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Big Data

The need for data scientists has grown exponentially over the past few
years. While there are many Big Data or Data Science minors out there,
this minor uniquely focuses on the interaction between the field of Data
Science and Artificial intelligence and uses a software-oriented approach to
solving ‘wicked (data) problems’.

Leerdoelen

The student is able to:
 work on a data science driven research project
 create and train a machine learning model/pattern
 create an application to show the outcomes of the machine learning
/ deep learning models/patterns
 design and develop a highly scalable parallel distributed processing
cluster of nodes
 cooperate with fellow students in software development activities
 effectively communicate with external clients

Aanvullende informatie

The core of the minor consists of a group project for an external client,
which provides you the opportunity to work on real-life realistic problems.

Strategies and teaching activities
 Workshops by experts
 Do research with your project group
 Lectures on theory combined with practical exercises


Competences
 Analysing - Specifying a distributed computer system consisting of
timing, resource usage and performance
 Designing - Designing a software system while taking into account
existing components and libraries, while making use of design
principles and/or quality criteria
 Designing - Designing a distributed computer system, including
setting up actuators, sensors, timing, resource usage and
performance
 Realising - Setting up an infrastructure that meets demands in
terms of performance, usability, security and compliance
 Realising - Realising a public or private cloud based infrastructure
and services while taking note of all requirements

Ingangseisen

Bachelor ICT 3rd year with experience in programming.

Toetsing

 Project assessment for Big Data & AI Project consisting of a written
report, a code review and a presentation of the created application.
 Individual final assignment for Machine Learning, Deep Learning &
Statistics (Data Mining)
 Individual final assignment for Big Data & AI Fundamentals
 Individual weekly assignments for Parallel Distributed Systems
(Hadoop)
 Individual weekly assignments for Computer Vision
All assessments must be completed with a sufficient grade (55 or higher)

 

Course - EC - Type - Weekly lectures - Self-study

1) Big Data & AI Project (5EC) - Theory - 2 hours - 12 hours 

2) Big Data & AI Fundamentals (2EC) - Project  - 3 hours - 3 hours

3) Machine Learning, Deep Learning & Statistics (Data Mining) (2EC) - Practical / Assignments - 2 hours - 3 hours

4) Parallel Distributed Processing (Hadoop Fundamentals)
(3EC) - Practical / Assignments - 3 hours - 3 hours

5) Computer Vision (Digital Image Processing) (1EC) - Workshops - 2 hours - 3 hours

6) Professional Presenting (1EC) - Presentation - 2 hours - 0 hours

7) Study Coaching (1EC) - Portfolio - N/A - 2 hours

Total (15EC) - Weekly lectures 14 hours - Self-study 24 hours

Rooster

April – June
Class days: 4 days (1 day off)