@freecodecamp

⚠️ Note: Instead of loading the notebooks on notebooks.ai, you should use Google Colab instead. Here are instructions on loading a notebook directly from GitHub into Google Colab: https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb#scrollTo=K-NVg7RjyeTk

The code links in the description have been updated to the content stored on GitHub.

My takeaways:
1. Table of Content 1:45
2. Introduction 2:52
2.1 What is data analysis 2:52
2.2 Data analysis tools 4:38
2.3 Data analysis process 7:31
2.4 Data Analysis vs Data Science 8:56
2.5 Python and PyData Ecosystem 9:28
2.6 Python data analysis vs Excel 9:46
3. Real example data analysis with Python: getting a sense of what you can learn from this course 11:00
4. How to use Jupyter Notebooks 30:50
5. Intro to NumPy 1:04:58
5.1 Low-level basis: binary numbers, memory footprint 1:09:32
5.2 Python is not memory efficient to store numbers since it wraps everything into objects. Whereas in NumPy, we can select the number of bits to represent numbers 1:22:50
5.3 NumPy can compute arrays faster than Python 1:24:58
5.4 NumPy tutorial: NumPy arrays, matrices 1:29:47
5.5 Memory footprint and performance: Python vs NumPy 1:53:14
6. Intro to Pandas: getting, processing and visualizing data 1:56:58
6.1 Pandas data structure: Series 1:58:41
6.2 We can change the index of Pandas series and this is fundamentally different from NumPy arrays 2:02:55
6.3 The upper limit of slicing in Pandas series is included, whereas, in NumPy, the limit is excluded 2:07:55
6.4 Pandas data structure: DataFrames 2:14:36
6.5 Most operations in Pandas are immutable 2:29:10
6.7 Reading external data 2:36:47
6.8 Pandas plotting 2:44:41
7. Data cleaning 2:47:18
7.1 Handling miss data 2:51:40
7.2 Cleaning invalidate values 3:03:17
7.3 Handling duplicated data 3:06:09
7.4 Handling text data 3:11:05
7.5 Data visualization 3:13:41
7.6 Matplotlib global API 3:14:25
7.7 Matplotlib OOP API 3:18:27
8. Working with data from(/to) SQL, CSV, txt, API etc. 3:25:15
8.1 Python methods for working with files 3:26:37
8.2 Python methods for working with CSV files 3:29:33
8.3 Pandas methods for working with CSV files 3:30:05
8.4 Python methods for working with SQL 3:36:17
8.5 Pandas methods for working with SQL 3:38:58
8.6 Pandas methods for working with HTML 3:43:09
8.7 Pandas methods for working with Excel files 3:49:56
9. Python recap 3:55:18

@SpicyTurkey83

I have a data science minor (with a Masters in Mech. Engineering). I work as a data scientist with focus on engine systems at the airlines. I'll tell you right now, this course was more informative than all the classes combined that I took in college. Santiago is one of the best you'll get, and also for free. This is truly a wonderful refresher.

@satyasieng5166

1. Table of Content 1:45
2. Introduction 2:52
2.1 What is data analysis 2:52
2.2 Data analysis tools 4:38
2.3 Data analysis process 7:31
2.4 Data Analysis vs Data Science 8:56
2.5 Python and PyData Ecosystem 9:28
2.6 Python data analysis vs Excel 9:46
3. Real example data analysis with Python: getting a sense of what you can learn from this course 11:00
4. How to use Jupyter Notebooks 30:50
5. Intro to NumPy 1:04:58
5.1 Low-level basis: binary numbers, memory footprint 1:09:32
5.2 Python is not memory efficient to store numbers since it wraps everything into objects. Whereas in NumPy, we can select the number of bits to represent numbers 1:22:50
5.3 NumPy can compute arrays faster than Python 1:24:58
5.4 NumPy tutorial: NumPy arrays, matrices 1:29:47
5.5 Memory footprint and performance: Python vs NumPy 1:53:14
6. Intro to Pandas: getting, processing and visualizing data 1:56:58
6.1 Pandas data structure: Series 1:58:41
6.2 We can change the index of Pandas series and this is fundamentally different from NumPy arrays 2:02:55
6.3 The upper limit of slicing in Pandas series is included, whereas, in NumPy, the limit is excluded 2:07:55
6.4 Pandas data structure: DataFrames 2:14:36
6.5 Most operations in Pandas are immutable 2:29:10
6.7 Reading external data 2:36:47
6.8 Pandas plotting 2:44:41
7. Data cleaning 2:47:18
7.1 Handling miss data 2:51:40
7.2 Cleaning invalidate values 3:03:17
7.3 Handling duplicated data 3:06:09
7.4 Handling text data 3:11:05
7.5 Data visualization 3:13:41
7.6 Matplotlib global API 3:14:25
7.7 Matplotlib OOP API 3:18:27
8. Working with data from(/to) SQL, CSV, txt, API etc. 3:25:15
8.1 Python methods for working with files 3:26:37
8.2 Python methods for working with CSV files 3:29:33
8.3 Pandas methods for working with CSV files 3:30:05
8.4 Python methods for working with SQL 3:36:17
8.5 Pandas methods for working with SQL 3:38:58
8.6 Pandas methods for working with HTML 3:43:09
8.7 Pandas methods for working with Excel files 3:49:56
9. Python recap 3:55:18

