Study Plan

During the first weeks in the Master's programme a study plan needs to be created by the students. It is expected that the students familiarise themselves with the programme to get the most out of it. The study plan is an indicative plan for how the student wants to go through the programme, but is not binding and leaves students free to change course during the programme.

The curriculum of this Master’s programme consists of a mandatory and an optional section. All courses are 7.5 EC, unless indicated otherwise.

Mandatory courses (32 EC)

Advanced research methods

The following quantitative subjects will be discussed:

  • Fundamental statistical concepts/elementary probability topics
  • Correlation and regression analysis
  • Analysis of variance (one-way ANOVA, multi-way ANOVA, ANCOVA, repeated measures, multivariate ANOVA )
  • Logistic regression
  • Factor analysis (principal component analysis)
  • Cluster analysis
  • Non-parametric tests

The following qualitative methods will be discussed:

  • Systematic literature review
  • Grounded theory
  • Case studies
  • Experiment design and operation
  • Analysis and interpretation
  • Mixed methods
  • Design research
  • Ethics

Method engineering

Method Engineering is defined as the engineering discipline to design, construct, and adapt methods, techniques and tools for the development of information systems. Similarly as software engineering is concerned with all aspects of software production, so is method engineering dealing with all engineering activities related to methods, techniques and tools.Typical topics in the area of method engineering are:

  • Method description and meta-modeling
  • Method fragments, selection and assembly
  • Situational methods
  • Method frameworks, method comparison
  • Incremental methods
  • Knowledge infrastructures, meta-case, and tool support

Data science and society

This is the introductory course for the Applied Data Science profile, the Applied Data Science postgraduate MSc programme, and the Business Informatics (MBI) programme. As such, it's primary objective is to inspire and introduce you to the emerging domain of Applied Data Science. . The following assignments are among the key parts of the course:

  • Book review: Explore data science and its societal impact
  • Mid-term e-exam on data engineering with Hadoop
  • End-term e-exam on data analytics with Spark

The graded deliverables generate the final course grade as follows:

  1. [A] Book review
  2. [B] Mid-term exam
  3. [C] End-term exam
  4. [D] Optional bonus for extraordinary participation/performance

Grade = [A]*0.10 + [B]*0.40 + [C]*0.50 + [D]

NB: To qualify for the second chance exam, all grading components need to be at least 4.0, and component A needs to have been submitted within the allotted time. The 2nd chance exam is an extensive market survey report assignment.

Business process management

There is no content available for this course.

Introduction to Business Informatics

The course is obligatory for all students that have received formal approval for entering the Business Informatics master program after 1 February 2007. This course consists of several meetings in which students are informed about the opportunities of the MBI curriculum.

Introducing Natural Sciences

There are two morning sessions with several speakers introducing the student to the the education system of the graduate school, its rules, its curricula, general and practical information about personnel and administration, specific information about the programme itself and expectations of the programme board about their students, honours education, specific profiles across disciplines and the profession of teacher.
Knowing what kind of skills and attitudes the labour market is looking for is considered as important. Workshops will train students to enhance awareness about their own strengths and weaknesses or introduce them to the work and life of PhD students.
Students will have ample time to get to known each other and their programme board.
Lunches, drinks and a concluding dinner will be organised.

Dilemmas of the scientist

This course consists of one workshop. Themes that will be addressed in this course:
The course discusses dilemmas of integrity in the practice of doing academic research. Students will learn what such dilemmas are and how they can deal with them in practice.

Students can only attend this course after they have completed the first workshop.

Electives (7.5 EC each)

Software production

Software Production is the research domain that covers product development, corporate entrepreneurship, and societal implications of large scale software development in open market conditions. Requirements formulation is an essential activity during software production, for which User Stories have been adopted widely.
The overall goal of this year's seminar is to develop a MOOC with corresponding book on User Stories. We will integrate current scientific knowledge on User Stories into a well-designed set of learning modules with presentations, assignments, and exams. Industrial materials and case material in various media will be included to boost the student experience.

Course form
The course is run as a seminar. Interactive discussions led by students, PhDs, and staff.
The research project is performed individually assisted by peers. Students will present their proposals of chapters and clips, as well as their final deliverables.

Exam form
Various presentations. Written research reports. Knowledge clips

Foundations of information science

There is no content available for this course.

