Home > Taking courses of MSc program as minor student

Taking courses of MSc DSBE as minor student

Interested in following our courses?

In case you are interested to follow (a set of) individual courses of our MSc program Data Science in Business and Entrepreneurship (DSBE) but you are not registered as a student of this program it is good to know that there are possibilities! We allow students from external MSc program in a subset of our program as a minor students (in Dutch: bijvakstudent). 

Please note that we have limited capacity. In case there is no capacity in a certain course, we might have to inform you that you cannot participate in the course. 

Am I eligible?

The courses of our master’s program that are open for external students have the following label:

A. No hard requirements

You are eligible to follow this course with any completed BSc degree. However, please be aware that, as the name of our study program faithfully reflects, the scope of DSBE resides at the cross-junction of two fields of study: data science on the one side, and, business and entrepreneurship on the other side. All courses are specifically designed for our master’s program. You are advised to take this into account when applying to this courses asking yourself if  the course compliments the curriculum of your own master’s program.

B. Eligible to our master’s program DSBE

You are eligible to follow this course in case you meet the admission requirements of our master’s program. Our admission requirements can be found here.

C. Eligible to our master’s program DSBE plus specific knowledge requirement

Next to being eligible to our master’s program, this course requires a specific knowledge requirement (SKR). We set this SKR, as we also set this requirement for our own students.

Which courses are (not) open for external students?

Admission requirement label A

Fall semester:

Spring semester:

Admission requirement label B

Fall semester:

Spring semester:

Admission requirement label C

Below the courses are listed with label C. The extra specific knowledge requirement (SKR) is written behind every course.

Fall semester:

Spring semester:

 

 

The following courses are not open to external students:

  • Data Intrapreneurship in Action
  • Data Consultancy in Action
  • Data Entrepreneurship in Action
  • Research in Action
  • Data Engineering
  • Data Mining
  • Strategy and Business Models
  • Social Network Analysis for Data Scientists

 

How to apply and deadlines

How to apply / check eligibility

Download this document, complete and send it to education.support@jads.nl. In case you have chosen courses under label B, you will have to attach your Transcript of Records and a course (check) list, that can be downloaded here. In case you have choses courses under label C, you have to indicate which course you have followed and passed covers the SKR, and include proof of passing this course.

Deadlines

  • For courses in the fall semester, you have to apply before August 1.
  • For courses in the spring semester, you have to apply before December 15.

You are eligible for the course. What is next?

In case you are registered as a student to a master’s program at Tilburg University, you do not have to do anything. We will make sure you are registered to the course(s).

In case you are not registered as a students at Tilburg University, you have to complete the this document and send it to the Student Administration of Tilburg University. Please note that they require the following documents:

  • Copy passport or copy front and back of your ID-card
  • Proof of Payment from the educational institution where you have paid tuition fees. (N.B.: All universities and most HBO schools will send the proof directly to Tilburg University, after you request it. If not, an original Proof of Payment must be submitted (so a copy will not be accepted).
  • A permission letter of your current educational institution in which is mentioned the exact subject(s) you wish to study, including the statement that the courses will be part of your current program.
  • In case of master subject(s) you also need a permission letter from the Program Coordinator of the MSc Data Science in Business and Entrepreneurship, mandated by the Examination Board JADS (permission letter be send to you after you have applied)

Recommended knowledge requirements

To be able to prepare for the course, lecturers have written down some (recommended) knowledge requirements. In case a course is not listed, there is no specific recommended knowledge requirement.

No mandatory requirements, but a bit of background in cognitive science / cognitive psychology  will be very helpful

  • Basic programming experience (Python)
    • Variables
    • Functions
    • Classes
    • Numpy
    • Basic containers and related operations, e.g., lists, sets, dictionaries, numpy arrays
    • Matplotlib
  • Basic knowledge of linear algebra
    • Basic mathematical notation
    • Vectors
    • Dot products
    • Matrix operations
    • Derivatives
    • Gradients
    • Probability distributions
    • Random sampling
  • A reasonable working level of either [R] or Python
  • Familiarity with statistics / research methods
    • Sampling
    • Experimental design
    • Causality / potential outcomes
  • Knowledge of probability theory, random variables, distributions, etc. Preferably some familiarity with Bayesian methods
  • Basic familiarity with common marketing concepts such as Churn, Uplift, etc. will be useful but we will cover these also during the course.
  • Data Engineering (JM0140) and Data Mining (JM0150) will provide a useful background for the assignments in this course.

No prior knowledge: we assume that students do not have a finance background, but of course this helps

  • (a course such as) Data Mining (JM0150)
  • Linear Algebra
    • Scalars, Vectors, Matrices and Tensors
    • Multiplying Matrices and Vectors
    • Identity and Inverse Matrices
    • Linear Dependence and Span
    • Norms
    • Diagonal, Symmetric, Orthogonal Matrices
    • Eigen decomposition
    • Singular Value Decomposition
    • The Moore-Penrose Pseudoinverse
    • The Trace Operator
    • The Determinant
    • Principal Components Analysis
  • Probability and Information Theory
    • Random Variables
    • Probability Distributions
    • Marginal Probability
    • Conditional Probability
    • The Chain Rule of Conditional Probabilities
    • Independence and Conditional Independence
    • Expectation, Variance and Covariance
    • Common Probability Distributions
    • Bayes’ Rule
    • Information Theory
  • Numerical Computation
    • Overflow and Underflow
    • Poor Conditioning
    • Gradient-Based Optimization
    • Jacobian and Hessian Matrices
    • Linear Least Squares
  • Machine Learning Basics
    • Learning Algorithms (The Task: Classification, Classification with missing inputs, Regression, Transcription, Machine translation, Structured output, Anomaly detection, Synthesis and sampling, Imputation of missing values, Denoising, Density estimation or probability mass function estimation; The Performance Measure; The Experience: Unsupervised learning algorithms, Supervised learning algorithms; Linear Regression)
    • Capacity, Overfitting and Underfitting (The No Free Lunch Theorem; Regularization)
    • Hyperparameters and Validation Sets (Cross-Validation)
    • Estimators, Bias and Variance (Point Estimation; Bias; Variance and Standard Error; Trading off Bias and Variance to Minimize Mean SquaredError; Consistency)
    • Maximum Likelihood Estimation (Conditional Log-Likelihood and Mean Squared Error; Properties of Maximum Likelihood)
    • Bayesian Statistics (Bayesian Linear Regression
    • Supervised Learning Algorithms (Probabilistic Supervised Learning, Support Vector Machines,)
    • Unsupervised Learning Algorithms (Principal Components Analysis, k-means Clustering)
    • Stochastic Gradient Descent
  • Basic hands-on experience of the following data mining concepts
    • Classification
    • Regression
    • Clustering
    • Dimensionality reduction
    • Noise filtering
  • Basic understanding of the following data mining concepts
    • Graph mining
    • Social network analysis
    • Concept drift detection
  • Less criticall: basic understanding of business process modeling
    • BPMN
    • Petri Net
    • Transition systems
  • (a course such as) Data Engineering (JM0140)
  • Students are expected to have a basic understanding of computer science, in particular computer architecture, operating systems and networks, as these are the building blocks for cyber security.
  • (a course such as) Social Network Analysis for Data Scientists (JM2080)
  • Python programming skills.
  • Knowledge of python libraries to automatize the basic process.
  • Libraries to create user interfaces (i.e., react or angular JS)
  • .JSON, CSV, and SQL to store data.
  • Latex.
  • Google to search for basic answers.
[toggle title="Natural Language Processing (JM2050)"]
  • 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
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