Master's Degree in Human Resources Development and Management

Curriculum structure

Planning the teaching involves the structuring of content, systems and methodologies that make it possible to acquire the knowledge, capacities, abilities and basic skills necessary for students to be able to develop their activity in any of the lines specified in the professional profile defined for the degree.

The curriculum, structured into modules/subjects and courses, is based on the regulatory framework for the organization and verification of official university education—Royal Decree 1393/2007—and the guidelines developed by the University of La Laguna.

Curriculum

If you want to consult the history of the teaching guides, select it by accessing the ULL e-guide portal (once you have selected the subject, click on the academic year to view previous courses):

Code Subject Typology Credits Course Four-month period
315731101 Strategic Management of the Company Mandatory 3 1st 1st
315731102 Human Resources Planning Mandatory 3 1st 1st
315731103 Integrated management of quality, environment and occupational health Mandatory 3 1st 1st
315731104 Strategies for evaluating and analyzing organizations Mandatory 3 1st 1st
315731105 Statutory regime of public officials Mandatory 3 1st 1st
315731106 Team management skills Mandatory 6 1st 1st
315731201 Legal framework of labor relations I Mandatory 3 1st 2nd
315731107 Job analysis and development of training plans Mandatory 6 1st 1st
315731108 Selection and socialization processes in the organization Mandatory 3 1st 1st
315731202 Design and implementation of performance evaluation systems Mandatory 3 1st 2nd
315731204 Master's Thesis Mandatory 6 1st 2nd
315731203 Internships Mandatory 12 1st 2nd
6 elective credits must be taken
315730901 Legal framework for labour relations II Elective 3 1st 2nd
315730902 Management and Promotion of Quality of Life at Work Elective 3 1st 2nd
315730903 Behavioral assessment in organizations Elective 3 1st 2nd
315730904 Specialized software application for data analysis Elective 3 1st 2nd