Information for new MSc DSBE students as of August 2024
All the information you need for the start of your study program!
On this page you can find relevant information for students starting with the master’s program Data Science in Business and Entrepreneurship (DSBE) as of August 2024.
Below a guide to all different topics:
- Acronym guide
- Contact persons
- Complete your enrollment
- Mode of education 2024-2025
- Enrollment to study program
- Which systems / platforms will be used?
- Examination program 2024
- Academic calendar 2024-2025
- Generic schedule (time slots) fall semester
- (Recommended) prior knowledge courses of semester A
- Fixed teams in semester A
- Statistics Bootcamp
- Coaching and Skills
- Study association D.S.A. Pattern
- Introduction Days
- Nice to read articles!
- Campus card
- Information for international students
- Podcast episode
Acronym guide
- JADS = Jheronimus Academy of Data Science
- (MSc) DSBE = (master’s program) Data Science in Business and Entrepreneurship
- PM = pre-master’s program
- TilburgU / TiU = Tilburg University
- TiSEM = Tilburg School of Economics and Management
- TU/e = Eindhoven University of Technology
- IE&IS = Industrial Engineering & Innovation Services
Contact persons
We can imagine you have many questions. If you have not found the answer in our existing communications (e.g. on this website), please feel free to contact us.
Contact the Admissions Officer (Ismay Repko) via admissionsDSBE@tilburguniversity.edu, in case your question concerns:
- Admission procedure
Contact the Student Desk of Tilburg University if your question concerns:
- Enrollment
Contact Valérie Goorts (Student Affairs Officer) and/or Nigel Claus (Program Coordinator/Study Advisor) via education.support@jads.nl, in case your question concerns:
- Study program
- Study advice
Contact questions@jads.nl, in case your question concerns:
- General questions (or in case you do not know who to contact)
It is helpful to include your student number in the contact.
Complete your enrollment
In case you are conditionally admitted, please check the conditions in your admission letter to see what you still have to do in order to obtain unconditional admission. Please provide the necessary documents by post as soon as possible. Also check if you have met the criteria linked to the payment procedure of the tuition fee.
Mode of education 2024-2025
By default, education will take place on campus.
Enrollment to master's program
DSBE is a joint degree and all students will have a main enrollment via Tilburg University and an extra enrollment (in Dutch: neveninschrijving) at Eindhoven University of Technology. Once your main enrollment is completed you will automatically get the extra enrollment.
You might notice two different domains are used (@tilburguniversity.edu and @uvt.nl) but do not worry: it is the same!
Which systems / platforms will be used?
In the DSBE program, we use all educational-related systems of TilburgU.
More information can be found on this website. The registration to the courses of DSBE opens mid-Augustus , 2024 (as of 16:00h).
Once you have a TilburgU account, you will be added to this page. The Education Support Team will regularly check if there are new students to add.
Examination program 2024
The MSc DSBE entails a two-year full-time program requiring students to attain 120 credit points (ECTS). The master starts either at the end of August/ beginning of September, or at the end of January/beginning of February. The curriculum is divided into four semesters of 30 credits each.
The study load of the individual courses is 6 EC, except for the Master thesis, which has a study load of 30 EC.
- 10 compulsory courses (60 EC)
- 5 elective courses (30 EC)
- Master thesis (30 EC)
For more information and links to the course description of all courses, go to this page.
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Homologation program
Admitted for the homologation program? Your semester A, B and C will look a bit different. Go to this webpage and find more information by scrolling down to the topic ‘Homologation program’.
Academic calendar 2024-2025
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Nota bene:
- The fall semester 2024-2025 starts in the week of Monday August 26, 2024
- The Introduction Days will take place on Thursday August 22 and Friday August 23, 2024
Generic schedule (time slots) spring semester
The image below presents the generic schedule of fall semester. It shows the time slots that are reserved for the different courses. It might be possible that in some weeks a course is not taught / does not use the full time slot. Course coordinators will inform students about the details on their course page in Canvas.
