Pre Master
Source: https://www.tue.nl/en/education/graduate-school/master-data-science-in-business-and-entrepreneurship/pre-master Parent: https://www.tue.nl/en/education/graduate-school/master-data-science-in-business-and-entrepreneurship
Pre Master
Pre-Master's program Data Science in Business and Entrepreneurship
Do you have a relevant background and sufficient knowledge of statistics and mathematics, but are you not directly eligible to the Master Data Science in Business and Entrepreneurship? You could then think of a 30 EC pre-master’s program, which is available for university graduates.
Are you an HBO student? Then an (extended) tailor-made pre-master's program could be your key to our master’s program.
Pre-Master's program for university graduates
The pre-Master's program includes the following courses:
Courses pre-Master's program
- Programming – 6 ECTS\ The course gives students who do not have experience with programming, a first introduction and basic skills in (mainly imperative) programming and scripting, using Python 3. You can solve simple programming problems independently, and structure these in the language Python. Most of the learned principles can be applied to other computer languages used in data science (e.g. R) as well.
- Data-structures and Algorithms – 6 ECTS\ This course offers algorithmic techniques for solving problems with respect to data science applications. The main objective of this course is to learn basic skills and knowledge to design efficient algorithms and data structures and to analyze their complexity.
- Introduction to Machine Learning – 6 ECTS\ The "introduction to Machine Learning" course will cover basic topics in Data Mining and Machine learning, leading from the design of a proper data-scientific study campaign which starts from data mining and preparation and proceeds to experimentation with ML algorithms. Known frameworks for Data Mining (i.e., CRISP-DM) will be considered and experimented upon practically. Furthermore, you will learn the basics of research design and hypothesis formulation/testing. Subsequently, you will get to grips with most commonly used techniques of machine-learning including decision-trees, instance-based learning, as well as artificial neural networks. Finally, you will learn the basics of model evaluation, model generalization as well as the bias-variance trade-off.
- Foundations of Databases – 6 ECTS\ This course will introduce the fundamentals of database systems. The main emphasis is on the relational algebra and model. Analysis, design and implementation of database systems are discussed in detail. Furthermore, you will learn to understand semi-structured data (e.g. XML and JSON).
- Statistics for Data Scientists – 6 ECTS\ In this course, we systematically cover fundamentals of statistical inference and testing, and give an introduction to statistical modeling. The first half of the course will be focused on the fundamentals of statistical inference such as sampling procedures, probability theory, and random variables. In the beginning we also provide a gentle introduction to the R language for statistical computing, which will be used throughout the course to show how theoretical concepts can be applied in practice. In the second half of the course we will deal with the estimation and testing of population characteristics based on sample data. Furthermore, we provide an introduction to statistical modeling via introductory lectures on (generalized) linear regression models and briefly discuss the Bayesian approach to statistics. Throughout the course, real-data examples will be used in lecture discussion and homework problems. This course lays the foundation, preparing you for other courses in machine learning, data mining, and visualization.
You will find a detailed description of the courses and required literature in our course catalog.
Go to the course descriptions \
Please note: this is the curriculum for this academic year and may change each year. Therefore, please check the current program in the OSIRIS Student course catalog at the start of the academic year.
Do you want to know more about this pre-Master’s program?
Visit the Master's Open day
Admission and application
An admission committee will determine your eligibility for the pre-Master’s program taking into account your eventual eligibility for the Master’s program.
To apply for the pre-Master's program, please follow the same steps as applying for the Master's program Data Science in Business and Entrepreneurship.
Are you an applied science / HBO student?
- Students from specific programs
Excellent students studying at
-
- Fontys ICT or Applied Mathematics
- BUAS Applied Data Science & AI
- Hogeschool Rotterdam Applied Data Science & AI
can be offered the opportunity to follow a minor program during their studies. For more information about the minor, you can contact the study advisor of your program at your own institution. Please note that the application for the minor program goes directly through your own study department.
- Graduates holding another degree from a university of applied sciences
If your relevant HBO degree contains at least 15 EC in mathematics and statistics, which includes a minimum of 5 EC in mathematics and 5 EC in statistics, your English meets the language requirement, and your average grade is 7,5 or higher, you are most likely to be eligible for a pre-master’s program at JADS. The content depends on the courses you completed during your HBO study program. The pre-master’s program can vary between 30-60 EC. Should you be interested, please contact the admissions officer: questions@remove-this.jads.nl