The purpose of this subject is a) to teach the students the programming language Python 3 and b) to apply the language, its features, and third party libraries to “get things done”. That is, the course aims to enable the students to collect, process, and visualize data using various techniques. The main goal is to allow the students to use Python as a tool in their later careers to quickly analyse problems, find answers to business questions, etc. Objectives
We will learn about Python’s basic data types, basic data structures, control structures, expressions, statements, operators, and program operation. Additionally, we will learn how to use “Jupyter Notebooks”, as interactive programming environment and its application for knowledge presentation. Data Collection We will learn how to automatically download files from the web, scrape text and images from web pages, and how to read and write various file formats, such as text files, CSV files, JSON files, Excel files, etc. Data Visualization We will learn how to plot data into various plot styles, such as, line plots, scatter plots, bar plots, on maps, etc. using different technologies, such as “matplotlib”, “pygal”, and “bokeh”. Data Science We will apply some common algorithms in data science, such as “KMeans”, “Mean Shift”, “Page Rank”, etc. Additionally, we will learn how to make use of the most prominent science libraries “NumPy” and “Pandas” for effective and efficient data processing. Image Processing We will have a look at basic image processing tasks, such as, reading image files, morphological operations, colour spaces, and the application of “OpenCV” to process images and streams of images automatically. Automation On top of automatic web scraping, we will have a look at UI automation and the “Selenium” framework to let computers perform the boring and repetitive tasks.
After completing this course, the students will be able to:
Due to the project-based design of the course, the students will become competent in collecting various types of data, formulate problems about this data, implement solutions to given problem statements, and to present results on an abstract as well as technical level. Additionally, the students will gain experiences in code reviews by reviewing Python code of their fellow students.
See lecture notes.
See lecture notes.
All above
All above
All above
All above
You earn studypoints (SP) by solving the assignments from the list below. There are in total 9 assignments during the course. This number may chage as itdepends on the precise amount of groups to be formed in the first session.
Task | Hand-out | Hand-in | Studypoints | URL |
---|---|---|---|---|
Selection of Datasets | 28. Aug. 2018 | 3. Sep. 12:00 | 10 mandatory | - |
Group Gifted Perception + Review |
18. Feb. 2018 | 23. Feb. 23:55 | 10 | https://github.com/HawkDon/Python_Assignment1 |
Group Foolish Supermarket + Review |
25. Sep. 2018 | 30. Feb. 23:55 | 10 | https://github.com/Zurina/Dataset/blob/master/README.md |
Group Plain Product + Review |
2. Oct. 2018 | 7. Oct. 23:55 | 10 | https://github.com/MikkelHansen95/dataset |
Group College Impossible + Review |
8. Oct. 2018 | 21. Oct. 23:55 | 10 | https://github.com/BoMarconiHenriksen/impossibleCollegeDataset |
Project 1 + Review | - | - | 10 | |
Project 2 + Review | - | - | 10 | |
Project 3 + Review | - | - | 10 | |
Project Hand-in | - | 19. Dec. 2018 (23:55) | - |
As you can see in the table above can you earn 130 SP for all the exercises. You have to get at least 80% of the SP, which is a requirement to be eligible for the exam.
The project is mandatory for the exam, where the project is considered for grading (eksamensgrundlag).
One single grade is given according to the 7-point grading scale.
The student must fulfill the mandatory learning activities.