Advanced Practical Course Data Science (Summer 2021) - Revision history

Jzhang: /* Schedule */

2021-06-03T16:28:40Z

Schedule

← Older revision		Revision as of 18:28, 3 June 2021
Line 66:		Line 66:
	\|-		\|-
	\| align="right" \| 17.06.2021		\| align="right" \| 17.06.2021
	\| No lecture ~~// Task 2 report submission~~		\| No lecture
	\|-		\|-
	\| align="right" \| 24.06.2021		\| align="right" \| 24.06.2021
	\| // Task 3: release		\| // Task 3: release // Task 2 report submission
	\|-		\|-
	\| align="right" \| 01.07.2021		\| align="right" \| 01.07.2021

Jzhang: /* Schedule */

2021-04-12T23:34:15Z

Schedule

← Older revision		Revision as of 01:34, 13 April 2021
Line 42:		Line 42:
	\|-		\|-
	\| align="right" \| 22.04.2021		\| align="right" \| 22.04.2021
	\| ~~Lecture 2: The Python Data Science Stack - Task 1: Release~~		\| No lecture (Girls Day)
	\|-		\|-
	\| align="right" \| 29.04.2021		\| align="right" \| 29.04.2021
	\| ~~No lecture~~		\| Lecture 2: The Python Data Science Stack - Task 1: Release
	\|-		\|-
	\| align="right" \| 06.05.2021		\| align="right" \| 06.05.2021

Jzhang at 23:28, 12 April 2021

2021-04-12T23:28:57Z

← Older revision		Revision as of 01:28, 13 April 2021
Line 31:		Line 31:
	*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)
			\|-
			\|}

Sadhata: /* Details */

2021-03-22T14:51:35Z

Details

← Older revision		Revision as of 16:51, 22 March 2021
Line 6:		Line 6:
	\|credits=180h, 6 ECTS		\|credits=180h, 6 ECTS
	\|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke		\|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~~]; [http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]		\|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]
	\|ta=[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 16:00-18:00		\|time=Thursday 16:00-18:00

Woelk1: /* Details */

2021-03-16T09:04:39Z

Details

← Older revision		Revision as of 11:04, 16 March 2021
Line 8:		Line 8:
	\|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]; [http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]		\|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]; [http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]
	\|ta=[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=		\|time=Thursday 16:00-18:00
	\|place=2.101(online)		\|place=2.101(online)
	\|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=~~256838~~&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung~~&k_semester.semid=20202&idcol=k_semester.semid&idval=20202&getglobal=semester~~]		\|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=267540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
	}}		}}

Woelk1: /* Details */

2021-03-12T09:22:23Z

Details

← Older revision		Revision as of 11:22, 12 March 2021
Line 8:		Line 8:
	\|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]; [http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]		\|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]; [http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]
	\|ta=[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=~~Friday 16:00-18:00~~		\|time=
	\|place=2.101(online)		\|place=2.101(online)
	\|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=256838&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20202&idcol=k_semester.semid&idval=20202&getglobal=semester]		\|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=256838&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20202&idcol=k_semester.semid&idval=20202&getglobal=semester]

Woelk1: /* Schedule */

2021-03-12T09:22:04Z

Schedule

← Older revision		Revision as of 11:22, 12 March 2021
Line 31:		Line 31:
	*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" \| 06.11.2020~~
	~~\| Lecture 1: Introduction & The Data Science Pipeline~~
	\|-
	~~\| align="right" \| 13.11.2020~~
	~~\| Lecture 2: The Python Data Science Stack - Task 1: Release~~
	\|-
	~~\| align="right" \| 20.11.2020~~
	~~\| No lecture~~
	\|-
	~~\| align="right" \| 27.11.2020~~
	~~\| Task 1: Intermediate meeting~~
	\|-
	~~\| align="right" \| 04.12.2020~~
	~~\| // Task 1 report submission //Task 2: release~~
	\|-
	~~\| align="right" \| 11.12.2020~~
	~~\| No lecture~~
	\|-
	~~\| align="right" \| 18.12.2020~~
	~~\| Lecture 3: Advanced Algorithms for Data Science~~
	\|-
	~~\| align="right" \| 25.12.2020~~
	~~\| No lecture // Task 2 report submission~~
	\|-
	~~\| align="right" \| 01.01.2021~~
	~~\| No lecture (holidays）~~
	\|-
	~~\| align="right" \| 08.01.2021~~
	~~\| No lecture (holidays)~~
	\|-
	~~\| align="right" \| 15.01.2021~~
	~~\| Lecture 4: Evaluation and Tuning of Models // Task 3: release~~
	\|-
	~~\| align="right" \| 22.01.2021~~
	~~\| No lecture~~
	\|-
	~~\| align="right" \| 29.01.2021~~
	~~\| Task 3: Intermediate meeting~~
	\|-
	~~\| align="right" \| 05.02.2021~~
	~~\| Final Presentation (TBD)~~
	\|-
	~~\| align="right" \| 12.02.2021~~
	~~\| Final Presentation (TBD)~~
	\|-
	~~\| align="right" \| 31.03.2021~~
	~~\| Final Report deadline (Including report and code)~~
	\|-
	\|}

