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. (1/2 h) Why? workflow? Setting the scene - packages/eco-system - sharing - visibility - literate programming - reproducibility
  2. Getting started with Jupyter Notebook
  3. (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)
  4. (2 h) Pandas
  5. Intro and key data structures
  6. Different approaches to read files - one vs all
  7. Opening and closing
  8. Parsing CSV files
  9. Iterating - lists and dictionaries
  10. Populating and writing to a file (CSV, STATA, SAS)
  11. Extracting columns/rows - indexing and selecting
  12. Handling missing data
  13. Simple statistics (mean, count, median, min, max, std, corr)
  14. Manipulating
  15. Split-apply-combine methodologies (if time permits)
  16. (1 3/4 h) Visualization
  17. Visualization using pandas
  18. plotnine - Grammar of Graphics
  19. Plenty of examples and hands-on
  20. (1/4 h) Outlook, final questions, wrap-up etc.

Literature:

Python primer

A Primer on Scientific Programming with Python, Hans Petter Langtangen

Notebooks and data carpentry

Inspiration: Interesting Jupyter Notebooks

Inspiration: JupyterCon

Data Capentry: Data Analysis and Visualization in Python for Ecologists

Grammar of Graphics

Grammar of Graphics, Wilkinson (book)

The Grammar of Graphics, Wilkinson (chapter - shorter)

A Layered Grammar of Graphics, Wickham (paper)

ggplot cheat-sheat

Data Viz

Fundamentals of Data Visualization, Wilke

Edward R. Tufte, The Visual Display of Quantitive Information, Graphics Press, 1983

The Principle pf Propotional Ink, Carl Bergstrom and Jevin West,
An Admin’s Guide to Data Visualization, Caskey L. Dickson

Plotnine

Plotnine readthedocs

Plotnine pdf documentation

Plotnine examples

Data Carpentry plotnine examples

Python Plotting for Exploratory Data Analysis, with plotnine examples

Kaggle plotnine examples

Other

plotly