The Complete Pandas Bootcamp 2020: Data Science with Python
4.7 (1,211 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
8,648 students enrolled

The Complete Pandas Bootcamp 2020: Data Science with Python

Pandas fully explained | 150+ Exercises | Must-have skills for Machine Learning & Finance | + Scikit-Learn and Seaborn
Highest Rated
4.7 (1,211 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
8,648 students enrolled
Created by Alexander Hagmann
Last updated 8/2020
English
English [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 32 hours on-demand video
  • 42 articles
  • 24 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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 1.0 (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 1.0
  • Learn the Basics of Pandas and Numpy Coding (Appendix)
  • Learn and master important Statistical Concepts with scipy
Course content
Expand all 307 lectures 32:02:07
+ Getting Started
8 lectures 56:43
Tips: How to get the most out of this course
05:27
More FAQ / Important Information
02:37
Installation of Anaconda
08:08
Opening a Jupyter Notebook
09:29
How to use Jupyter Notebooks
14:00
How to tackle Pandas Version 1.0
03:07
+ Pandas Basics (DataFrame Basics I)
21 lectures 01:56:16
Create your very first Pandas DataFrame (from csv)
09:09
Pandas Display Options and the methods head() & tail()
06:41
First Data Inspection
11:25
Built-in Functions, Attributes and Methods with Pandas
09:34
Make it easy: TAB Completion and Tooltip
08:57
First Steps
3 questions
Explore your own Dataset: Coding Exercise 1 (Intro)
03:46
Selecting Columns
06:05
Selecting one Column with the "dot notation"
02:16
Zero-based Indexing and Negative Indexing
03:04
Selecting Rows with iloc (position-based indexing)
10:07
Slicing Rows and Columns with iloc (position-based indexing)
04:39
Selecting Rows with loc (label-based indexing)
03:14
Slicing Rows and Columns with loc (label-based indexing)
10:21
Label-based Indexing Cheat Sheets
00:02
Indexing and Slicing with reindex()
05:30
Summary, Best Practices and Outlook
06:30
Indexing and Slicing
6 questions
Coding Exercise 2 (Intro)
01:20
Coding Exercise 2 (Solution)
03:58
Advanced Indexing and Slicing (optional)
05:22
+ Pandas Series and Index Objects
20 lectures 01:50:09
Intro
00:17
First Steps with Pandas Series
03:53
Analyzing Numerical Series with unique(), nunique() and value_counts()
13:50
Analyzing non-numerical Series with unique(), nunique(), value_counts()
07:17
Creating Pandas Series (Part 1)
06:12
Creating Pandas Series (Part 2)
05:40
Indexing and Slicing Pandas Series
10:08
Sorting of Series and Introduction to the inplace - parameter
08:59
nlargest() and nsmallest()
03:48
idxmin() and idxmax()
05:20
Manipulating Pandas Series
07:47
Pandas Series
5 questions
Coding Exercise 3 (Intro)
00:04
Coding Exercise 3 (Solution)
06:18
First Steps with Pandas Index Objects
05:57
Creating Index Objects from Scratch
03:16
Changing Row Index with set_index() and reset_index()
10:07
Changing Column Labels
03:20
Renaming Index & Column Labels with rename()
03:51
Pandas Index objects
3 questions
Coding Exercise 4 (Intro)
00:04
Coding Exercise 4 (Solution)
04:00
+ DataFrame Basics II
15 lectures 01:17:42
Intro
00:09
Filtering DataFrames by one Condition
10:20
Filtering DataFrames by many Conditions (AND)
04:45
Filtering DataFrames by many Conditions (OR)
05:04
Advanced Filtering with between(), isin() and ~
08:35
any() and all()
04:07
Removing Columns
05:18
Removing Rows
07:06
Adding new Columns to a DataFrame
03:27
Creating Columns based on other Columns
06:37
Adding Columns with insert()
02:43
Adding new Rows (hands-on approach)
02:55
DataFrame Basics II
4 questions
Coding Exercise 5 (Intro)
00:04
Coding Exercise 5 (Solution)
08:49
+ Manipulating Elements in a DataFrame / Slice +++Important, know the Pitfalls!+++
8 lectures 48:10
Intro
00:31
Best Practice (How you should do it)
09:15
Chained Indexing: How you should NOT do it (Part 1)
09:35
Chained Indexing: How you should NOT do it (Part 2)
08:42
View vs. Copy
05:58
Simple Rules what to do when...
08:09
Manipulating DataFrames / Slices
3 questions
Coding Exercise 6 (Intro)
00:04
Coding Exercise 6 (Solution)
05:55
+ DataFrame Basics III
15 lectures 01:43:38
Intro
00:14
Sorting DataFrames with sort_index() and sort_values() (Version 1.0 Update)
09:09
Ranking DataFrames with rank()
08:07
nunique() and nlargest() / nsmallest() with DataFrames
05:30
Summary Statistics and Accumulations
10:26
The agg() method
03:27
Coding Exercise 7 (Intro)
00:04
Coding Exercise 7 (Solution)
05:03
User-defined Functions with apply(), map() and applymap()
13:46
Hierarchical Indexing (Part 1)
10:43
Hierarchical Indexing (Part 2)
11:18
String Operations (Part 1)
07:20
String Operations (Part 2)
09:36
Coding Exercise 8 (Intro)
00:04
Coding Exercise 8 (Solution)
08:50
+ Visualization with Matplotlib
9 lectures 49:16
Intro
00:12
The plot() method
08:48
Customization of Plots
12:56
Histograms (Part 1)
04:34
Histograms (Part 2)
06:27
Barcharts and Piecharts
04:00
Scatterplots
07:18
Coding Exercise 9 (Intro)
00:04
Coding Exercise 9 (Solution)
04:56
+ ----PART 2: FULL DATA WORKFLOW A-Z----
2 lectures 00:22
Welcome to PART 2: Full Data Workflow A-Z
00:17
Download: Part 2 Course Materials
00:04
+ Importing Data
6 lectures 50:40
Importing csv-files with pd.read_csv
14:13
Importing messy csv-files with pd.read_csv
09:44
Importing messy Data from Excel with pd.read_excel()
08:06
Importing Data from the Web with pd.read_html()
07:09
Coding Exercise 10
00:17
Requirements
  • A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
  • An internet connection capable of streaming videos.
  • Ideally some Spreadsheet Basics/Programming Basics (not mandatory, the course guides you through the basics)
Description

+++ UPDATE (June 2020): Bonus: A complete Machine Learning Project A-Z with Pandas, Scikit-Learn and Seaborn is available now! +++

+++ UPDATE (Feb 2020): 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.x! +++


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 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 with 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 4 hours 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, then 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 1.0. 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.