Advanced Practical Course Data Science (Winter 2019/2020)
Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there. |
Details
Workload/ECTS Credits: | 180h, 6 ECTS |
Module: | M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke |
Lecturer: | Prof. Xiaoming Fu; Dr. Yali Yuan |
Teaching assistant: | MSc. Jiaquan Zhang; Shuai Xu |
Time: | Friday, 16-18 |
Place: | Ifi 2.101 |
UniVZ | link |
Course Organization
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.
While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:
- Introduction to the practical machine learning pipeline
- Exploratory data analysis
- The Python Data Science stack
- How to deal with unbalanced data
- Advanced algorithms for Data Science (an overview of competition winning algorithms)
- Parameter tuning for predictive models
Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester. Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester.
Prerequisites
- You are highly recommended to have completed a course on Data Science (e.g., "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
Schedule
When? | What? |
25.10.2019 | Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release |
01.11.2019 | No lecture |
08.11.2019 | Lecture 2: The Python Data Science Stack |
15.11.2019 | Task 1: Intermediate meeting |
22.11.2019 | Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission // Task 2: release |
29.11.2019 | No lecture |
06.12.2019 | No lecture |
13.12.2019 | Lecture 4: Evaluation and Tuning of Models // Task 2 report submission // Task 3: release |
20.12.2019 | No lecture |
27.12.2019 | No lecture |
03.01.2020 | No lecture |
10.01.2020 | Task 3: Intermediate meeting I |
17.01.2020 | No lecture |
24.01.2020 | No lecture |
31.01.2020 | Task 3: Intermediate meeting II |
31.01.2020 07.02.2020 | Task 3: Presentations (Final Presentation) |
05.05.2020 | Final Report deadline (Including report and code) |