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Advanced Practical Course Data Science (Summer 2021): Difference between revisions
Advanced Practical Course Data Science (Summer 2021) (view source)
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*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.). | *You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.). | ||
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries | *Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries | ||
==Schedule== | |||
{| {{Prettytable|width=}} | |||
|- | |||
|{{Hl2}} |'''When?''' | |||
|{{Hl2}} |'''What?''' | |||
|- | |||
| align="right" | 15.04.2021 | |||
| Lecture 1: Introduction & The Data Science Pipeline | |||
|- | |||
| align="right" | 22.04.2021 | |||
| Lecture 2: The Python Data Science Stack - Task 1: Release | |||
|- | |||
| align="right" | 29.04.2021 | |||
| No lecture | |||
|- | |||
| align="right" | 06.05.2021 | |||
| Task 1: Intermediate meeting | |||
|- | |||
| align="right" | 13.05.2021 | |||
| No lecture (Ascension Day) | |||
|- | |||
| align="right" | 20.05.2021 | |||
| Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release | |||
|- | |||
| align="right" | 27.05.2021 | |||
| Lecture 4: Evaluation and Tuning of Models | |||
|- | |||
| align="right" | 06.03.2021 | |||
| No lecture | |||
|- | |||
| align="right" | 10.06.2021 | |||
| No lecture | |||
|- | |||
| align="right" | 17.06.2021 | |||
| No lecture // Task 2 report submission | |||
|- | |||
| align="right" | 24.06.2021 | |||
| // Task 3: release | |||
|- | |||
| align="right" | 01.07.2021 | |||
| No lecture | |||
|- | |||
| align="right" | 08.07.2021 | |||
| Task 3: Intermediate meeting | |||
|- | |||
| align="right" | 15.07.2021 | |||
| Final Presentation (TBD) | |||
|- | |||
| align="right" | 22.07.2021 | |||
| Final Presentation (TBD) | |||
|- | |||
| align="right" | 31.09.2021 | |||
| Final Report deadline (Including report and code) | |||
|- | |||
|} | |||