If this generic schedule is deviating from the schedule published by the course coordinator, the latter is always leading.
Nota bene
- First year mandatory courses for MSc are visualized in blue
- Elective courses for MSc are visualized in yellow
- In case you admitted to the MSc program via the homologation program, the course Data Engineering (JM0140) is moved to year 2, and you take Natural Language Processing (JM2050) during your first semeste
The generic schedule is also published on https://rooster.uvt.nl/.
- For the master’s program: Select “+ Add Timetable” and add “3N300_M Data Science in Business and Entrepreneurship_Jaar 1” for academic year 2024-2025.
| Statistics Bootcamp (JM0020) | Friday, Augustus 30, 2024 |
| Social Network Analysis for DS (JM2080) | Thursday, December 12, 2024 |
| Natural Language Processing (JM2050) | Friday, December 13, 2024 |
| Data Engineering (JM0140) | Tuesday, December 17, 2024 |
| Statistics Bootcamp (JM0020) | Friday, October 4 , 2024 |
| Social Network Analysis for DS (JM2080) | Thursday, January 23, 2025 |
| Natural Language Processing (JM2050) | Friday, January 24, 2025 |
| Data Engineering (JM0140) | Tuesday, January 28, 2025 |
(Recommended) prior knowledge courses of semester A
In previous cohorts, some discrepancies between students in terms of prior knowledge have been noticed. Although all upcoming students are assessed on the same admission criteria not all students seem to have the same knowledge and skills at the start of the academic year. Of course, it might be the case that a course with particular knowledge has been taught to you a while ago (e.g. in the first year(s) of your bachelor’s program) and you simply forgot.
In order to give you a chance to revise, the course coordinators of semester A listed the (recommended) prior knowledge a student is advised to have before entering the courses. Please note that some of these points will be covered / will get specific attention in the courses but revising them in advance will result in a head start!
Basic programming and data management experience, such as…
- Data manipulation, analysis, and visualization
- Knowledge and experience in Python and R
- Ability to collect data/ information to complement the provided datasets
Basic understanding of business and psychology theories, such as…
- Entrepreneurship and personality theories
- Toolkits: Business model canvas and value proposition canvas
Basic understanding of statistical methods, such as…
- Experiment, A/B testing, questionnaire, interview and related statistical analyses
Attitude and mindset:
- Passion in entrepreneurship and/ or business development
- Creative mindset and proactive attitude
- Presentation skills
- Team player
- Basic software engineering experience (in Python)
- If the students have some experience of applying software design and documentation principles, then they will be able to easily understand complex systems such as big data processing systems and programming models, complex deployment environments, and so on.
- Web Apps with Flask
- SQL databases with Python libraries
- Testing the application with a testing tool
- Skill to design and document a multi-tier ML-based application
- Skill to build and evaluate a multi-tier ML-based application with Python
In sum, an introductory course on software and system design (focusing on ML-based applications) would be helpful for the students (at the pre-master level). May be Introduction to Data Architecture.
Basic programming experience (Python)
- Variables
- Functions
- Classes
- Array
- Operations
- Numpy
- Matplotlib
Basic knowledge of linear algebra
- Basic mathematical notation
- Dot products
- Matrix operations
- Derivatives
- Gradients
- Probability distributions
Basic understanding of inferential statistics probability theory, random variables, and distributions
- Detailed understanding of null-hypothesis formulation, null-hypothesis testing, p-values, confidence intervals, significance levels, statistical power
- Working knowledge and understanding of linear regression (OLS), knowing and understanding the assumptions underlying OLS (ideally, for both fixed and random effects)
- Working knowledge and understanding of logistic regression, understanding how to interpret results (incl. coefficients) from a logistic regression, knowledge how to transform coefficients into log-odds
- Working knowledge and understanding of moderator-mediator variable distinction
A basic level of expertise with programming tools for data preprocessing and analysis (incl. Python, R), or statistical software (incl. STATA, MATLAB, SPSS).