Business intelligence

This course deals with a collection of computer technologies that support managerial decision making by providing information of both internal and external aspects of operations. They have had a profound impact on corporate strategy, performance, and competitiveness, and are collectively known as business intelligence.During this course the following BI topics will be covered:

  • Business perspective
  • Statistics
  • Data management
  • Data integration
  • Data warehousing
  • Data mining
  • Reporting and online analytic processing (i.e., descriptive analytics)
  • Quantitative analysis and operations research (i.e., predictive analytics)
  • Management communications (written and oral)
  • Systems analysis and design
  • Software development

ICT startups

There is no content available for this course.

ICT advisory

The advisory discipline is an established industry and employs hundreds of thousands of people. Advisory is best described as “creating value for organisations, through the application of knowledge, techniques and assets, to improve business performance. This is achieved by through the rendering of objective advice and/or the implementation of business solutions” (Markham & O’Mahoney, 2013). Giving advice is not limited to a particular industry and can be found in any industry and on many different topics such as taxes, business strategy, marketing, ICT etc. Logically, the focus of this course is on giving ICT advice but to a variety of industries.
In this course we address ICT advisory from four different perspectives: Descriptive, Practitioner, Critical, and Career perspective. These will be addressed in the lectures of the course and are based on the book that is prescribed for this course. Some of these lectures are delivered by the students themselves, as part of learning how to present and how to provide training. Besides the theory you will be practicing your consultancy skills in the skills workshops. Skills include for example presenting, analysing and writing. Each of the workshops will be provided by a different consultancy company that is based in the Netherlands and concerns a mix of small, medium and large consultancy organisations. Finally, you will practice skills and theory in a project where you have to advise a real client. In this project you will work in teams of three students, where the client that you will be working for is provided by one of the consultancy companies. During the project you will produce a number of intermediate deliverables and the end deliverables are an advice report and a presentation. The deliverables will be graded and are part of your grade for the course.

Several consultancy companies will be participating in this course by providing guest lectures, skills workshops and projects at their clients. At the same time, you also learn more about the different types of consultancies as we have a nice mix of small, medium and large consultancy companies that participate.

Registering for this course using an online form (DO NOT register yourself through Osiris)
The course is intended solely for MBI students in the business and/or technical consultancy profile. Furthermore, students should have completed at least half a year of their MBI program before starting with this course. Exceptionally, students from other masters of the ICS department can apply but there is no guarantee they will be accepted since their background should match the characteristics of the projects of the current course edition. Exchange students and students from other programmes are not accepted in the course.

Please use the following online form to apply to this course

Since you will be working for real clients we only expect motivated students to subscribe and therefore we ask you to write a professional motivation letter (max 1 A4 including letter head and signature) that should be addressed to the coordinator, Sergio España. Include a statement that says that you will invest at least the 210 hours that equal the 7,5 ECTS awarded for the course. The letter is uploaded using the online form mentioned above. By submitting the form you will be considered a candidate to take the course. The coordinator, along with other department members, will select a number of students based on (i) the quality of the motivation letter, (ii) the prior MBI courses taken and their grades, (iii) the amount of consultancy projects that have been acquired and how well they match the background of the applicants. You will be informed of the final decision mid June.

Team formation
We form the teams and assign them to the client projects. Given the nature of this course and to maximise the chances of success, we do not allow you to freely choose your team mates or your project. We need to keep all consultancy companies happy or they will stop working with us, and our way of managing this has yielded excellent results till now. We will inform you of your team members and your project before the course starts.

Non disclosure agreements
Later in the course you might be asked (by the consultancy or the client company) to sign a Non-Disclosure Agreement (NDA) in which you declare that you will handle in the best interest of the client and will not disclose any information you get from the client. In principle, this is fine. But, please, consult with the course coordinator before signing any NDA since we will want to assess it first (you are expected to act responsibly but we also want to protect you from abusive clauses).

Preferred knowledge
It is preferred that students have knowledge of business and ICT as typically covered by the bachelor courses INFOB1ISY, INFOB1OICT and INFOB3SMI.

Data mining

If properly processed and analyzed, data can be a valuable source of knowledge. Data mining provides the theory, techniques and tools to extract knowledge from data. Learning models from data can also be an important part of building a decision support system. In turn, the computer plays an increasingly important role in data analysis: through the use of computers, computationally expensive data mining methods can be applied that were not even considered in the early days of statistical data analysis. In this course a number of well-known data mining algorithms are coved. The type of problems they are suited for, their computational complexity and how to interpret and apply the models constructed with them are covered.

