Additional Resources Wiki

[alvinctk]

A community contributed wiki on data science, python, SQL, and machine learning additional resources. You are welcome to edit this wiki post add any resources (or add a new section) that you find useful.

To add a new section, type the following:

[details="Sample Section Name"]
- [Resources 1 link](www.app.dataquest.io)
- [Resources 2 link](www.dataquest.io)
[/details]

Sample Section Name

• Resources 1 link
• Resources 2 link

Wiki Rules

Any resources that does not have either data science, python, SQL, and machine learning information will be removed.

Any information that violates the community will be removed.

Thank you for your help and your contribution.

Additional Resources

Data Structures and Algorithms Notes/Text/Lecture

• Useful old Stanford CS Library

Lecture Videos on Programming

• MIT 6.006 Introduction to Algorithms, Fall 2011

Pandas Best Practices

• Zen of Python Pandas Optimizations

Python Best Practices

@alvinctk

Here are some best practices of Python that you can read online:

1. Read the Zen of Python
   On the python interpreter do the following:

>>> import this
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren’t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you’re Dutch.
Now is better than never.
Although never is often better than right now.
If the implementation is hard to explain, it’s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea – let’s do more of those!

2. Read Python PEP 8 style guide
   https://www.python.org/dev/peps/pep-0008/
3. Read Google Python style guide
   http://google.github.io/styleguide/pyguide.html
   The guide provides a good example on what not to do and what to do.
4. My notes on Transforming Code into Beautiful Idiomatic Python
   Common examples on transforming code into idiomatic Python.

As you observed that all points mentioned above are based on style, the style is very very important in writing readable code. It will have an impact on those who use the code base. And significantly reduce overhead time to understand someone’s code.

If you follow the above mentioned points, you should be well in accordance of writing Python code with the best practices in mind.

Sites to practice Python

In ascending order from easiest site (CodingBat) to hardest site (CodeChef).

• CodingBat
• LeetCode
• HackerRank
• HackerEarth
• CodeChef

Python Tutorial

• Google For Education - Python
• Python 3.7 Tutorial by Python Documentation
• Python 3.* Official Documentation
• Real Python
• Python Tutorials by techbeamers
• Other Tutorials by techbeamers
• Hacker Earth Programming Tutorial and Practice Problems

Python Textbook Resources

Online Textbook

• Online interactive textbooks: Runestone Academy
• Online text: Algorithms by Jeff Erickson
• Online text: Algorithms, 4th Java Edition by Robert Sedgewick and Kevin Wayne

• Online text: Introduction to Programming in Python by Robert Sedgewick and Kevin Wayne, Data Structures and Algorithms

Textbooks

• Introduction to Algorithms, Third Edition by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein.
• The Algorithm Design Manual by Second Edition by Steven S. Skiena.
• Programming Pearls, Second Edition by Jon Bentley
• Data Structures and Algorithms with Python, Springer Press, by Kent D. Lee and Steve Hubbard
• Python Algorithms - Mastering Basic Algorithms in the Python Language, Second Edition by Magnus Lie Hetland
• Fluent Python by Luciano Ramalho

Data Set

• DQ blog post - Free datasets for projects
• Analytics Vidhya - Data Hack
• data.europa.eu
• London government data

Data Visualization

• Tips for better visualizations
• Python Graph Gallery

Data Science Textbook Resources

• Python Data Science Handbook by Jake VanderPlas
• “Free Download Consent by Author/Publisher” Green Tea Press
• “Useful Pandas Text” - Python Data Analysis by Wes Mckinney

Data Science Videos

• Data School Youtube Channel
• Code Basics Youtube Channel

Data Science Cheat Sheet

• Large collection of neural networks machine learning numpy pandas matplotlib cheat sheet
• Data Science Overview Cheat Sheet

Machine Learning Posters

Poster Session AI

SQL

• Learn SQL from sqlbolt
• techonthenet mysql
• Recursion in SQL explained graphically
• Leetcode Curated SQL 70 Problems

How to improve/learn Python skills:

Suggested Python Learning Path For Beginners

Documentation has too many jargons. You should follow this path that I have listed below. Consult documentation when in doubt.

1. Read the Zen of Python

@alvinctk

1. Read the Zen of Python
   On the python interpreter do the following:

>>> import this
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren’t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you’re Dutch.
Now is better than never.
Although never is often better than right now.
If the implementation is hard to explain, it’s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea – let’s do more of those!

2. Watch the lecture on “Transforming Code into Beautiful Idiomatic Python” by Raymond Hettinger.

@alvinctk

My notes on Transforming Code into Beautiful Idiomatic Python
Common examples on transforming code into idiomatic Python.

