mszell/introdatasci
Course materials for: Introduction to Data Science and Programming
| repo name | mszell/introdatasci |
| repo link | https://github.com/mszell/introdatasci |
| homepage | https://learnit.itu.dk/local/coursebase/view.php?ciid=282 |
| language | Jupyter Notebook |
| size (curr.) | 315096 kB |
| stars (curr.) | 80 |
| created | 2019-12-04 |
| license | |
Course materials for: Introduction to Data Science and Programming
These course materials cover the second half of the course held in fall 2019, improved over fall 2020, at IT University of Copenhagen. Public course pages:
2019: https://learnit.itu.dk/local/coursebase/view.php?ciid=282
2020: https://learnit.itu.dk/local/coursebase/view.php?ciid=522
Topics
The covered topics are:
- Array programming with numpy
- Single variable analysis
- Normal distributions
- Data relationships
- Simulation and top-down design
- Object-oriented programming
- Code optimization
- Introduction to network science
- Skewed data
- Graph properties
- Network models
- Network analysis and visualization
- Graph algorithms
These topics come after the first half of the course (not included here) which covers programming fundamentals in Python.
Sources
The course materials were adapted from a number of sources. All materials were used for educational, non-commercial reasons only. Feel free to use as you wish for the same purpose, at your own risk. For other re-use questions please consult the license of the respective source:
- Scientific Python course by Roberta Sinatra
- A lecture of J.R. Johansson (http://jrjohansson.github.io)
- Python for Data Analysis by Wes McKinney
- Introduction to the practice of statistics by D.S. Moore and G.R. McCabe
- Python Programming by John Zelle
- https://www.thedigitalcatonline.com/blog/2015/03/14/python-3-oop-notebooks/
- https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/classes
- https://www.thedigitalcatonline.com/blog/2015/03/14/python-3-oop-notebooks/
- https://towardsdatascience.com/speed-up-jupyter-notebooks-20716cbe2025
- https://jakevdp.github.io/PythonDataScienceHandbook/01.07-timing-and-profiling.html
- https://people.duke.edu/~ccc14/sta-663/MakingCodeFast.html
- Network Science by A.L. Barabasi
- Networks: An Introduction by M.E.J. Newman
- Bruno Gonçalves / Data4Sci: https://github.com/DataForScience/Networks
- James Bagrow: http://bagrow.com/dsv/
- A network science class by Sean Cornelius and Emma Thompson