Technologies for learning

In this course you will study advanced software technologies for learning, such as serious games in which you have to develop a sustainable city, simulations such as a virtual company that you have to run, competing against several other virtual companies, intelligent tutoring systems for learning mathematics, physics, or logic, etc. In particular, you will study the underlying intelligence necessary to determine what a student has learned, what a student should do next, give feedback to a student, etc.In this course you will learn about the use of software technology to support student learning.
Student learning is supported by applications such as:

  • Serious games
  • Simulations
  • Intelligent Tutoring Systems
  • Exercise Environments
  • Automatic Assessment Systems

These applications use technologies such as:

  • Model tracing: does a student follow a desirable path towards a solution?
  • Static and (sometimes) dynamic analysis: what is the quality of a student solution?
  • Learning analytics: what do students do in a learning application?
  • User modeling: what does a student know?

which build upon:

  • Strategies, parsing and rewriting
  • Bayesian networks
  • Datamining
  • Constraint solving
  • Artificial Intelligence
  • Domain-specific technologies, such as compiler technology for the domain of programming.

Adaptive interactive systems

This course is about the design and evaluation of interactive systems that automatically adapt to users and their context. It discusses the layered design and evaluation of such systems. It shows how to build models of users, groups and context, and which characteristics may be useful to model (including for example preferences, ability, personality, affect, inter-personal relationships). It shows how adaptation algorithms can be inspired by user studies. It covers standard recommender system techniques such as content-based and collaborative filtering, as well as research topics such as person-to-person recommendation, task-to-person recommendation, and group recommendation. It also discusses explanations for adaptive interactive systems and usability issues (such as transparency, scrutability, trust, effectiveness, efficiency, satisfaction, diversity, serendipity, privacy and ethics). The course content will be presented in the context of various application domains, such as personalized behaviour change interventions, personalized news, and personalized e-commerce.body { font-size: 9pt;

Software ecosystems

vendors no longer function as independent units, where all customers are end-users, where there are no suppliers, and where all software is built in-house. Instead, software vendors have become networked, i.e., software vendors are depending on (communities of) service and software component suppliers, value-added-resellers, and pro-active customers who build and share customizations. Software vendors now have to consider their strategic role in the software ecosystem to survive. With their role in the software ecosystem in mind, software vendors can become more successful by opening up their business, devising new business models, forging long-lasting relationships with partnership networks, and overcoming technical and social challenges that are part of these ecosystem is a set of actors functioning as a unit and interacting with a shared market for software and services, together with the relationships among them. These relationships are frequently underpinned by a common technological platform or market and operate through the exchange of information, resources and artifacts. Several challenges lie in the research area of software ecosystems. To begin with, insightful and scalable modeling techniques for software ecosystems currently do not exist. Furthermore, methods are required that enable software vendors to transform their legacy architectures to accommodate reusability of internal common artifacts and external components and services. Finally, methods are required that support software vendors in choosing survival strategies in software ecosystems.introduce many new research challenges on both a technical and a business level. In a traditionally closed market, software vendors are now facing the challenge of opening up their product interfaces, their knowledge bases, and in some cases even their software. Software vendors must decide how open their products and interfaces are, new business models need to be developed, and new standards for component and service reuse are required. These challenges have been identified but have hardly been picked up by the research community.In this seminar topics on SECOs are discussed. These topics can range from consultancy oriented for product software companies to highly technical for software engineers. The course is largely dependent on student participation. Some example topics are:

  • Virtualized software enterprises
  • Open source software ecosystems
  • Market-specific domain engineering
  • Software ecosystem orchestration
  • Software development communities
  • Software product lines
  • Software product management
  • Publishing APIs
  • API development
  • Formal modeling of business models
  • Architectural implications of reusability
  • Keystone and niche player survival strategy
  • Software ecosystem creation
  • Economic impact of software ecosystems
  • Communities of practice and software reuse
  • Product software and software licensing
  • Software business models
  • Software ecosystem practices and experience
  • Software ecosystem modeling
  • API related topics: design, development, marketing
  • Software ecosystem models
  • A software ecosystem analysis method
  • Strategic advice for software vendors
  • API compatibility over subsequent releases

Data analysis and visualisation

What puts former criminals on the right track? How can we prevent heart disease? Can Twitter predict election outcomes? What does a violent brain look like? How many social classes does 21st century society have? Are hospitals spending too much on health care, or too little? When is a series of spikes in hundreds of website logfiles an operational problem?