@tonysoviet3692

As a data analyst in Maersk, I really appreciate this course in balancing between the technical foundations and actual executions! Most people only get to learn the codes without understanding the concepts, which are what separate workers from engineers!

@Nachiketa_TheCutiePie

Part 1: Introduction
 Part 2: Real Life Example of a Python/Pandas Data Analysis project 00:11:11
Part 3: Jupyter Notebooks Tutorial (00:30:50)
Part 4: Intro to NumPy (01:04:58), (01:30:00)

Part 5: Intro to Pandas (01:57:08)
Part 6: Data Cleaning (02:47:18)
Part 7: Reading Data from other sources (03:25:15)
Part 8: Python Recap (03:55:19)

@whileHidingmyYouTube

25:00 Sakila Database
31:00 Jupyter notebook
1:05:00 Numpy
1:25:00 Data Types
2:48:00 Data Cleaning

@himansuvarghese1181

There's a special place in heaven for you guys. 
After the python course, I had to try different videos like numpy, pandas etc. This is way better!

@iskandarzulkarnain9705

I swear this is the best channel in youtube ever.

@notallama1868

2:08:09 The difference where the upper limit is included only seems to apply if you've defined your own index.  It seems to work the same if you use the default numeric index.

@jiksvids9804

This is far better than many high-priced tutorial courses on the most popular MOOC platforms. I will forever keep this for future reference ❤❤

@nanditsrivastava2026

i dont know how many comments down here are real, but i think this tutorial was wayyy to direct for a beginner... Numpy and Pandas are explained very well no doubts on that...the data cleaning part was very direct, no beginner will get a bit of it, reading external files section was Ok, Matplotlib was explained like everyone knows about the attributes since birth.

I am working as a data scientist from the past 4 years, i would not recommend this to anyone who is a beginner, except for the Intro , numpy , pandas and reading data from external sources section...

@rumaisay746

I am new to python. but I enjoyed this. If you are a newbie, dont focus on learning the syntax in this video. the best way to learn programming is to learn the functions first and then set aside sometime to work on your syntax skills. syntax overwhelms in the beginning. thank you for this. also loved your voice :)

@Hi-theere

Thanks!

@jacktorrence4595

This is the best channel ever.
No one does  so clear, long and ad free videos....
My compliments 👏👏🤟👊🤙

@yashkeshorts343

7.6 Matplotlib global API 3:14:25
7.7 Matplotlib OOP API  3:18:27

@YlmazDALKIRANscallion

2:18 Please change the video description and use the index below

00:00:00 Introduction
00:11:11 Real Life Example of a Python/Pandas Data Analysis project
00:30:50 Jupyter Notebooks Tutorial
01:04:58 Intro to NumPy
01:57:08 Intro to Pandas 
02:47:18 Data Cleaning 
03:25:15 Reading Data from other sources
03:55:19 Python Recap 

So we can jump out the section by using video process bar.

@notallama1868

1:45:43 Notably you can multiply arrays of different dimensions so long as the array with more dimensions is made up of arrays of the same shape as the smaller array.

@datafreak911

I have been watching your course for 2 weeks and I can say this is the best guide  I have ever seen. Thank you guys

@N-k-N

Copy of @leixun comment, so I can see it at the top.