- Managing installing and updating packages of your chosen language/software
- Knowledge on debugging or reading package documentation
- Ability to read data from different source files (e.g. csv, txt, xlsx, RDS) as well as basic data manipulation (e.g. subset/filter, data type transformation, numeric transformation)
- Basic plotting skill (including histograms, scatter plots, density plots) (e.g., ggplot2 in R, matplotlib in Python)
- Familiarity with running regression analyses
Not required, but helpful:
- Ability to scrape data from the web
- Basic knowledge of clustering (e.g., distance measures)
- Basic knowledge of text analysis and text processing
A reasonable level of expertise with R
- Managing R and Rstudio (installing and updating packages from CRAN and github)
- Knowledge of how to use the internal help facilities of R (so, no relying on Google)
- Ability to use base R (or the tidyverse) to manage data structures (including subsetting, select, filter, join) and read in data (from txt, csv, data.frames)
- Working with data.frames and lists (Including extracting elements from them)
- Basic plotting skill (including histograms, scatter plots, density plots)
- Working with functions (incl. calling arguments by name, working with argument lists)
Working knowledge and expertise with matrices
- Matrix manipulation
- Understanding eigenvalues
- Matrix calculus, matrix equations
Basic understanding of inferential statistics
- Detailed understanding of null-hypothesis formulation, null-hypothesis testing, p-values, confidence intervals, significance levels
- Working knowledge and understanding of linear regression (OLS), knowing and understanding the assumptions underlying OLS
- Working knowledge and understanding of logistic regression, understanding how to interpret results (incl. coefficients) from a logistic regression, knowledge how to transform coefficients into log-odds
Not required, but helpful:
- Knowledge of Bayesian statistics
- Ability to scrape data from the web
- Knowledge or bootstrapping or resampling
- Experience with rmarkdown
- Good understanding of machine learning (ML) methods and concepts, including
- supervised and unsupervised learning
- generic ML algorithms for classification and clustering
- Experience in programming in Python
Fixed teams in semester 1
Background
As all courses in semester A contain group work, the first semester is perceived as being very challenging. During this semester, you work on the group assignments in fixed teams for the entire semester in order to take away organizational struggles and create time for content! These teams are composed of (in principal) 5 students.
It is expected that teams are composed of students with various educational backgrounds resulting in a well-rounded group where group members can gain from each other’s strengths. In case we notice that certain teams are composed of students with the same backgrounds, the Education Support Team might randomize some of the teams.
Peer evaluation sessions
To foster teamwork and evaluate team working skills every team will have two mandatory guided peer review sessions. The first, intermediate peer review session is planned in September-October. The second, and last peer review session is planned at the end of November-beginning of December.
Registration
- You can register for a team via this Google Sheet
- Please register a.s.a.p., before Thursday August 29, 2024
- Nota bene:
- Put your details in the orange section in case you are admitted to the master’s program via the homologation program (i.e. not doing Data Engineering in semester 1 – but instead you take Natural Language Processing)
- Put your details in the yellow section in case you want us to put you in a team
- Put your details in the green section in case you are planning to take less than five courses of semester A
- Abbreviation guide:
- DE = Data Engineering
- DM = Data Mining
- SNA4DS = Social Network Analysis for Data Scientists
- SBM = Strategy and Business Models
- DIiA = Data Intrapreneurship in Action
- NLP = Natural Language Processing
Statistics Bootcamp
In education week 1 of 2024-2025 (starting August 26, 2024) a Statistics Bootcamp is organized that is mandatory for MSc students in year 1. The Statistics Bootcamp will be assessed by a written examination.
Introduction
Statistics forms a foundation of our data science master and a basic level of statistical knowledge is a fundamental prerequisite to successfully participating in the Master’s. To refresh and improve your statistical skills we organize a Statistics Bootcamp (i.e. a short intensive training program). The Statistics Bootcamp trains and extends the statistical knowledge of each master’s student in a systematic manner while also making sure that students are aware of the relevance and implications of statistics in data science.