Data analysis is the art and science of tackling questions like these by looking at data. Just as cartographers make maps to see what a country looks like, data analysts explore the hidden structures of data by creating informative pictures and summarizing relationships among variables. And just as doctors diagnose sick patients and advise healthy ones on how to stay healthy, data analysts predict important events and variables so we can act on this knowledge. Methods from statistics, machine learning, and data mining play an important part in this process, as well as visualizations that allow the analyst and other humans to better understand what we can conclude from the available facts.

During this course, participants will actively learn how to apply the main statistical methods in data analysis and how to use machine learning algorithms and visualizing techniques. The course has a strongly practical, hands-on focus: rather than focusing on the mathematics and background of the discussed techniques, you will gain hands-on experience in using them on real data during the course and interpreting the results.
This course covers both classical and modern topics in data analysis and visualization:

  1. Exploratory data analysis (EDA);
  2. Supervised machine learning and statistical learning;
  3. Unsupervised learning and data mining techniques;
  4. Visualization (throughout the course).

This course is essential as a basis for each track of the Master of Applied Data Science. If you want to register for this course, please also register for the Applied Data Science profile. Students that need to follow this course mandatory for the ADS profile need to enroll themselves before the end of September (information on how and where will be provided within the profile). Other interested students can enroll themselves during the FSW Elective enrollment in the beginning of October, depending on the still available place in the course. Further information on this procedure can be found on the website of one of the academic masters of the faculty of social and behavioural sciences.

Note also that if you are not an FSW student, the registration period may differ from your habitual one.

Please take notice: 7,5 EC instead of 5 EC.

Pattern recognition

In this course we study statistical pattern recognition and machine learning.

The subjects covered are:

General principles of data analysis: overfitting, the bias-variance trade-off, model selection, regularization, the curse of dimensionality.
Linear statistical models for regression and classification.
Clustering and unsupervised learning.
Support vector machines.
Neural networks and deep learning.

Knowledge of elementary probability theory, statistics, multivariable calculus and linear algebra is presupposed.

Enterprise architecture

What kind of business processes are important in our organization, and how can we support these processes using IT? What is the application landscape of our organization, and do we need to update it to improve the speed and flexibility with which we can do business? How can we manage our technical infrastructure to improve access to information for our employees but at the same time minimize security risks?In this course, you will learn the techniques that allow you to answer these and other questions. The core subject of the course is the modelling and analysis of enterprise-wide architectures (i.e. business process architectures, information architectures, application architectures, technical architectures, combination of architectures, and so on). In addition, we will discuss related topics such as risk management and business process modelling.

Software architecture

The course on software architecture deals with the concepts and best practices of software architecture. The focus is on theories explaining the structure of software systems and how system’s elements are meant to interact given the imposed quality requirements.Topics of the course are:

  • Architecture influence cycles and contexts
  • Technical relations, development life cycle, business profile, and the architect’s professional practices
  • Quality attributes: availability, modifiability, performance, security, usability, testability, and interoperability
  • Architecturally significant requirements, and how to determine them
  • Architectural patterns in relation to architectural tactics
  • Architecture in the life cycle, including generate-and-test as a design philosophy; architecture conformance during implementation
  • Architecture and current technologies, such as the cloud, social networks, and mobile devices
  • Architecture competence: what this means both for individuals and organizations

Natural language generation

The taught component of the course will consist of four parts:

I. General Introduction. In the first part of the course you will learn what the different aims of practical and theoretical NLG can be, what are the main elements of the standard NLG pipeline, how NLG systems are built, and how they are evaluated. Template-based and end-to-end systems will be discussed briefly.

II. Practical systems. You will get acquainted with a range of practical applications of NLG; a few will be discussed in detail: candidates applications are medical decision support, knowledge editing, and robo-journalism. Strengths, weaknesses, and opportunities for the practical deployment of these systems will be discussed. If time allows, we will devote attention to multimodal systems, which produce documents in which pictures or diagrams complement a generated text.