3. Read Google Python Style Guide. Great with examples on what not to do (cons or bad) and what to do (pros or good).

@alvinctk

Read Google Python style guide
http://google.github.io/styleguide/pyguide.html

4. Read Python Official PEP 8 Style Guide

@alvinctk

Read Python PEP 8 style guide
https://www.python.org/dev/peps/pep-0008/

5. Read Python Official Tutorial. You can select specific version.

@alvinctk

Python 3.7 Tutorial by Python Documentation

6. Supplement Python education class by Google

@alvinctk

Google For Education - Python

7. Other additional resources (that will be updated and) found at Additional Resources Wiki.

Real Python explains really well.

@alvinctk

• Google For Education - Python
• Python 3.7 Tutorial by Python Documentation
• Real Python
• Python Tutorials by techbeamers
• Other Tutorials by techbeamers
• Hacker Earth Programming Tutorial and Practice Problems

8. Practice site for beginners

There are sites to practice your Python skills.

LeetCode, HackerRank, HackerEarth, and CodeChef has challenging exercises.

CodingBat has easier problems. You might want to try this first.

Next level after CodingBat, HackerRank and LeetCode has similar difficulties level from easy to hard. LeetCode has a better user community discussion forum, more updated problems, and with customizable test case.

Next level after HackerRank and LeetCode, try HackerEarth or CodeChef. Try CodeChef last.

HackerEarth is more difficult than HackerRank.
CodeChef difficult level is very very hard.

References that users use to resolve their issue:

Resources to resolve issues

• pip install package timeout - install a package in anaconda connection timeout
• Blog post about settingwithcopywarning

Contributed by:

@alvinctk
@Slavina
@mmanderson1
Contributors put your name here.

And update with a post below with your changes!

[Slavina]

Online text: Algorithms, 4th Edition by Robert Sedgewick and Kevin Wayne

All the code in this one is written in Java. Or am I missing something? Is there also a Python version?

[alvinctk]

All the code in this one is written in Java. Or am I missing something? Is there also a Python version?

The Java code is listed at https://algs4.cs.princeton.edu/code/.

[Slavina]

The Java code is listed at https://algs4.cs.princeton.edu/code/

What I mean is, I can’t find the Python version.

[alvinctk]

What I mean is, I can’t find the Python version.

I think I might have messed my url link. Apologize for any inconvenience.
Here is the Python version at https://introcs.cs.princeton.edu/python/40algorithms/

[alvinctk]

• Online text: Algorithms, 4th Java Edition by Robert Sedgewick and Kevin Wayne

• Online text: Introduction to Programming in Python by Robert Sedgewick and Kevin Wayne

Updated to reflect the differences in languages - Python vs Java.

[Slavina]

Updated to reflect the differences in languages - Python vs Java.

Thank you!! I couldn’t find it and was starting to think that I must be crazy!!

[alvinctk]

Thank you!! I couldn’t find it and was starting to think that I must be crazy!!

I had to google search for it.

[alvinctk]

Data Science Textbook Resources

• Python Data Science Handbook by Jake VanderPlas
• “Free Download Consent by Author/Publisher” Green Tea Press
• “Useful Pandas Text” - Python Data Analysis by Wes Mckinney

Updated and added on the top wiki post.

[alvinctk]

• Python 3.7 Tutorial by Python Documentation
• Real Python
• Python Tutorials by techbeamers
• Other Tutorials by techbeamers
• Hacker Earth Programming Tutorial and Practice Problems

Added a few Python tutorials links.

[alvinctk]

Python Tutorial

• Google For Education - Python

Added Google’s Python Class educational tutorial link.

[alvinctk]

Data Structures and Algorithms Notes/Text/Lecture

• Useful old Stanford CS Library

Added Useful old Stanford CS Library.

[alvinctk]

How to improve/learn Python skills:

Documentation has too many jargons. You should follow this path that I have listed below. Consult documentation when in doubt.

Added suggested Python learning path.

[Slavina]

Fluent Python by Luciano Ramalho

Added this. Really good resource for intermediate - advanced Python.

[alvinctk]

At first I thought I added it.

[Slavina]

Yeah - confusing. You get all the credit

[alvinctk]

Yay! I love it. Thanks for your help.

[alvinctk]

8. Practice site for beginners

There are sites to practice your Python skills.

LeetCode, HackerRank, HackerEarth, and CodeChef has challenging exercises.

CodingBat has easier problems. You might want to try this first.

Next level after CodingBat, HackerRank and LeetCode has similar difficulties level from easy to hard. LeetCode has a better user community discussion forum, more updated problems, and with customizable test case.

Next level after HackerRank and LeetCode, try HackerEarth or CodeChef. Try CodeChef last.

HackerEarth is more difficult than HackerRank.
CodeChef difficult level is very very hard.

Added number 8 in learning Python path.

[alvinctk]

References that users use to resolve their issue:

pip install package timeout

-install a package in anaconda connection timeout

Added new section for references that users use to resolve their issue.

[MatthewCH]

I added a nice Data Science Overview Cheat Sheet to the corresponding section.