Data Analysis with Python - Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn)

Learn Data Analysis with Python in this comprehensive tutorial for beginners, with exercises included!
NOTE: Check description for updated Notebook links.

Data Analysis has been around for a long time, but up until a few years ago, it was practiced using closed, expensive and limited tools like Excel or Tableau. Python, SQL and other open libraries have changed Data Analysis forever.

In this tutorial you'll learn the whole process of Data Analysis: reading data from multiple sources (CSVs, SQL, Excel, etc), processing them using NumPy and Pandas, visualize them using Matplotlib and Seaborn and clean and process it to create reports.
Additionally, we've included a thorough Jupyter Notebook tutorial, and a quick Python reference to refresh your programming skills.

💻 Course created by Santiago Basulto from DataWars

 ⭐️ Course Contents ⭐️
⌨️ Part 1: Introduction
What is Data Analysis, why Python?, what other options are there? what's the cycle of a Data Analysis project? What's the difference between Data Analysis and Data Science?

⌨️ Part 2: Real Life Example of a Python/Pandas Data Analysis project (00:11:11)
A demonstration of a real life data analysis project using Python, Pandas, SQL and Seaborn. Don't worry, we'll dig deeper in the following sections

⌨️ Part 3: Jupyter Notebooks Tutorial (00:30:50)
A step by step tutorial to learn how to use Juptyer Notebooks

⌨️ Part 4: Intro to NumPy (01:04:58)
Learn why NumPy was such an important library for the data-processing world in Python. Learn about low level details of computations and memory storage, and why tools like Excel will always be limited when processing large volumes of data.

⌨️ Part 5: Intro to Pandas (01:57:08)
Pandas is arguably the most important library for Data Processing in the Python world. Learn how it works and how its main data structure, the Data Frame, compares to other tools like spreadsheets or DFs used for Big Data

⌨️ Part 6: Data Cleaning (02:47:18)
Learn the different types of issues that we'll face with our data: null values, invalid values, statistical outliers, etc, and how to clean them.

⌨️ Part 7: Reading Data from other sources (03:25:15)

⌨️ Part 8: Python Recap (03:55:19)
If your Python or coding skills are rusty, check out this section for a quick recap of Python main features and control flow structures.

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[freecodecamp]

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

[leixun]

*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*

[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)

[tonysoviet]

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!

[JoseAntonio-wtzm]

This course is awesome ! The explanations are very clear and the teaching way is very fine. Thank you so much for all the hard work you put in making this !

[satyasieng]

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

[jacktorrence]

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

[jiksvids]

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

[StevenTokyo]

Been looking forward to this course since the beginning of the year. It could not have come at a better time. Thank you very much!

[himansuvarghese]

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!

[mohsenkalani]

I never could imagine to find such an invaluable complete course for FREE in YouTube. I can not find words to appreciate.

[noranviccanto]

Just seen the first few minutes and I seem to loke it. You have one of the most easy, quick and to the point explanations. Subscribed and willing to complete the video...

[UnitedKonvict]

You’re absolutely brilliant and generous for giving out this much information for all of us to learn, thank you!

[notallama]

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.

[theyadore]

Coming from Excel to Python i found this really helped. Thank you for helping me get my bearings.

[rumaisay]

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 :)

[datafreak]

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

[daniellakshan]

Oh, thank you so much brothers, I have been waiting for a course like this from you guys, your channel has been so much helpful for me to improve my coding skills. You guys deserve to receive an award for this incredible service. Thanks again brothers, keep it up. 😘

[EustaquioSantimano]

Thank you Santiago Basulto ! The beginner course training was excellent. It was a delicious appetiser. Now I am waiting in anticipation for the main dish :)

[mlevy]

Amazing video, really really good, thank you Santiago for offering such a great free class online.