Syllabus

LEARNING OUTCOMES

After the course you understand the role of conceptual modeling in managing data, and know the commonly used database modeling and querying languages. You can design simple databases and write queries.

Credits: 5

Schedule: 09.04.2024 - 11.06.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Nitin Sawhney

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

• valid for whole curriculum period:

  Basic concepts and methods in database systems. Relational databases: relational algebra, UML-design, Basic concepts and methods in database systems. Relational databases: relational algebra, UML-design, normalization and SQL.

• applies in this implementation

  Course Description: This is an introductory course on relational databases designed for Data Science BSc students; so no prior knowledge of databases is assumed. The course covers the fundamentals of relational algebra, the design of the relational schema including the Unified Modeling Language (UML), functional dependency and normal forms, the concept of transactions, creating SQL tables (including indexes), and using SQL to query the database. 
  

  Following the course, the students will have the know-how to design and implement relational databases that meet the normalization rules. Moreover, the student should be able to use SQL to write and run various types of queries so as to extract the desired data from the database, an essential part when analyzing data. In particular, the course will draw on relevant examples to prepare students to apply the principles of relational databases to projects in data science.
  

  
  
  

Assessment Methods and Criteria

• valid for whole curriculum period:

  Assignemtns and Quizzes. 

Workload

• valid for whole curriculum period:

  Lectures 69h, independent work 59h.

DETAILS

Study Material

• valid for whole curriculum period:

  A. Jung, "Machine Learning: The Basics," Springer, 2022. 

Substitutes for Courses

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  Prerequisites

FURTHER INFORMATION

Further Information

• valid for whole curriculum period:

  Teaching Language: English

  Teaching Period:

  2022-2023 Spring V
  2023-2024 Spring IV-V

  Check starting date in the course implementation.

Description