Study Guide

Objective

After completing the course, the student can:
- run data engineering projects

Content

Data Engineering pipeline in practice

Materials

Teacher provided lecture material
Supporting public online material
All needed material (or at least a link to them) will be available in itslearning.

Teaching methods

Project work as a group of 3-4 students.

Exam schedules

There's no exam.

International connections

Project-based-learning. Theory is introduced by the teacher during the contact lessons and then the students apply the theory to a given project. The project is there to support learning the concepts introduced on theory lessons.

Student workload

Contact hours 33 h
Independent studying 102h, including:
- Studying the theory
- Applying theory to the group work project

Content scheduling

The course consists of an end-to-end data engineering project. We will use the concepts learnt on previous courses to work as a group to extract, pre-process, transform and load data into a system. The data is AI-enriched and shared through different interfaces. The work is documented throughout the project and a data management plan is produced for long-term use.

Further information

Itslearning and contact classes are the main communication channels used on this course.

The student is required to have a computer capable of running a simple Ubuntu virtual machine and basic skills to work with Ubuntu command line.

Evaluation scale

H-5

Assessment methods and criteria

Assessment is based on 0-100 scale of points.

Group work project is worth 78 points in total.

There are weekly reports each individual student makes to report the work they have done for the project during that week. These reports are worth 22 points in total.

You need to get at least 50% of the group work project points, and at least 50% of the points for the weekly reports to pass the course.