For HR competence course Data Visualization with Jupyter Notebook

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README.md

Data Processing and Visualization with Jupyter Notebook

A one day workshop on: Thursday 14th of February

Language: English

Timeline

Session I: 9.00--12.00

Break

Session II: 13.00-16.00

Summary

Data processing and visualization is crucial in many sciences---but how do I get started? In this course we will build a working knowledge for performing simple data processing and generating visualization of data using the programming language Python. The course requires no knowledge of the Python programming language, but a basic programming proficiency is required (your have programmed before). We will first cover basic programming in the Python language and how to work with the Jupyter Notebook tool. This basic part will then be extended with data processing and visualization based in a dedicated data analytics tools named Pandas. The day is organized with lectures and small exercises to be solved individually or in small groups.

Prerequisites:

If you have never programmed in Python before, it could be good with some background and read this
Please install the following Python distribution called anaconda, (select the Python 3.7 version)

Course content on-day

(1/2 h) Why? workflow? Setting the scene - packages/eco-system - sharing - visibility - literate programming - reproducibility
Getting started with Jupyter Notebook
(1 1/2 h) Python and Jupyter basics
1. Running cells, data types
2. Arithmetic
3. Control (if)
4. For-loops (and list comprehension)
5. Functions (+ doc-strings)
6. Objects and methods
7. Shift-tab and help
8. Shortcuts
9. Comments (Markdown)
10. Latex-ify
11. Cell execution order and state (pitfalls of arbitrary execution order)
(2 h) Pandas
Intro and key data structures
Different approaches to read files - one vs all
Opening and closing
Parsing CSV files
Iterating - lists and dictionaries
Populating and writing to a file (CSV, STATA, SAS)
Extracting columns/rows - indexing and selecting
Handling missing data
Simple statistics (mean, count, median, min, max, std, corr)
Manipulating
Split-apply-combine methodologies (if time permits)
(1 3/4 h) Visualization
Visualization using pandas
plotnine - Grammar of Graphics
Plenty of examples and hands-on
(1/4 h) Outlook, final questions, wrap-up etc.