python_for_data_analysis

Repository with training materials for introduction to Python for Data Analysis.

Learning objectives

This Notebook is created as a document supporting introduction and demonstration of Python for data analysis. First part provides a basic introduction with motivations for using python and explaining its landscape for scientific computing. Second part is a rapid demonstration on various functions of Pandas library. Focus is on showing whats possible and prodivind example code snippets that can be tried and studied deeper later.

We will cover:

1. How to automate common data analysis and data manipulation tasks.
So there in no need to do manual data upload, cleaning, pivot tables to aggregate or do vlookups to join the data.

2. How to perform sophisticated analysis functions.
Timeseries analysis, filling missing data, dropping duplicates, descriptive statistics etc in seconds with few lines of code.

3. How to create a reproducible analysis.
So you can rerun it with same parameters or create multiple follow up versions easily. When there is a sudden change no need to repeat whole process again, you can just rerun analysis code with the new assumptions,

4. How to visualise and share insights directly from the source of the analysis.
No need to waste time copy-pasting analysis output to ppt decks !

5. How to share and collaborate on analysis together with the data team.
Can ask data team for notebooks, tweak them and collaborate together.

Set up instructions

To follow the session interactively , you can download Anaconda and open it using jupyter notebook:

Full instructions: https://jupyter.readthedocs.io/en/latest/install.html

Main installation steps:

1. Download Anaconda. We recommend downloading Anaconda’s latest Python 3 version.

2. Install the version of Anaconda which you downloaded, following the instructions on the download page.

3. Congratulations, you have installed Jupyter Notebook. To run the notebook, open terminal and run the command:

jupyter notebook

For best experience I recommend opening and navigating through this notebook in Jupyter Lab with added support for the side bar Table of Content.