Applied Data Science - career prospects
A candidate with a completed 2-year Master's degree in Applied Data Science shall have the following total learning outcomes defined in terms of knowledge, skills and general competences:
K1: Advanced knowledge within Data Science, which includes data processing, data, machine learning, data extraction, statistics and typical programming languages for the area, including: Python and R.
K2: Specialized insight into data analysis.
K3: In-depth knowledge of scientific theory and methods in Data Science.
K4: Apply knowledge about algorithms for statistical analysis, machine learning or data extraction in new areas within data science.
K5: Analyse professional issues based on the fourth science paradigm, 4Vs of big data (volume, velocity, variety, and variability), data-driven approach, CRISP-DM (cross-industry standard process for data mining).
S1: Analyse and relate critically to different sources of information, datasets and data processes; and apply these to structure and formulate data-driven reasoning.
S2: Analyse existing theories, methods and interpretations within the subject area and work independently in applying and evaluating different storage and data processing technologies.
S3: Use CRISP-DM and scientific methods to develop data analysis programs in an independent way.
S4: Conduct independent, limited data collection, analysis and evaluation according to established engineering principles in accordance with current research ethical standards.
G1: Analyse relevant ethical issues arising from data usage and data recovery.
G2: Apply their knowledge and skills in new areas to carry out advanced tasks and projects related to data processing, data analysis and optimisation.
G3: Communicate comprehensive independent data analysis and development work and master Data Science expressions.
G4: Communicate on issues, analyses and conclusions related to data-driven research and development, both with specialists and to the general public.
G5: Contribute to new ideas and innovation processes by introducing data-driven approaches.