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Business Data Analytics: Application and Tools

Business Data Analytics: Application and Tools
type: Vorlesung (V)
semester: SS 2020
time: 2020-04-20
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)


2020-04-20
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-04-27
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-04-27
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-05-04
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-05-04
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-05-11
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-05-11
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-05-18
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-05-18
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-05-25
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-05-25
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-06-08
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-06-08
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-06-15
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-06-15
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-06-22
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-06-22
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-06-29
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-06-29
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-07-06
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-07-06
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-07-13
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-07-13
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2020-07-20
09:45 - 11:15 wöchentlich
11.10 Kleiner ETI HS
11.10 Elektrotechnisches Institut (ETI)

2020-07-20
09:45 - 11:15 wöchentlich
20.30 SR 0.019
20.30 Kollegiengebäude Mathematik, Englerstr. 2


lecturer: Dr. Philipp Staudt
David Dann
Nicolas Haubner
sws: 2
lv-no.: <a target="lvn" href="https://campus.studium.kit.edu/events/BXaUFypOSMKRD9B3aWmHMw">2540466</a>
Prerequisites

Prior knowledge of object oriented programming and statistics is recommended.

Description

The ongoing digitalization and digitization of businesses, industries and societies is generating vast amounts of data. Hence, researchers and businesses are facing increasing pressure to build capabilities to cope with the data and generate value from the contained but yet to be discovered knowledge, insights and information. Researchers and practitioners tackling this task are referred to as data scientists and need skills at the intersection of programming, statistics and development operations. This course provides a hands-on perspective on these fields.

Content of teaching

The aim of this course is to introduce practical foundations, concepts, tools and current practice of Analytics from a data scientist’s perspective. The lecture is complemented with an Analytics challenge that is based on real-world data from research projects. The students immediately apply their newly acquired knowledge and learn to use a range of open source tools to solve the challenge.

Content:

  • Conceptual and theoretical Foundations
  • Programming languages common in data science
  • Data acquisition, pre-processing
  • Basics of data organization and DevOps
  • Tool chain selection and automation
  • Open source analytics frameworks and data processing infrastructures
  • Applied analytics challenge (based on a current research project or a cooperation with an industry partner)
Workload

The total workload for this course is approximately 135 hours.

Aim

The students

  • understand the foundations of key methods, processes and programming languages for data science projects
  • explore key capabilities of state-of-the-art open source frameworks and tools
  • learn how to successfully manage data, code and analytical models
  • learn professional, tool supported communication of analysis results and generated insights
  • get hands-on experience by working with real-world data and a selection of frameworks and tools