Introduction to Python for Data Analysis

Python is one of the world's most popular programming languages, used in as diverse a range of places as in software systems used by NASA, in Google's search engine and web crawler, and by Industrial Light & Magic in the production of special effects for feature films.

It has also become the leading language for data analysis and machine learning.

What makes it so popular is that it is intuitive to learn, extremely easy to read, and - while not as fast as compiled languages (like C++), its ease-of-use and straightforward syntax will save you a huge amount of time. There are also a wide range of libraries and code recipes to accomplish almost any task, and a vast community of enthusiasts able to offer guidance and support.

Learning outcomes:

• Understand, and have practical experience with, the basic syntax and approach to coding in Python.
• Write and use modular functions, and import third-party libraries.
• Learn and apply a basic set of methods from the core data analysis libraries of Numpy, Pandas and Matplotlib.
• Investigate and manipulate data to learn its metadata, shape and robustness.
• Learn how data management, and analysis, supports the Model-View-Control approach to application development.
• Investigate and apply JavaScript tools, such as D3, Plotly and Leaflet, for data presentation and visualisation.
• Learn how to improve your knowledge and experience through online documentation and question-and-answer communities.

Lessons:

1. 01 - Installing Python and Jupyter Notebook with Anaconda
2. 02 - Python basics
3. 03 - Python advanced
4. 04 - Python for data analysis
5. 05 - Introduction to data as a science
6. 06 - JavaScript tools for visualisation

This fledgling overview is not aimed at teaching analysis or statistical techniques, but rather as a brief introduction to the syntax and methods used in Python programming, as well as a tiny fragment of JavaScript for data presentation. Along the way, you'll learn some of the techniques and approaches to developing software for web applications, as well as what to do to continue your learning experience.

Sources:

• LearnPython.org
• Beginning Python by Magnus Lie Hetland, Apress
• 10 Minutes to pandas, from the pandas documentation

Licence:

MIT Licence