This repository contains all notebooks for the integrated course "Data Analysis with Python", held at the Salzburg Unviersity of Applied Sciences (SUAS).
Instructor: M. Uray, Office Hours: see department website
| Course Titel | Data Analysis with Python |
|---|---|
| Semester | 5. Semester |
| ECTS/SWS | 3 ECTS / 2 SWS |
| Course Type | Lecture with integrated project work (ILV) |
| Course Description | Introduction to Python. Functions, classes and exceptions; simple I/O and the most important standard modules. Python IDEs and frameworks for computation (partly cloud-based), special tool-boxes (pandas, matplotlib, numpy, scipy, scikit-learn). Tool-boxes (pandas, matplotlib, numpy, Scipy, scikit-learn) and scripting of these, implementation of classical of classical exploratory data analysis and presentation of the results, tSNE or geoplots, display of signals and images. Outlook: Export of data and graphics, crawling of data from the internet, construction of data sets, simple GUI elements |
| Course Outcomes | Students are able to solve simple problems that they know from other programming languages using the Python language. They can create independent scripts as well as notebooks and know the advantages and disadvantages of both. The students know the various libraries and frameworks for evaluating different data and can use these applications to read data and can use these applications to read clean, process, and display data. They know the different categories of data and how they can be visualized. The students know about the components of data sets and can easily write programs that collect data from the Internet or devices. |
| Topic | Notebook | |
|---|---|---|
| Lec 1 | Introduction, Python and Basic Operations | Lecture/01_BasicOperations.ipynb |
| Lec 2 | Functions, Classes and Data Structures | Lecture/02_FunctionsClassesDataStructures.ipynb |
| Lec 3 | Numpy and Pandas | Lecture/03_NumpyPandas.ipynb |
| Lec 4 | Matplotlib and Seaborn | Lecture/04_MatplotlibSeaborn.ipynb |
| Lab 1 | Categorial Data | Lab/01_CategoricalData.ipynb |
| Pr 1 | Introduction to the Project | Project/FinalProject.ipynb |
| Lab 2 | Maps | Lab/02_Maps.ipynb |
| Lab 3 | Continous Data | Lab/03_ContinuousData.ipynb |
| Lec 5 | Advanced Python | Lecture/05_AdvancedPython.ipynb |
| Lab 4 | Timeseries Data | Lab/04_TimeseriesData.ipynb |
| Lec 6 | Basic Machine Learning (scikit-learn) | Lecture/06_MachineLearning-Basicssklearn.ipynb |
| Lab 5 | Classification and Prediction | Lab/05_Classification.ipynb |