An introduction to Python for Data Science

Language: English

Timeline

Session I: 9.00–12.00

Break

Session II: 13.00-16.00

Description

On day one we will build a working knowledge for performing simple data processing of tabular data 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.

Method

The day is based on participatory live coding where the instructor will type in the code with the participants following along. We have good experience with this teaching form. There will also be plenty of small 5 min. exercises.

Content

(1/2 h) Why? workflow? Setting the scene - packages/eco-system - sharing - visibility - literate programming - reproducibility
(2 h) Getting started with Jupyter Notebook
Running cells, data types
Arithmetic
Control (if)
For-loops (and list comprehension)
Functions (+ doc-strings)
Objects and methods
Shift-tab and help
Shortcuts
Comments (Markdown) and Latex-ify
Cell execution order and state (pitfalls of arbitrary execution order)
Dictionaries and JSON (if time permits)
(2 1/4 h) Pandas
Intro and key data structures
Iterating - lists and dictionaries
Extracting columns/rows - indexing and selecting
Handling missing data
Simple statistics (mean, count, median, min, max, std, corr)
File handling (CSV, STATA, SAS)
Concatenate, join and merge
Split-apply-combine methodologies (groupby)
(1 h) Visualization with seaborn
Bar plots and histograms
Basic terminology
Scatter plots
(1/4 h) Outlook, final questions, wrap-up etc.