In one week, we bundle the efforts of three mandatory courses in the master’s program Data Science in Business and Entrepreneurship. These three courses are 1) Social Network Analysis for Data Scientists, 2) Data Intrapreneurship in Action, and 3) Strategy and Business Models.
To avoid the repetition of the same statistics content in three courses, we group those three courses into one overarching Statistics Bootcamp. In short, the Statistics Bootcamp provides the opportunity to advance your statistical knowledge and creates a more homogeneous level across all students, allowing the lecturers to immediately move on to advanced data science content in the remainder of the semester.
Learning outcomes
- Transferable statistical skills to be applied across the master’s program, and more precisely in the following topics:
- Module 1: Ordinary Least Squares / Hypothesis testing / p-values
- Module 2: Descriptive statistics / Data manipulation / Data checks
- Module 3: Generalized Linear Models / Loglinear models
- Module 4: Introduction to Prediction and Causal Inference
- Programming in the R language is required
Schedule
In the run of 2023-2024, the schedule looked like this (for Monday until and including Thursday):
- 09:30 – 11:00: lecture in the chapel
- 11:00 – 15:00: work on your assignment for the day
- 15:30 – 17:00: lab in the chapel
It is assumed that the run of 2024-2025 will look more or less the same.
Fun breaks with games and snacks
During the week (usually after the lecture), we will try to arrange some snacks and organize activities/games in order for you to regain some energy to survive this intensive bootcamp!
Examination
All the materials in the Bootcamp constitute mandatory content essential for the three courses that are jointly designing this project. All Bootcamp material will be examined in these courses (projects/reports/exams/etc.). Each course will specify which content you need to study for the examination.
A pass for this course is required.
The first examination opportunity is on Friday August 30, 2024 (09:30-11:00). The second examination opportunity is scheduled on Friday October 4, 2024 (09:00-10:30). The examination will be assessed in TestVision.
Coaching and Skills
Professional Coaching
As a part of your professional development, every MSc DSBE student is offered an optional coaching trajectory, which consists of in total four sessions. You start this trajectory in the first semester of year 1, after signing up via the Canvas page of Skills Journey. Every semester you have the chance to speak with your coach. In the coaching sessions you can evaluate your development and personal learning goals. You will get concrete tips and advice, adjusted to your personal ambitions. To prepare the first session you will receive a link to an online Development Assessment Tool. This will provide insights in your personality and competences and gives input on defining your (future) goals and ambitions. A bonus is that you already collect input for your Final Development Reflection Report, which is a mandatory assignment to be able to graduate (see the Professional Skills paragraph). So if you want to work on your professional development, make use of a professional coach!
Professional Skills
As a data scientist and entrepeneur it is important to know how to bring your message across to others. Skills that are important in the DSBE program are: presenting, interviewing, debating, selling, consulting & advising, creative thinking, negotiating and storytelling. To be able to graduate you need to pass at least 6 out of these 8 professional skills and make a Final Development Reflection Report. The skill assessments are embedded in the courses of the DSBE program. An exception is the skill interviewing, which can be assessed via the Skills Journey course. To pass the skill assessments you will receive instructions from the course teacher, via Canvas (Skills Journey). To practice and prepare yourself, you can sign up for optional skill workshops. In these dynamic workshops there is a lot of interaction and you will be able to practice and improve on your skill. You will be better prepared for the skill assessments and your future!
Study association D.S.A. Pattern
D.S.A. Pattern, or in short Pattern, is the study association for all Data Science students at Eindhoven University of Technology, Tilburg University, and Jheronimus Academy of Data Science (JADS).
Feel free to take a look at their website or contact them via info@dsapattern.nl.
Cohort group on Whatsapp
Do you want to get in touch with your fellow students. Make use of the Whatsapp group, via: https://chat.whatsapp.com/Ghz6b7i4kfGGnTuuNaajaJ
Introduction Day
SAVE THE DATE: August 22 and 23, 2024!