Woelk1: /* Announcement */

2021-03-12T09:21:41Z

Announcement

@@ Line 12: / Line 12: @@
 |univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=256838&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20202&idcol=k_semester.semid&idval=20202&getglobal=semester]
 }}
 ==Course Organization==

Woelk1: /* Details */

2021-03-12T09:18:49Z

Details

← Older revision		Revision as of 11:18, 12 March 2021
Line 6:		Line 6:
	\|credits=180h, 6 ECTS		\|credits=180h, 6 ECTS
	\|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke		\|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]		\|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]; [http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]
	\|ta=[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=Friday 16:00-18:00		\|time=Friday 16:00-18:00
	\|place=2.101(online)		\|place=2.101(online)
	\|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=256838&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&idcol=k_semester.semid&idval=20202&getglobal=semester~~&htmlBodyOnly=true~~]		\|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=256838&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20202&idcol=k_semester.semid&idval=20202&getglobal=semester]
	}}		}}

Woelk1: Created page with "{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} == Details == {{CourseDetails |credits=180h, ..."

2021-03-12T09:16:26Z

Created page with "{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} == Details == {{CourseDetails |credits=180h, ..."

New page

{{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=Friday 16:00-18:00
|place=2.101(online)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=256838&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&idcol=k_semester.semid&idval=20202&getglobal=semester&htmlBodyOnly=true]
}}

==Announcement==

'''20.10.2020 Our first lecture will be on 06.11.2020 through the Meeings tool on StudIP'''

'''30.09.2020 Our course in this semeter will be online'''

'''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, when and where are the lectures, how is presentation arranged). 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" | 06.11.2020
| Lecture 1: Introduction & The Data Science Pipeline
|-
| align="right" | 13.11.2020
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
| align="right" | 20.11.2020
| No lecture
|-
| align="right" | 27.11.2020
| Task 1: Intermediate meeting
|-
| align="right" | 04.12.2020
| // Task 1 report submission //Task 2: release
|-
| align="right" | 11.12.2020
| No lecture
|-
| align="right" | 18.12.2020
| Lecture 3: Advanced Algorithms for Data Science
|-
| align="right" | 25.12.2020
| No lecture // Task 2 report submission
|-
| align="right" | 01.01.2021
| No lecture (holidays）
|-
| align="right" | 08.01.2021
| No lecture (holidays)
|-
| align="right" | 15.01.2021
| Lecture 4: Evaluation and Tuning of Models // Task 3: release
|-
| align="right" | 22.01.2021
| No lecture
|-
| align="right" | 29.01.2021
| Task 3: Intermediate meeting
|-
| align="right" | 05.02.2021
| Final Presentation (TBD)
|-
| align="right" | 12.02.2021
| Final Presentation (TBD)
|-
| align="right" | 31.03.2021
| Final Report deadline (Including report and code)
|-
|}

← Older revision		Revision as of 11:22, 12 March 2021
Line 31:		Line 31:
	*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" \| 06.11.2020~~
	~~\| Lecture 1: Introduction & The Data Science Pipeline~~
	\|-
	~~\| align="right" \| 13.11.2020~~
	~~\| Lecture 2: The Python Data Science Stack - Task 1: Release~~
	\|-
	~~\| align="right" \| 20.11.2020~~
	~~\| No lecture~~
	\|-
	~~\| align="right" \| 27.11.2020~~
	~~\| Task 1: Intermediate meeting~~
	\|-
	~~\| align="right" \| 04.12.2020~~
	~~\| // Task 1 report submission //Task 2: release~~
	\|-
	~~\| align="right" \| 11.12.2020~~
	~~\| No lecture~~
	\|-
	~~\| align="right" \| 18.12.2020~~
	~~\| Lecture 3: Advanced Algorithms for Data Science~~
	\|-
	~~\| align="right" \| 25.12.2020~~
	~~\| No lecture // Task 2 report submission~~
	\|-
	~~\| align="right" \| 01.01.2021~~
	~~\| No lecture (holidays）~~
	\|-
	~~\| align="right" \| 08.01.2021~~
	~~\| No lecture (holidays)~~
	\|-
	~~\| align="right" \| 15.01.2021~~
	~~\| Lecture 4: Evaluation and Tuning of Models // Task 3: release~~
	\|-
	~~\| align="right" \| 22.01.2021~~
	~~\| No lecture~~
	\|-
	~~\| align="right" \| 29.01.2021~~
	~~\| Task 3: Intermediate meeting~~
	\|-
	~~\| align="right" \| 05.02.2021~~
	~~\| Final Presentation (TBD)~~
	\|-
	~~\| align="right" \| 12.02.2021~~
	~~\| Final Presentation (TBD)~~
	\|-
	~~\| align="right" \| 31.03.2021~~
	~~\| Final Report deadline (Including report and code)~~
	\|-
	\|}