III. Module in focus: Referring Expressions Generation. We will zoom in on one part of the standard NLG pipeline, which is responsible for the generation of referring expressions (e.g., as when an NLG system says “the city where you work”, or “the area north of the river Rhine”). We will discuss a range of rule-based algorithms, and some that are based on Machine Learning.

IV. Perspectives on NLG. We will discuss what linguists, philosophers, and other theoreticians have to say about human language production, and how this relates to NLG. We may start with a Gricean approach, and continue with the Bayesian-inspired Rational Speech Acts approach. We will ask how accurate and how explanatory existing NLG algorithms are as models of human language production (i.e., human speaking and writing), and what are the main open questions for research in this area.

The core of the course will be presented in lectures. Additionally, students will be asked to read, present, and discuss some key papers and systems which illustrate the issues listed above.

Big data

Big Data is as much a buzz word as an apt description of a real problem: the amount of data generated per day is growing faster than our processing abilities. Hence the need for algorithms and data structures which allow us, e.g., to store, retrieve and analyze vast amounts of widely varied data that streams in at high velocity.

In this course we will limit ourselves to data mining aspects of the Big Data problem, more specifically to the problem of classification in a Big Data setting. To make algorithms viable for huge amounts of data they should have low complexity, in fact it is easy to think of scenarios where only sublinear algorithms are practical. That is, algorithms that see only a (vanishingly small) part of the data: algorithms that only sample the data.

We start by studying PAC learning, where we study tight bounds to learn (simple) concepts almost always almost correctly from a sample of the data; both in the clean (no noise) and in the agnostic (allowing noise) case. The concepts we study may appear to allow only for very simple – hence, often weak – classifiers. However, the boosting theorem shows that they can represent whatever can be represented by strong classifiers.

PAC learning algorithms are based on the assumption that a data set represents only one such concept, which obviously isn’t true for almost any real data set. So, next we turn to frequent pattern mining, geared to mine all concepts from a data set. After introducing basic algorithms to compute frequent patterns, we will look at ways to speed them up by sampling using the theoretical concepts from the PAC learning framework.

ICT entrepreneurship

A software product is defined as a packaged configuration of software components or a software-based service with auxiliary materials, which is released for and traded in a specific market.
In this course the creation, production and organization of product software will be discussed and elaborated in depth:

  • Requirements management: prioritization for releases, tracing en tracking, scope management
  • Architecture and design: variability, product architectures, internationalization, platforms, localization and customization
  • Development methods: prototyping, realization and maintenance, testing, configuration management, delivery; development teams
  • Knowledge management: web-based knowledge infrastructures,
  • Protection of intellectual property: NDA, Software Patents
  • Organization of a product software company: business functions, financing, venture capital, partnering, business plan, product/service trade-off, diversification

This course is explicitly meant for students Information Science and Computer Science. Pre-arranged or mixed teams are are no problem, it is the product idea that matters.

The aim of this course is to create a prototype and business plan for a novel software product. Students can join the course either with a product idea or without. In both cases your participation in the course must be formally approved.

Requirements engineering

The course will cover the following topics:

  • The RE process and its activities
  • Standards and tools
  • Agile RE, user stories
  • Requirements elicitation
  • Linguistic aspects of natural language requirements
  • From requirements to architectures
  • Requirements prioritization
  • Maturity assessment
  • (Verification of) formal specifications
  • Release planning
  • Requirements traceability
  • Crowd RE

All information about the course will be made available through Blackboard before the course starts.

To qualify for the retake exam, the grade of the original must be at least 4.

Mobile interaction

Mobile devices, such as smart phones and tablets, have become as powerful as traditional computers, often replacing them for various tasks. Yet, interacting with them remains challenging due to issues such as limiting form factor, mobile context, etc. On the other hand, it is exactly this form factor, context, and other characteristics of mobiles that provide us with new and exciting opportunities for alternative usages. Examples range from innovative mobile games, to mobile AR (augmented reality) applications. In this course, we will have a closer look at standard interaction with mobiles (e.g., via touch screen; including potential issues as well as opportunities), address new approaches, and look into related current and future research -- including wearable devices (e.g., head mounted displays, such as Google Glass, wristbands and smart watches, such as the Apple Watch). Concrete application domains include mobile gaming and mobile video.

