Data Science: Predictive Analytics

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TU Dresden | Semester overlapping

Data Science: Predictive Analytics

for questions please contact

pa_isd@tu-dresden.de

The final classification for the exam is uploaded in the Opal document file.

Consultation with Prof. Brendel : on request
(please contact kerstin.petzold@tu-dresden.de for an appointment)

Course description

The module deals with the fundamentals and principles of supervised procedures for generating prediction models on the basis of structured data with the help of procedures from the field of machine learning using analytical information systems. Students are able to apply these methods to various practical examples and to evaluate, interpret and critically question the results. In particular, students will built up an understanding of the ideal-typical knowledge-discovery-in-databases process and its components in general, and modelling and data pre-processing in particular. Furthermore, the students will be able to appropriately approach and solve predictive modelling problems on basis of complex data sets and thus provide adequate decision support. They will have the ability to deal with selected application systems of machine learning in particular and predictive modelling in general.

Course facts

  • Format: e-Lectures & exercises
  • Language: English
  • Semester: every winter term
  • Scope: 5 CP

Important information

The number of participants is limited to 60. First come first serve. There will be a waiting list.

Due to the unclear Covid-19 situation, no attendance events are planned until further notice.

A kickoff meeting with all the information for the course as well as the online exercises will be held via MS-Teams. A corresponding link will be available for enrolled students in the link list.

DataCamp

In addition to the excercises, we offer 13 DataCamp courses. The participation is not mandatory but it is highly recommended because the DataCamp courses entail the necessary programming knowledge for the excercises. The exam will take place on the same day as ADA and DSDT. Please consider this in your semester planning.

Final exam

The exam will take place as online exam in Opal (ONYX). Further information will be given during the course.

Timeslot

Wednesday 5.DS 14:50 - 16:20

Timetable

Agenda Topic Weekday date start end
Kickoff Kickoff Wednesday 19.10.2022 14:50 16:20
lecture 1 Introduction Data Science Wednesday 26.10.2022 upload
lecture 2 Introduction Data Science Part 2 Wednesday 02.11.2022 upload
lecture 3 Introduction to Predictive Modeling Wednesday 09.11.2022 upload
holiday - Wednesday 16.11.2022 -
lecture 4 Business Understanding Wednesday 23.11.2022 upload
lecture 5 Data Understanding Wednesday 30.11.2022 upload
exercise 1 Business and Data Understanding Wednesday 30.11.2022 14:50 16:20
lecture 6 Data Preparation Wednesday 07.12.2022 upload
lecture 7 Modeling Wednesday 14.12.2022 upload
exercise 2 Data Preparation Wednesday 04.01.2023 14:50 16:20
lecture 8 Linear Regression Wednesday 04.01.2023 upload
lecture 9 Logistic Regression Wednesday 11.01.2023 upload
lecture 10 Artificial Neural Networks Wednesday 18.01.2023 upload
lecture 11
 Evaluation Wednesday 25.01.2023 upload
exercise 3
 Regression Wednesday 25.01.2023 14:50 16:20
Q&A-Exam Q&A Exam Wednesday 01.02.2023 14:50 16:20
Exam Final Exam Wednesday 22.02.2023 Group 1 12:00 pm
 Group 2
2:30 pm

Access to e-lecture videos

After the kick-off there will be weekly e-lectures. These will be uploaded every Wednesday and enrolled students will be able to finde links to e-lecture videos in the link list. These videos will be hosted at videocampus sachsen
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