Database Use and Design (Spring 2021)

Abstract

The course gives an introduction and overview of data engineering techniques and practices.

Description

A data ‘revolution’ is underway, a fourth industrial revolution, one that is already reshaping how knowledge is produced, business conducted, and governance enacted. Data has traditionally been time-consuming and costly to generate, analyze, interpret, and generally provided a relatively static and coarse snapshot of phenomena.

This state of affairs is changing now. Rather than being scarce and limited in scope, data production is increasingly becoming a ‘deluge’ i.e. a vast flow of real-time, varied, resolute, and relational data relatively low in cost. 

Data is increasingly becoming open as well. This data abundance (as opposed to data scarcity) is reshaping how we work with, circulate, trade, analyze, and exploit data. This development is founded on the latest wave of information sources and communication technologies, such as collective intelligence, artificial intelligence/machine learning, big data harvested from social media and the internet, or through the internet of things. Data is produced through mobile phones, distributed and cloud computing, open-source platforms, crowdsourcing platforms, and the plethora of digital devices encountered in homes, workplaces, public spaces, and inter-worked sensors and devices. 

These technical infrastructures lead to evermore aspects of everyday life – work, consumption, travel, communication, and leisure – being captured as data. Moreover, they are re-configuring the production, circulation, and interpretation of data. 

The students will gain an understanding of the technical aspects of data management and the opportunities and risks they create for organizations.

During the course, the students will relate and work with the (changing) nature of database use and design, including:

• Data purpose, representation and modelling 

• Data collection, retrieval, and storage 

• Data Engineering and Data Processes 

• Architecture of Unbundled Data Systems

Formal prerequisites

Python programming is a prerequisite.

This course is part of the second semester in the bachelor's degree in Global Business Informatics.

Intended learning outcomes

After the course, the student should be able to:

• Explain the difference between the relational and non-relational data models
• Describe the architecture and components of a database system
• Design an ER model and a relational model in a concrete scenario
• Define SQL queries in a concrete scenario
• Define Python programme for batch data processing
• Design a data management process
• Explain the difference between different data sourcing methods

Learning activities

Lectures and exercises. The exercises will essentially be based on statistical analysis of collected crowdsourcing data and programming tasks, in Python and SQL.

Course literature

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences.

Author: Rob Kitchin, R., 1^st Edition. 

Database Systems: Design, Implementation, & Management.

Authors: Carlos Coronel, Steven Morris, 13^th Edition.

Plus uploaded readings to course page in LearnIT.

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 40%
• Lectures: 20%
• Exercises: 20%
• Exam with preparation: 20%

Ordinary exam

Exam type:
C: Submission of written work, Internal (7-point scale)
Exam variation:
C1G: Submission of written work for groups

Time and date