Errors

Misuse of function names and keywords

TypeErrors and ValueErrors

**NEW** Debugging Pandas Errors with ChatGPT

Getting help on StackOverflow.com

How to traceback more complex Errors

Problems with the Python Installation

External Factors and Issues

Errors related to the course content (Transcription Errors)

Summary and Debugging Flow-Chart

**NEW** The Debugging Flow-Chart with ChatGPT

Pandas Series and Index Objects

Intro

First Steps with Pandas Series

Analyzing Numerical Series with unique(), nunique() and value_counts()

Maximum Value in a numerical column

Most common Value in a numerical column

Analyzing non-numerical Series with unique(), nunique(), value_counts()

Unique Values in a Text Column

Most common value in a text column

Creating Pandas Series (Part 1)

Creating Pandas Series (Part 2)

Creating Pandas Series from scratch

Indexing and Slicing Pandas Series

Sorting of Series and Introduction to the inplace - parameter

Sorting "inplace"

nlargest() and nsmallest()

The n largest values in a Pandas Series

idxmin() and idxmax()

Manipulating Pandas Series

Pandas Series

Jupyter Coding Exercise 3 (Intro)

Jupyter Coding Exercise 3 (Solution)

First Steps with Pandas Index Objects

Creating Index Objects from Scratch

Selecting Column Labels of a DataFrame

Changing Row Index with set_index() and reset_index()

Resetting an Index

Changing Column Labels

Renaming Index & Column Labels with rename()

Renaming Column Labels

Pandas Index objects

Jupyter Coding Exercise 4 (Intro)

Jupyter Coding Exercise 4 (Solution)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers advanced data analysis with AI assistance from ChatGPT, which may enhance learning and project development

Explores Pandas library, which is essential for data handling in fields like data science, machine learning, and finance

Provides a comprehensive overview of Pandas, including its latest updates, making it highly relevant

Offers numerous coding exercises and practical case studies, ensuring hands-on practice and application of concepts

Features real-world examples and datasets, making learning more relatable and applicable

Covers fundamentals of statistical concepts and integrates them with Pandas, enhancing data analysis capabilities

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in The Complete Pandas Bootcamp 2024: Data Science with Python with these activities:

Read the book "Python for Data Analysis"

Show steps

This book provides a comprehensive introduction to Python for data analysis. By reading this book, you can supplement your understanding of the concepts covered in the course and gain a deeper understanding of Pandas.

View Python for Data Analysis on Amazon

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Acquire the book
Read the chapters relevant to the topics covered in the course
Complete the exercises and examples provided in the book

Offer assistance to fellow students in the course forum

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By helping others, you can solidify your own understanding of the concepts covered in the course. Participate in the course forum and offer assistance to fellow students who may be struggling.

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Review the course forum regularly
Identify questions or discussions where you can offer assistance
Provide clear and helpful responses

Create a collection of resources on Pandas

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Organize and consolidate resources on Pandas to enhance your learning experience. This could include articles, tutorials, code examples, and other materials.

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Gather relevant resources from various sources
Organize the resources into a logical structure
Create a document or website to share your compilation

One other activity

Expand to see all activities and additional details

Show all four activities

Contribute to a Pandas open-source project

Show steps

Practical experience is invaluable. Contribute to a Pandas open-source project to apply your skills, learn from others, and make a meaningful contribution to the community.

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Identify a Pandas open-source project that aligns with your interests
Familiarize yourself with the project's codebase and documentation
Identify an area where you can contribute your skills
Make a pull request with your proposed changes

Career center

Learners who complete The Complete Pandas Bootcamp 2024: Data Science with Python will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts may work with this course as they use Pandas in their work. This course features nearly 36 hours of video that covers Pandas. Also, this course is good for those with backgrounds in other programming languages and want to get into Python for data analysis.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists will benefit from the real-world case studies in this course. Also, the five-part course structure goes from Pandas basics, to the complete data workflow A-Z, project challenges, application for finance and investing, and finally an application for machine learning.

See salaries and explore the career path for Data Scientist

Financial Analyst

Financial Analysts may wish to take this course as it relates to Pandas work for finance and investing. Also, since this course uses real data, it's a great way to learn.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

Quantitative Analysts may benefit from taking this course. The course covers topics like data analysis, data cleaning, and data preparation. These are useful skills for a Quantitative Analyst.

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Machine Learning Engineer

Machine Learning Engineers will benefit from the Pandas skills taught in this course and its emphasis on Python as a platform/environment for Data Science. Also, the course covers some Machine Learning libraries such as scikit-learn.

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Data Engineer

Data Engineers will find the Pandas training in this course useful for their work. Also, it may be helpful to have the Python crash course given by the course to make sure your coding skills are proficient before learning Pandas.

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Business Intelligence Analyst

Business Intelligence Analysts can use this course as a way to improve their understanding of Pandas. Also, the materials on data analysis and data cleaning will be essential to have.

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Statistician

Statisticians can benefit from working on the data analysis and data processing in this course. Also, the course covers scipy, which will be helpful in Python coding.

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Data Architect

Data Architects would be likely to benefit from taking this course. The real-life case studies and projects in this course will be especially helpful for someone in this role.

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Software Developer

Software Developers may find some use with this course. This is because the course explains the full real-world data workflow, A-Z.

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Database Administrator

Database Administrators may wish to look into this course. The course teaches real-world projects, case studies, and the full data workflow, A-Z.

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Product Manager

Product Managers may work with this course as it covers the full data workflow, A-Z. Also, the 150+ coding exercises will be beneficial for practicing and mastering Pandas skills.

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Business Analyst

Business Analysts may wish to take this course as it may be useful for many of the Pandas basics and coding exercises given in this course.

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Operations Research Analyst

Operations Research Analysts may find this course useful as it covers Pandas basics, data analysis, data cleaning, and the full real-world data workflow, A-Z.

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Market Researcher

Market Researchers may be interested in this course as it covers Pandas basics and the full real-world data workflow, A-Z. Also, the data analysis and data cleaning topics will be useful in this role.

See salaries and explore the career path for Market Researcher