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At the end of this course, students should be:
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Better prepared for a data-driven world in practice by providing them with both a ‘big picture’ view point and ‘hands on’ experience with data analytics (to a level on which students understand enough to pursue the missing pieces);
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Be familiar with general terms related to data analytics, data-driven decision making and digital transformations in organizations in general;
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Knowledgeable regarding the social, economic, political and ethical concerns related to data analytics;
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Able to critically think and reflect on outcomes of data analytics and translate these into responsible (business) solutions and insights;
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Able to critically reflect on their own learning process during this course;
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Capable of presenting a business solution based on their analysis of data, in which they account for methods used, conclusions drawn, and reflect on (social/societal, ethical, political) implications as well as on their learning process.
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In an era in which (big) data seems to be the resource no organizations can do without anymore, making good use of such data and turning them into responsible business solutions and insights becomes a core competence for organization’s viability. Consequently, new analytical methods have been developed over the years to extract insights from massive datasets using machine learning and artificial intelligence techniques, wherein the power of computational algorithms are used to process and analyze data. These developments require different skills from future business analysts, advisors and managers such as a basic understanding of what (big) data is, what data structures are and the different algorithms that suite each data type. However, big data and data analytics also raise a whole series of ethical questions with respect to their use in ‘dataveillance’ (surveillance through data records), social sorting (differential treatment to services), anticipatory governance (predictive profiling), control creep (data generated for one purpose being used for another) and the extent to which their systems make an organization hackable, to name just a few (Kitchin, 2016). This course will address all these issues in order to better prepare you for working in contemporary organizations.
This course is not designed to turn you into a data analyst, nor does it cover all ethical dilemmas or other unforeseen consequences. However, we start you off with a crash course ‘big picture’ view of data and algorithms then take you through weekly ‘hands on’ assignments in a lab setting. These sessions address a sample of the data and analytics algorithms you were presented with at the start of the course, and give you the chance to roll up your sleeves and take a stab at it yourself. These weekly assignments will help you get familiar with the data analytics tools you will use for your final assignment. You will work weekly in pairs or triples aiming towards submitting your method description and a final media presentation in which you reflect on your methods, your business solutions and insights, possible ethical implications (and how you suggest to tackle them), and a reflection of your own learning process in this course*.
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For this course, it is recommended (but not mandatory) to have finished a quantitative research methods course on a Master level (Advanced Research Methods, (ARM Part B) (MANMOD012) or Methodology in Marketing and Strategic Management Research (MAN-MMA032A) or Research Methodology (MAN-MBAM005). Student interest in data-driven analytics using machine learning and artificial intelligence is advised.
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Final group assignment (student pairs or triples): video presentation of business solution based on data analytics along with solution description and files.
Entry Requirement exam: All (in between) group assignments have to be completed with sufficient.
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