# Informatics 2 syllabus

Contemporary usage of Python

4 SWS, 5 ECTS, in degree program {abbr}`LSI (Life Science Informatics)` Master and {abbr}`ICS (International Computer Science)`

## Intended learning outcomes

The purpose of the course is for you (the student) to learn to:

Professional competences:

- outline fundamental features of the Python programming language
- understand the advantages of object-oriented and functional programming 
- know different request types to access web resources
- list useful libraries from the standard library

Methodological competences:

- implement programs for string processing
- leverage the interactive interpreter for short computing tasks
- use object-oriented programming to breakdown a program into classes
- use functional programming to write shorter code
- implement programs for interacting with web APIs
- carry out simple image processing tasks
- leverage Numpy to conveniently work with matrices 
- use an unknown library by reading its documentation

Social competences

- cooperate in a pair programming setting
- evaluate someone else's work and give constructive feedback (e.g., in context of peer-assessed exercises)

## Prerequisites

- Computer science fundamentals (e.g., information, hardware, software, operating systems, shells, algorithms)
- Fundamental programming tools (e.g, control flow, data structures, functions)

## Content (what we do to reach the learning outcomes)

Most of the contents are based on the course [CS41: The Python Programming Language](https://stanfordpython.com) from Stanford University.

- Python basics:

  - Interactive interpreter
  - Comments
  - Variables and types
  - Numbers and Booleans
  - Strings and lists
  - Console I/O
  - Control Flow
  - Loops
  - Functions
  - Assignment Expressions
  
- Data structures:
  
  - `list`
  - `dict`
  - `tuple`
  - `set`
  
- Object-oriented Python:
  
  - errors and exceptions
    - *easier to ask for forgiveness than permission* (EAFP) vs *look before you leap* (LBYL)
  - data model
  - classes
  - exceptions as classes
  
- Functions:
  
  - namespaces and scope
  - Python Functions
  - (variadic) arguments
  - Parameter ordering
  
- Functional programming:
  
  - meaning
  - first-class functions
  - `lambda`s
  - iterators and generators
  - `map` and `filter`
  - decorators
  
- Python & the Web:
  
  - HTTP
  - requests library
  -  working with images
  - creating a web interface for your app using Flask library
  
- Numpy:
  
  - what is a matrix?
  - why are matrices useful?
  - n-dimensional array `ndarray`
  - axes and shapes
  - matrix operations
  - statistical methods
  - parameter fitting example
  
- Standard library and third-party libraries

## Didactic methods

To reach the learning outcomes we will use the following didactic methods:

- [Flipped classroom](https://en.wikipedia.org/wiki/Flipped_classroom)
- Labs with feedback sessions
- [Pair programming](https://en.wikipedia.org/wiki/Pair_programming)

## Grading

Written exam 90 min.

The examination is based on the intended learning outcomes.

## Materials

- Lecture videos in THD's Moodle
  - alternatively: [Stanfordpython course reader](https://github.com/stanfordpython/course-reader)
- [Slides that accompany the videos](https://mygit.th-deg.de/lsi/inf2-notes/-/jobs/artifacts/master/browse?job=build). These are tailored for discussions during the class.
- Exercise notebooks in THD's Moodle
- Previous Exams 
  - [Instructor versions](https://mygit.th-deg.de/lsi/nbgrader-notebooks/inf2-exams-source)
  - [Student versions](https://mygit.th-deg.de/lsi/nbgrader-notebooks/-/jobs/artifacts/main/browse/inf2-exams-release?job=build)

Additional:

- [The Python Tutorial](https://docs.python.org/3/tutorial)

## Time & Room

This course takes place only in summer semester. For time and room refer to [Thabella LSI-2](https://thabella.th-deg.de/thabella/opn/pdf/page/LSI-2).

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## Applicability in this and other programs

- Data mining and machine learning
- Bioinformatics algorithms and data structures
- Data visualization
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