course-structure-3day.txt

Day 1 - Introduction
Prerequisites: Basic use of MATLAB or another programming language
 - What is Python, why use it, who uses it and what are the benefits?
 - Basic control structures and variables; Jupyter Notebooks (i)
 - Applications of Python: Scripting tasks
 - File operations / Matplotlib / Basic Numpy (i) 
 - Applications of Python: Statistics / Data Analysis / Logging
 - Closing comments
Assistance installing on your own device
 
Interactive sessions, marked (i)
 
Afterwards: assistance available with installs
 
Day 2 - Tooling
Prerequisites: Day 1 -or- basic familiarity with Python concepts (if structures, opening files, preferably numpy and jupyter)
Session 1
 - Brief Recap of Day 1
 - Further control structures (lambdas / basic classes / exceptions) (i)
 - Pandas and basic data science (i) 
 - Applications of Python: Engineering & Simulation
 - Using an IDE (Spyder), environments; networks and threading (i) 
 - Applications of Python: Embedded / Control / Network
 - Closing presentation
 
Day 3 - Application
Prerequisites: Day 2
 - Basic Machine Learning (i) 
 - Applications of Python: Big Science & Bioinformatics 
 - Preparing for an Audience: Data Visualization (i) 
 - Reproducible Science
 - Geospatial & Images (i)
 - Applications of Python: Geography
 - Closing presentation

Please note that specific topics are subject to some change, particularly due to timing constraints and group needs. If you have a particular subject above that you are keen to ensure is covered, please ask in advance.   
Interactive notes will be available online (Github). Interactive sessions are marked (i)
Afterwards: individual questions; PhD project-related pointers (at least for survey respondents in Day 1)