The Complete Pandas Bootcamp 2024- Data Science with Python

The Complete Pandas Bootcamp 2024: Data Science with Python, available at $119.99, has an average rating of 4.73, with 360 lectures, 34 quizzes, based on 3604 reviews, and has 27211 subscribers.

You will learn about 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 This course is ideal for individuals who are Everyone who want to step into Data Science. Pandas is Key to everything. or Data Scientists who want to improve their Data Handling/Manipulation skills. or Everyone who want to switch Data Projects from Excel to more powerful tools (e.g. in Research/Science) or Investment/Finance Professionals who reached the limits of Excel. It is particularly useful for Everyone who want to step into Data Science. Pandas is Key to everything. or Data Scientists who want to improve their Data Handling/Manipulation skills. or Everyone who want to switch Data Projects from Excel to more powerful tools (e.g. in Research/Science) or Investment/Finance Professionals who reached the limits of Excel.

Enroll now: The Complete Pandas Bootcamp 2024: Data Science with Python

Summary

Title: The Complete Pandas Bootcamp 2024: Data Science with Python

Price: $119.99

Average Rating: 4.73

Number of Lectures: 360

Number of Quizzes: 34

Number of Published Lectures: 358

Number of Published Quizzes: 34

Number of Curriculum Items: 394

Number of Published Curriculum Objects: 392

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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 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

Who Should Attend

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.

Target Audiences

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.

(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: Matplotliband Seaborn for Data Visualization | Numpy, Scipy and Scikit-Learn for Machine Learning, scientific, and statistical computing. This course covers all these Libraries. 

ChatGPTfor 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 errorsand 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!

Course Curriculum

Chapter 1: Getting Started

Lecture 1: Overview / Student FAQ

Lecture 2: Tips: How to get the most out of this course

Lecture 3: Did you know that?

Lecture 4: More FAQ / Important Information

Lecture 5: Installation of Anaconda

Lecture 6: Opening a Jupyter Notebook

Lecture 7: How to use Jupyter Notebooks

Lecture 8: Downloads (Get all Course Materials here!) **UPD Nov 23**

Chapter 2: - PART 1: PANDAS FROM ZERO TO HERO (BUILDING BLOCKS) -

Lecture 1: Intro to Tabular Data / Pandas

Lecture 2: Download Course Materials Part 1 (Reminder)

Chapter 3: **NEW** Pandas Coding with your personal assistant – ChatGPT

Lecture 1: Introduction

Lecture 2: Coding assistance for Pandas Coding using GPT-3.5 / GPT-4o mini (free)

Lecture 3: Pandas Data Analysis using GPT-4 / GPT-4o mini (Plus Subscription)

Chapter 4: Pandas Basics (DataFrame Basics I)

Lecture 1: Create your very first Pandas DataFrame (from csv)

Lecture 2: How to read CSV-files from other Locations

Lecture 3: Pandas Display Options and the methods head() & tail()

Lecture 4: First Data Inspection

Lecture 5: Built-in Functions, Attributes and Methods with Pandas

Lecture 6: Make it easy: TAB Completion and Tooltip

Lecture 7: Explore your own Dataset: Jupyter Coding Exercise 1 (Intro)

Lecture 8: Explore your own Dataset: Jupyter Coding Exercise 1 (Solution)

Lecture 9: Selecting Columns

Lecture 10: Selecting one Column with the dot notation

Lecture 11: Zero-based Indexing and Negative Indexing

Lecture 12: Selecting Rows with iloc (position-based indexing)

Lecture 13: Slicing Rows and Columns with iloc (position-based indexing)

Lecture 14: Position-based Indexing Cheat Sheets

Lecture 15: Selecting Rows with loc (label-based indexing)

Lecture 16: Slicing Rows and Columns with loc (label-based indexing)

Lecture 17: Label-based Indexing Cheat Sheets

Lecture 18: Indexing and Slicing with reindex()

Lecture 19: Summary, Best Practices and Outlook

Lecture 20: Jupyter Coding Exercise 2 – Intro

Lecture 21: Jupyter Coding Exercise 2 – Solution

Lecture 22: **NEW** Coding Exercises with ChatGPT

Lecture 23: Advanced Indexing and Slicing (optional)

Chapter 5: Excursus: How to avoid and debug Coding Errors (incl. ChatGPT)

Lecture 1: Introduction

Lecture 2: Test your debugging skills!

Lecture 3: Major reasons for Coding Errors

Lecture 4: The most commonly made Errors at a glance

Lecture 5: Omitting cells, changing the sequence and more

Lecture 6: IndexErrors

Lecture 7: Indentation Errors

Lecture 8: Misuse of function names and keywords

Lecture 9: TypeErrors and ValueErrors

Lecture 10: **NEW** Debugging Pandas Errors with ChatGPT

Lecture 11: Getting help on StackOverflow.com

Lecture 12: How to traceback more complex Errors

Lecture 13: Problems with the Python Installation

Lecture 14: External Factors and Issues

Lecture 15: Errors related to the course content (Transcription Errors)

Lecture 16: Summary and Debugging Flow-Chart

Lecture 17: **NEW** The Debugging Flow-Chart with ChatGPT

Chapter 6: Pandas Series and Index Objects

Lecture 1: Intro

Lecture 2: First Steps with Pandas Series

Lecture 3: Analyzing Numerical Series with unique(), nunique() and value_counts()

Lecture 4: Analyzing non-numerical Series with unique(), nunique(), value_counts()

Lecture 5: Creating Pandas Series (Part 1)

Lecture 6: Creating Pandas Series (Part 2)

Lecture 7: Indexing and Slicing Pandas Series

Lecture 8: Sorting of Series and Introduction to the inplace – parameter

Lecture 9: nlargest() and nsmallest()

Lecture 10: idxmin() and idxmax()

Lecture 11: Manipulating Pandas Series

Lecture 12: Jupyter Coding Exercise 3 (Intro)

Lecture 13: Jupyter Coding Exercise 3 (Solution)

Lecture 14: First Steps with Pandas Index Objects

Lecture 15: Creating Index Objects from Scratch

Lecture 16: Changing Row Index with set_index() and reset_index()

Lecture 17: Changing Column Labels

Lecture 18: Renaming Index & Column Labels with rename()

Lecture 19: Jupyter Coding Exercise 4 (Intro)

Lecture 20: Jupyter Coding Exercise 4 (Solution)

Instructors

Alexander Hagmann
Data Scientist | Finance Professional | Entrepreneur

Rating Distribution

1 stars: 25 votes

2 stars: 26 votes

3 stars: 166 votes

4 stars: 1073 votes

5 stars: 2314 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!