Data Engineering and Machine Learning Operations in Business

2022/2023

Content, progress and pedagogy of the module

This module intends to provide crucial supplementary knowledge, skills, and competencies to design, develop, and execute data science projects in business and research settings. This includes the acquisition, processing, and storage of typical real-world data within big data frameworks. Prospect students will learn how to query databases via an application programming interface (API), and how to work within common database frameworks suitable for structured, unstructured, dynamic, and big data. In addition, prospect students will learn how to refracture machine learning models and the necessary code and deploy it in web-based applications.

Insights and techniques learned in this module can be applied to real-world problems related to the deployment of machine learning models in end-to-end solutions, encompassing all steps from data acquisition to the application of machine learning models in production and service workflows.

Upon completion of the module students will have built a solid knowledge on processes, techniques, and workflows to provide functioning machine learning solutions in a real-world setting. Students will be capable of autonomously planning, managing, and executing complex machine learning projects, and provide client-facing application interfaces thereof.

Learning objectives

Knowledge

The objective is that the student after the module possesses the necessary knowledge on:

  • types, structures, and advantages of typical database solutions suitable for big data workflows.
  • principles and typical workflows of machine learning model deployment in production.
  • organisational challenges and typical solution approaches related to deployment.

Skills

The objective is that the student after the module possesses the necessary skills in:

  • populating, maintaining, and manipulating common databases suitable for big data workflows.
  • querying databases via common application programming interfaces.
  • deploying machine learning models for real time usage in local and web interfaces.

Competences

The objective is that the student after the module possesses the necessary competences in:

  • planning, managing, and executing complex end-to-end data science projects.
  • identifying possibilities to deploy machine learning models to real time and client facing applications.
  • evaluating data and machine learning projects, structures, and workflows in organizations.

Type of instruction

For information see § 17.

Exam

Prerequisite for enrollment for the exam

  • A prerequisite for participating in the exam is that the student has handed in written material.

Exams

Name of examData Engineering and Machine Learning Operations in Business
Type of exam
Oral exam based on a project
Group examination with max. 4 students.
ECTS10
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleDatateknik og ML operationer og maskinlæring i virksomheder
Module codeKADAT20225
Module typeCourse
Duration1 semester
SemesterSpring
ECTS10
Language of instructionEnglish
Location of the lectureCampus Aalborg
Responsible for the module

Organisation

Study BoardStudy Board of Economics and Business Administration
DepartmentAAU Business School
FacultyFaculty of Social Sciences and Humanities