Jzhang:

Advanced Practical Course Data Science (Summer 2020)

2020-07-27T08:11:48Z

2020-07-25T22:45:28Z

Jzhang:

{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang]
|ta=[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang]
|time=Thursday, 16-18
|place=Ifi 2.101
|univz=[https://univz.uni-goettingen.de/qisserver//rds?state=verpublish&status=init&vmfile=no&publishid=248178&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&idcol=k_semester.semid&idval=20201&getglobal=semester&htmlBodyOnly=true]
}}

==Announcement==

Due to the recent situations in the context of Covid-19, how to arrange the lectures in this winter semester is still not determined (online or face-to face lecturing). Any new information will be updated here in time, please check this webpage periodically to get the newest information.

==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 (September 30).

==Prerequisites==
*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

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | 23.04.2020
| Lecture 1: Introduction & The Data Science Pipeline
|-
| align="right" | 30.04.2020
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
| align="right" | 07.05.2020
| No lecture
|-
| align="right" | 14.05.2020
| Task 1: Intermediate meeting
|-
| align="right" | 21.05.2020
| No lecture // Task 1 report submission
|-
| align="right" | 28.05.2020
| Lecture 3: Advanced Algorithms for Data Science // Task 2: release
|-
| align="right" | 04.06.2020
| No lecture
|-
| align="right" | 11.06.2020
| Lecture 4: Evaluation and Tuning of Models // Task 2 report submission // Task 3: release
|-
| align="right" | 18.06.2020
| No lecture
|-
| align="right" | 25.06.2020
| No lecture
|-
| align="right" | 02.07.2020
| Task 3: Intermediate meeting I
|-
| align="right" | 09.07.2020
| No lecture
|-
| align="right" | 16.07.2020
| Task 3: Intermediate meeting II ('''FlexNow Registration Deadline''')
|-
| align="right" | 23.07.2020
| No lecture
|-
| align="right" | 30.07.2020 (online)
| Task 3: Presentations (Final Presentation)
|-
| align="right" | 20.08.2020
| Final Report deadline (Including report and code)
|-
|}

Advanced Practical Course Data Science (Winter 2020/2021)

2020-07-25T22:36:22Z

Jzhang:

{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://134.76.18.81/?q=people/dr-yali-yuan Dr. Yali Yuan]
|ta=[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang]
|time=Thursday, 16-18
|place=Ifi 2.101
|univz=[https://univz.uni-goettingen.de/qisserver//rds?state=verpublish&status=init&vmfile=no&publishid=248178&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&idcol=k_semester.semid&idval=20201&getglobal=semester&htmlBodyOnly=true]
}}

==Announcement==
22.04.2020 : We decide to use the meetings module in studip for our lectures. Please check the message in Studip.

Due to the recent recommendations in the context of Covid-19, we have to defer the start of the lectures of this course to 23rd April 2020. Currently, this course is scheduled in a purely online, non-face-to-face way. We plan to use some tools and platforms, e.g., zoom or DFNconf, see: https://www.uni-goettingen.de/en/622774.html. '''Please register into studIP in advance. The registration deadline is at 23:59 pm on 22nd April 2020.''' I will announce which tool will be used before our lecture start.

==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 (September 30).

==Prerequisites==
*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

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | 23.04.2020
| Lecture 1: Introduction & The Data Science Pipeline
|-
| align="right" | 30.04.2020
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
| align="right" | 07.05.2020
| No lecture
|-
| align="right" | 14.05.2020
| Task 1: Intermediate meeting
|-
| align="right" | 21.05.2020
| No lecture // Task 1 report submission
|-
| align="right" | 28.05.2020
| Lecture 3: Advanced Algorithms for Data Science // Task 2: release
|-
| align="right" | 04.06.2020
| No lecture
|-
| align="right" | 11.06.2020
| Lecture 4: Evaluation and Tuning of Models // Task 2 report submission // Task 3: release
|-
| align="right" | 18.06.2020
| No lecture
|-
| align="right" | 25.06.2020
| No lecture
|-
| align="right" | 02.07.2020
| Task 3: Intermediate meeting I
|-
| align="right" | 09.07.2020
| No lecture
|-
| align="right" | 16.07.2020
| Task 3: Intermediate meeting II ('''FlexNow Registration Deadline''')
|-
| align="right" | 23.07.2020
| No lecture
|-
| align="right" | 30.07.2020 (online)
| Task 3: Presentations (Final Presentation)
|-
| align="right" | 20.08.2020
| Final Report deadline (Including report and code)
|-
|}