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Data Analytics Infrastructure

Code

IT-DAI1

Version

1.0

Offered by

ICT Engineering

ECTS

5

Prerequisites

General admittance requirements

Main purpose

The course introduces the student to selected topics in the design and implementation of infrastructure to support data analytics.
Within this area, the course will introduce students to different tools and techniques for data acquisition, cleansing and integration from different sources, data modelling for analytics and basic visualization.
 
This course description is identical with the former course named IT-CDI1
 

Knowledge

Having completed this course, students should be able to describe basic techniques within the field, and argue the choice and applicability of these for different use scenarios.

This includes:

  • Use scenarios for analytical data processing, differences to transactional processing
  • Types of analytical data processing, such as reporting and visualization
  • Sources of data for analytical processing
  • Server and locally hosted platforms for data storage and analytical processing
  • Modelling techniques for designing data models for integration of multi-source data, including structured, semi-structured and unstructured data, and for modelling time-variant data/history
  • Design of systems for data acquisition, validating and cleansing data, integration and publishing of data.
 

Skills

Having completed this course, students should be able to:
  • Design and implement data models for integrating multi-source data, including dimensional data modelling, for structured and semi structured data
  • Design and implement data models for time-variant data
  • Design, implement and test systems for data acquisition, validation, integration and delivery from multiple sources and platforms
  • Design, implement and test basic descriptive statistical analysis on integrated data
  • Design, implement and test basic visualizations and graphs of data and analysis results.

Competences

Having completed this course, students should be able to
  • Discuss and argue pros, cons and trade-offs of choices
  • Use basic statistics and visualization to find and explain patterns of information in data.
 

Topics

Teaching methods and study activities

Details to be determined. A general overview of distribution of study activities across the student activities model is given below.
 
CATEGORY 1

Participation of lecturer and students
Initiated by the lecturer
50 hours -  40 %
Lessons, scheduled
Class discussion and case presentations
Exams and tests
 
CATEGORY 2
Participation of students
Initiated by the lecturer
70 hours - 50 %
  • Reading and self-study of assigned literature
  • Work on assignments / projects
 
CATEGORY 3
Participation of students
Initiated by students
10 hours - 5 %
  • Self-study of self-selected supplementary literature
  • Literature search

 

CATEGORY 4
Participation of lecturer and students 
initiated by students
7,5 hours - 5 %

  • Debate meetings
  • Study guidance


Note: 5 ECTS * 27,5  hours pr. ECTS = 137,5 Hours.
 

Resources

To be determined.

Evaluation

Oral examination, covering mandatory course work and theory covered in the course. Duration (grading included) app. 20 min/ 5 ECTS.

Permit criteria for attending examination

  • Mandatory course activities completed
  • Course assignment handed in before deadline
 

Examination

Oral examination
 

Individual oral examination without preparation based upon course assignment(s)
 
Allowed Tools: All
 
Internal Examiner

Grading criteria

Examinations account for 100 % of final grade.

Additional information

 

Responsible

Knud Erik Rasmussen (KERA)

Valid from

8/15/2019 12:00:00 AM

Course type

Compulsory Course for all ICT Engineering
4. semester

Keywords