For content information:
Oliver J. Ntenje, Dennis Moeke, or Mr Witek ten Hoven
E-mail: minor.datadriven-decisionmaking@han.nl
website: Data-Driven Decision Making in Business (hanuniversity.com)
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- For minors starting in September, after the registration period in March, a draw takes place in April if there are at that time more subscribers than available places.
- For minors starting in February, after the registration period in October, a draw takes place in November if there are at that time more subscribers than available places.
For the minors with places still available applies until the closing of the subscription period: Once a minor is full, it is closed!
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Are you interested in a challenging career in the field of applied data science and do you have a special interest in business related challenges? Then you should sign up for this minor!
Organisations increasingly realise that the smart use of data (financial, marketing, logistics, production, human resource, or otherwise) has the potential to enhance organisational performance. In practice, however, becoming data-driven – and particularly generating new value from data – is not as simple as it sounds. Often, managers lack an understanding of what data science can bring them, and data science professionals lack the skills to tailor the information to the needs of the decision makers.
During this minor you will learn how to connect the technical expertise of data scientists to specific business and cross-cultural expertise. You will gain enough data science skills to spot opportunities for such techniques to solve a variety of business problems and prepare for a career of further advanced learning in data science.
Real business project and experts
Right from the start of the minor, you will put the acquired knowledge and skills into practice by working on a real-life business project. The modules are developed and supported by experienced professionals and data scientists.
It should be noted that the minor also prepares you to meet the entry requirement of the MSc Applied Data Science of HAN (https://www.han.nl/opleidingen/master/applieddata-science/deeltijd/)
Topics
- Foundation course (period 1/3 - 10 weeks)
- Refresher course Mathematics, Statistics, and Python.
- Data science for business – the CRISP data mining model.
- Storytelling with Data– the art of data visualization
- Business intelligence (Tableau / Power BI)
- Introduction to data mining programming
- Introduction to modelling
- Business project (two periods, approximately 16 weeks)
- Data Science Tools and Techniques (two periods, approximately 16 weeks)
- Various Data Science Tools and Techniques are taught and practised to equip the students with the right skills to work on the business project and develop skills for later use.
- Relevant workshops and (guest) lectures provide students with insights and use of some tools and techniques.
- Students use the tools and techniques to work on an individual project of their choice to showcase their individual and personal skills mastered during the minor.
- Examples of the Tools and Techniques are:
- Python
- Disco / Celonis
- Tableau
- Process mining
- Data mining
- Data Visualisation
- Simulation
- Forecasting
- Clustering & market segmentation
- Market basket analysis
- Sentiment analysis (text analysis)
- Artificial Intelligence (AI) / Machine Learning.
Type of exchange course
This is a differentiation exchange course. This means it enables you to develop your professional competences in a different/broader context.
Block exchange course
This exchange course is offered once or twice a year in a block during a semester.
This is a continuation exchange course. You are interested in conducting more research and are preparing to continue your studies at a university (or to enrol in a Masters degree course at a University of Applied Sciences).
The key objective of the proposed minor in data driven decision making for business (3DMiB, for short) is to prepare students of economics and business studies for the rapidly growing use of data science, by organisations in all sectors of the economy.
The structure and contents of the curriculum is aligned with the needs of modern organisations. The objective of the minor is explicitly not to train the students to become data science experts with high-level programming skills, but rather to provide them with a sound overview and intuitive understanding of the most prevalent tools and techniques used in data science.
The emphasis of the minor is on applications relevant to organisations: students have to learn to critically analyse, evaluate, communicate and implement the findings, for effective use of the data.
The minor aims at bridging the gap between decision-makers (managers) and data scientists. In the optimal situation, both groups have sufficient knowledge about each other’s domain to effectively collaborate.
The student will be equipped to 'learn to learn'. After the minor, they should be well equipped to find their way, rather than get lost in the growing amount of data science tools, techniques , and applications. Interestingly, a smart education approach resonates well with the development from traditional programming and the use of computers to carry out repetitive tasks more efficiently than human beings, to machine learning seeking rules and algorithms to solve problems rather than answers to specific questions.
Learning outcomes
Upon completing the minor, students will be able to:
1. Break problems into component parts, extract key information, and develop descriptive models to understand complex systems or facilitate problem-solving;
2. Formulate problem definitions suited for technology-assisted methods such as data analysis, abstract models and algorithmic thinking in exploring, and finding solutions.
Competences
- Gain knowledge of data science
- Ability to formulate a data science problem
- Ability to bridge the gap between managers and data scientists
- Work in multidisciplinary teams from different study programme.
An exchange course will be of most benefit to you if it complements your study and/or your professional profile, is at an appropriate level and does not overlap with your major.
For whom?
Students of:
- International Business
- Logistics Management
- Bedrijfskunde
- Marketing (Commerciele Economie)
- Finance & Control (Bedrijfseconomie)
- Technische Bedrijfskunde
- Business related study programme
This course is intended for highly motivated students in their 3rd year or later. An affinity with and knowledge about International Business, Logistics & Distribution, Finance and Marketing, ICT and Business, or other business related knowledge is required. You will also need to be able to work as a team player in a project setting.
Admission requirements
- Good command of English (B2 level: equal to English at HAVO level)
- Not afraid of numbers and computers
- General business background from your studies (major)
There is a maximum of 30 participants per semester for this minor.
Mind you: after the enrolment period has expired lots will be drawn to assign places if there are more students enrolled than the spots available.
Nice to know
- Basic knowledge of MS Excel
- Interest in Data Driven Decision Making
- Eager to learn new techniques
Assessment
During this minor your performance will be assessed in the following ways, among others:
- Peer assessment and self-assessment
- Presentations and role-playing
- Portfolio
- Case studies
- Presentations
- Written exams
- Project (group)
- Project (individual)
- Cases
- IT applications
This exchange course has the following schedule and working methods:
Schedule
Time table HAN International School of Business (ISB), one semester of 2 periods of 10 weeks per period.
Students are expected to be available from Monday to Friday, so doing a part-time job or taking other subjects from Monday to Friday between 8.45 - 17.30 is not advisable.
The business project activities may take place at the business partners' location or at HAN-ISB campus, depending on the type of business project.
Working methods
- IT simulations
- Case teaching
- Lectures
- Consultancy hours
- Project learning
- Guest lectures
- Project work
State of the art literature in the field of data science from articles and workbooks.
Most of the literature required will be made available as we strive to use up-to-date literature.
Studenten van de HAN kunnen zich eenvoudig aanmelden via OSIRIS.
Externe studenten kunnen zich eenvoudig aanmelden via Kies Op Maat.