The added value of the introduction days written by Thimo, a current student who started the master’s program as of September 2023:
“The general purpose of the introduction days is to meet your fellow study members and get acquainted with the workings of the university and everything around it. While this is definitely the same for the introduction days at JADS, there is an additional benefit.
Specific for students starting the MSc as of fall semester: during the whole first semester A, you will work together in the same group which you can ensemble yourself. Due to the different types of courses, you want a diverse team that works together well and can help each other where needed. What better way to construct such a team than during the introduction days? You meet everyone in a social setting with fun activities that have plenty of room for easy-to-initiate conversations.
I’m really glad I chose to do this, as it allowed me to meet the people I level with really well, which resulted in fun working sessions and good collaboration during my first semester. Hopefully, it can do the same for you!”
Hi, future JADS student!
Every semester, D.S.A. Pattern organizes an introduction for the new generation of DSBE (pre-)master students! This semester it will take place on August 22 and 23, 2024 in Den Bosch!
During this day you will get a lot of practical information and participate in fun activities with the other new students. In this way, you can get to know your fellow students before starting your (pre-)master at JADS!
The JADS Intro will cost € 22,50, and this includes some food and drinks to enjoy throughout the day. More information about the program will be provided to you when the intro days are coming close! Get your ticket soon. To register, go to the this website (N.B: registration deadline = August 12, 2024)
We are looking forward to meeting you!
Best regards,
Jochem van den Boogaard (Chair ‘s-Hertogenbosch Department, Study association D.S.A. Pattern 2023-2024)
&
Zita Godrie (Chair ‘s-Hertogenbosch Department, Study association D.S.A. Pattern 2024-2025)
Campus cards
As a student of the DSBE program, you will have two enrollments: at Tilburg University (TilburgU) and Eindhoven University of Technology (TU/e). You can request a student card for both universities:
- For TilburgU, the information can be found on this page
- For TU/e, the information can be found on this page
- Access to library TilburgU
- Sport Centre
- Access to the Mariënburg Campus
- For printing facilities at the Mariënburg Campus
- Access to TU/e library
- TU/e Sports Centre
N.B. you will have to pick up the campus card yourself at the university campus in Tilburg and/or Eindhoven. In some cases, they can send the card to your home address. Contact the universities to check what is possible.
Information for international students
In case you need a visa, you should have already started the immigration procedure. You must have received an email from the Immigration Office about the procedure. If you have not received this email, please contact the Immigration Office of the University you applied at.
- TilburgU: immigration@tilburguniversity.edu
- TU/e: io@tue.nl
(please note: if you have not started the immigration procedure yet, it cannot be guaranteed that you will get your visa in time, which means you will miss the first week(s) of class/introduction day).
Special page ‘Student Housing Den Bosch’
Please visit this page for useful information regarding student housing in Den Bosch.
Accommodation at the Mariënburg Campus
- Please visit the website for more information:
- In case you want to know more about it, please contact directly Kenny Steenbakkers, Gapph Real Estate Manager.
- E-mail: k.steenbakkers@gapph.nl / marienburgcampus@gapph.nl.
Accommodations from regional housing corporations
Are you a Dutch or international student? Do you study at an educational institution in ‘s-Hertogenbosch and are you looking for a rental student room in the region Brabant? At http://www.studentroomsbrabant.com/ you can find the current supply of rental student rooms of the affiliated regional housing corporations.
Accommodation in Tilburg
To find accommodation in Tilburg city, please contact the Housing Office at Tilburg University.
Accommodation in Eindhoven
Please contact the International Office at Eindhoven University of Technology for student Housing in Eindhoven city.
Useful websites for finding information about part-time jobs include:
Tired of reading and in the mood for listening? Did you know JADS has its own podcast?
In the fourth episode, we talk with two international master students at JADS. Why did they choose to study abroad and come to the Netherlands? And why JADS? Does the Master’s Program Data Science in Business and Entrepreneurship offer them what they are looking for? All of this and much more on looking for accommodation, side jobs and public transport in this 32 minute episode.
Go to this website to listen!