This is the landing page for the class “Scientific Programming in Python” held in the years 2018-2020 at Osnabrück University.

Other pages

• Course Infrastructure
• Lectures-Repository
• Youtube-Channel
• latest version of the Dashboard
• Dashboard-Mockup
• Course Material

Recordings

All lecture recordings from 2018 – 2020 can be viewed on Youtube:

Lecture	YouTube 2018	Youtube 2019	Youtube 2020
1) Introduction	View	Part 1, Part 2	Organisation, Why Scientific Programming?, Our Path, Whirlwind Tour, Course Infrastructure, Setup: IDEs, Setup: Install locally, Setup: Jupyterlab, git intro, The shell, git practice, Howto Homework
2) Basic Python	View	Part 1, Part 2	Basic Types, Operators, Functions & Methods, Collections, Control Flow, Functions, Classes, Caveats
3) Advanced Python	View	Part 1, Part 2	Data Model, Iterators, Properties, Exceptions, Duck Typing, Factory Methods, Generators, Context Managers, Lambda Expressions, Comprehensions, Strings, Decorators
4) Numpy	View	Part 1, Part 2	Intro, Arrays, Masking, Math Operations, Indexing, Extending Arrays, Random Access, Printing,
5) Code Quality, IDEs, Debugging		Debugging in Pycharm (week 10)	Code Quality, Errors, Files&Imports, APIs, PyCharm, Debugging, PyCharm II,
6) Matplotlib	View	Part 1, Part 2	Intro, Plotting Functions, 2D Plots, Distributions, Annotations, Colors, Misc, Limits, Ticks, Legends
7) Basic Pandas	View	Part 1, Part 2	Series & Dataframes, Indexing & Assignment, Reading in, Ufuncs & Aggregations, GroupBy
8) Pandas: Cleaning Data	View	Part 1, Part 2	Missing Values, Deduplication, Categorical Data, Discretizing, Numericalizing, Pandas Plotting, Merging & Joining
9) Pandas: Analyzing Data & Time Series	View	Part 1, Part 2	Timestamps, Reading Time Series, Shifting & Windowing, Date Ranges
10) Statistical Visualization (ggplot, seaborn)	View	Part 1, Part 2	Intro, Step back, Library Comparison, Pandas Plotting, plotnine/ggplot, seaborn, seaborn&matplotlib,
11) Statistical Modeling (statsmodels)	View	Part 1, Part 2	Linear Models, Linear Regression, Regression: Sampling Distribution, NHST, Regression Assumptions, Multiple Regression,
12) Experiments (expyriment)	View	Part 1, Part 2	Intro, GUIs: PyGame, GUIs: Psychopy, GUIs: Expyriment, Experiment Design, Timing, Logging, Full Experiments, Outro, Shuffling
Mathematical computation using Scipy			Linear Algebra, Integration, Calculus, Signal Processing, Fourier Transform, Statistical Modeling
Study: A to Z, Scikit-Learn, Parallelism	View		Multiprocessing vs Multithreading, Multithreading, Multiprocessing.Process, Multiprocessing.Pool, High Level Multiprocessing
Interactive Visualization (Widgets, Altair)		View	Plotly, Jupyter Widgets, Altair
Data Exploration, Performance-Optimization		View	Exploratory Data Analysis (week 9), Data Voyager (week 10), Serialization, Numba, Jax, Cython

To view the Jupyter Notebooks corresponding to the videos of the respective years, access the lecture repository’s state from 2018 or 2019, respectively. To access the videos in the better Opencast-player, make sure you’re logged into Myuos before clicking the link.

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