Voltijd
Engels
ARNHEM
30.0
1 februari 2021 t/m: 3 juli 2021 -- open

For more information:

info@han.nl | T 31 (0)24 35 30 500 from 09.00 to 16.30 | www.han.nl

For content information: Mr Robert Goedegebuure & Mrs Frazen Zondervan
E-mail: minor.datadriven-decisionmaking@han.nl

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For popular minors a draw takes place 3 to 4 weeks after the opening, 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!

In addition, if the number of subscribers after 3 weeks is below the norm; this minor may possibly be withdrawn. So if you are interested, sign up immediately!

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Note: For HAN students it means that, in case of cancellation of the first choice AFTER the period of decision (this takes about three weeks) they may re-subscribe for the still available minors.
Even then: Once a minor is full, it is closed!

A good overview of the HAN minors can be found in the minors app! The app is accessible via: http://www.minoren-han.nl/

 

Are you interested in a challenging career in the field of applied data science and do you have a special interest for Logistics or Marketing & Sales? Then you should sign up for this minor!

Organizations increasingly realize that the smart use of data (financial, market, logistics, or otherwise) has the potential to enhance organizational 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 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 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)
    • Foundation Mathematics & Statistics & R & Python
    • Data science for business – the CRISP model for data mining
    • Storytelling with Data– the art of data visualization
    • Business intelligence (Tableau / Power BI)
    • Introduction to data mining
    • Introduction to modelling
  • Business project (two periods of in total 16 weeks)
  • Specialisation track (period 2)
    • Track: Logistics & Supply Chain Management
      • Process mining
      • Simulation
      • Blockchain (incl game)
      • Forecasting
    • Track: Marketing & Sales
      • Clustering & market segmentation
      • Market basket analysis
      • Sentiment analysis (text analysis)
      • Artificial Intelligence (AI)

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 organizations in all sectors of the economy. 

The structure and contents of the curriculum will be aligned with the needs of modern organizations. 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 techniques used in data science.

The emphasis of the minor will be on applications relevant to organizations: 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.

Traditional education consists of a process of instructing students. For learning data science, is not enough given the infinite number of tools and applications and the dynamic nature of data science. It is simply impossible for any person to know everything there is to know.

A smart aim is for students 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 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

  • State of the art knowledge of data science
  • Being able to formulate a data science problem
  • Being to judge upon a data science solution
  • Being able to bridge the gap between managers and data scientists
  • Working in a project team, to solve a task with a time restriction
  • Learning to learn (metacognitive competency)·        

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

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 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 for minors that have been over-enrolled.

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:

  • Peer assessment and self-assessment
  • Presentations and role-playing
  • Portfolio
  • Case studies
  • Presentations
  • Written exams
  • Project plan
  • 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 8 - 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 an option

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 scinece from articles and workbooks

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