Multimedia discourse interaction

Seminar Multimedia Discourse Interaction

Multimedia Discourse Interaction addresses the complexity of interacting with information present in different information carriers, such as language (written or spoken), image, video, music and (scientific) data. The goal is to convey information to a user in an effective way.

Knowledge of cognitive capabilities and limitations, such as information processing speeds, can be used to inform the design of useful and efficient ways of searching, browsing, studying, analysing and communicating information in a way that is appropriate to a user's task, knowledge and skills. Subsequently, the fragments of relevant information that are selected from multiple sources must be combined for meaningful presentation to the user. Models and theories exist, for example in artificial intelligence, but also in the fields of film theory and computational linguistics, that describe communication structures, such as narratives or arguments. These can be used to inform the process of selecting and assembling specific media fragments or selections of data into a presentation appropriate to an end‐user's information needs.

Information presentation consists of combining atomic pieces of information into some communication structure that facilitates viewers in understanding the relationship between the pieces. For example, in text, multiple words are strung together according to established structures, namely grammatically correct sentences. Similarly, a media fragment, for example a film shot, represents some atom of meaning. Fragments can be combined together into a communication structure meaningful to the viewer. This is precisely the task that a film director carries out. Individual communication structures, for example that relate different positions of an argument, for specific domains, for example the utility of war, have been modelled in the literature. When these are implemented and used to present video fragments to a human viewer, the video sequence is perceived as conveying a coherent argument and discourse.

The seminar explores literature from diverse subfields, including artificial intelligence, semantic web, multimedia and document engineering, providing a range of perspectives on the challenges.

Course from
This course is set up as a seminar. It challenges the participants to acquire and disseminate knowledge about a complex subject in an interactive way. The moderators make a pre-selection of relevant research papers and web references. Students are expected to supplement these with their own literature search. They are expected to take the lead on proposing, preparing and presenting projects. Participants will work in groups of 2 on a joint project. Group meetings are mandatory.

Exam Form

  • Attendance of meetings is obligatory
  • Individual: Oral presentations of various topics
  • Group: Report on project that also details the individual contributions

Seminar medical informatics

This seminar is about the development, implementation and evaluation of IS/IT in the health care domain, which can be labeled as 'medical informatics' but also 'health IT' or 'e-health'. Compared to the previous courses, this years' seminar will focus on medical apps and games. This is a relatively new and exciting field that is full of opportunities to explore and evaluate. It is about apps and games to help doctors in their clinical work, to help managers to govern their hospitals, to help patients to cope with their diseases. Three knowledge fields are combined in this course:
(1) Health care: what are the current challenges of health care, how do clinical and organizational processes in health care look like, how do health care systems, organizations and professionals work?
(2) Mobile health: what types of mobile systems are applied in health care, what type of apps do doctors, nurses and patient use - or want to use?
(3) Evaluation studies: what are principles and models to evaluate if apps and games in health will work? how to review apps and games in different stages and from different perspectives? The three fields will be addressed and integrated in this course. After this course, you have gained more knowledge about both the drivers and barriers in medical informatics, and of medical apps and games in health in particular.

Process mining

There is no content available for this course.

Only two seminars can be followed in one MBI programme. Apart from these courses, students can choose a small number of courses from other master programmes, after approval of the MBI Coordinators. Bachelor courses are not allowed in the Master’s programme, unless they are deficiency courses, prescribed during the admission process.

Thesis Project + Colloquium (43 EC)

In the final thesis project, you will plan and conduct research under the supervision of one of our staff members and an external coach. Research can be done on all subjects related to the list of courses or related to the interests of the staff of the department staff, with a focus on information science, after agreement with a supervisor within the department. The thesis project will contain both a scientific and an applied study on a specific business informatics topic. Students often perform these in collaboration with an external organisation, i.e. a knowledge or IT-intensive company. The project concludes with writing a thesis and a publishable paper based on your research.

Thesis results are presented and discussed in the Master’s in Business Informatics Colloquium, where all graduating students and staff meet during a biweekly gathering.

The research part consists of the following:

  • Thesis Project Part 1: 14EC
  • Thesis Project Part 2: 25EC
  • MBI Colloquium (INFOCQMBI4): 4EC

The thesis project can be started when all primary electives, secondary electives, and deficiency courses have been completed. Exceptions need to be approved by the program coordinator.