880502 :Master thesis/Data Science in Action

General info

Instruction language English
Type of Instruction Individual and group meetings with supervisors (No data available yet)
Type of exams Master thesis project proposal (pass/fail) + Master Thesis (graded) (No data available yet)
Level:Master
Course load:1 or 2 or 18 or 15 ECTS credits depending on the programme, see below.
Registration:Enrollment before start lectures
Blackboard InfoLink to Blackboard (When you see 'Guest are not allowed in this course', please login at Blackboard itself)

Lecturer(s)

No photo available
dr. M. Postma-Nilsenova (Masterthesis coordinator)


Objectives

The objective of this integrative project that will ultimately result in an individual Master’s thesis is to teach students how to perform large-scale data-driven analyses. Students combine the technical, legal and entrepreneurial skills acquired earlier in the program with insights provided by lecturers and companies involved in the different projects. Along the way, students acquire additional technical skills that are related to obtaining data (for instance extracting data from databases or data wrangling), performing the analysis (for instance data analysis based on mathematical modeling or machine learning), and creating (interactive) visualizations. With all this, the Data Science in Action project prepares students to work as all-round data scientists in a variety of fields.


Contents

  • The Data Science in Action projects are introduced during a plenary meeting organized in November 2017. Attendance of the meeting is obligatory.
  • Subsequently, students are matched to individual projects with selected internal and external project partners.
  • In the course of December-January, students write a project proposal.
  • The proposals are evaluated in February 2017.
  • A project must be approved by the Data Science in Action coordinator or the intended supervisor to receive a 'GO'. 


Specifics

The Data Science in Action projects are specifically intended for students in the Data Science: Business and Governance track.


Compulsory Reading

  1. Articles relevant for the selected thesis topic


Recommended Prerequisites

Course RS Data Processing OR RS Programming with R


Required Prerequisites

Course Data Mining for Business & Governance OR Analytics for Business & Governance


Compulsory for

  • Data Science: Business and Governance ( 2015: 1 ECTS, 2015: 2 ECTS, 2015: 15 ECTS, 2016: 2 ECTS, 2016: 1 ECTS, 2016: 15 ECTS, 2017: 18 ECTS )

(17-aug-2017)