MSc in Economics and Business - Specialisation in Data Science and Marketing Analytics
Theory and practice from computer science, marketing, economics, and statistics
Modern businesses and organizations are increasingly involved in collecting and processing vast amounts of customer and operations data. The resulting (big) data are more and more seen as an important resource for businesses. In the Data Science and Marketing Analytics program, students focus on the tools and skills that are needed to analyze such (big) data in modern businesses and turn it into meaningful insights.
In particular, Data Science and Marketing Analytics combines theory and practice from computer science, marketing, economics, and statistics, in such a way that the potential of big data can be exploited successfully to create greater value for the consumers and firms.
Data science has been dubbed the sexiest career of the 21st century, according to Harvard Business Review. Given the growing awareness of the possibilities of exploiting data science for marketing analytics in business, the data science skills acquired during the Data Science and Marketing Analytics program are expected to provide graduates with excellent job prospects. According to Trevor Hastie, the John A. Overdeck Professor of Statistics at Stanford University, it is all quite obvious: “Big data is everywhere - it drives web search, web advertising and quantitative finance, to name a few industries. Data Science plays a fundamental role in this new Economy. With its strong history in data modeling, Erasmus School of Economics is well poised to train a new generation of data scientists.”
Data Science and Marketing Analytics graduates are therefore expected to have many job opportunities in various sectors of the economy such as (online) retailing, financial services, consulting, and healthcare. For example, many businesses and organizations are either setting up or expanding business analytics/customer analytics/marketing analytics units. The combination of marketing and economic knowledge with data science skills will be of great value to our graduates and the companies that hire them.
“Many big companies use machine learning techniques to recognize patterns and predict. This subject prepared me to apply the learned techniques in practice.”
Equal Shares of Data Science and Marketing courses
The curriculum of the master specialization Data Science and Marketing Analytics consists of roughly equal shares of data science and marketing courses.
The program starts with a course “Introduction to programming” and a course “Business analytics”. These two courses are supplemented by the course “Strategic Marketing Decision Making” to introduce statistical techniques commonly used in Marketing.
In the second block, students take the seminar Data Science. In this seminar, state-of-the-art machine learning methodology is introduced along with essential data preparation and collection skills to conduct valuable analyses. The seminar puts a strong emphasis on application of the methods in ‘R’ rather than focusing on underlying statistical theory. Students have to actively participate in the seminar through presentations, written reports, group discussions and applied projects where machine learning techniques are applied to real-world cases. Successful participation in the seminar enables students to gather and process complex data structures by selecting and executing appropriate tools.
In the third block students choose, depending on their personal goals and preferences, a marketing seminar focused on customer analytics, customer relationship management, consumer channel dynamics, or supply chain management and transportation.
Block 4: Thesis and elective courses
In block four, students start working on their thesis whilst completing one elective course in marketing or economics and a final data science course: “Advanced marketing and media analytics”. In this course, an overview of the latest and most advanced machine learning methods, with an emphasis on prediction for marketing (such as sales, communications, usage, etc.), is given. Once again, the focus in this course will be on application rather than on mathematical/statistical rigor.
Cost & Fees
- € 2.060 EEA/EU students*
- € 14.700 NON-EEA/EU students**
*This is the statutory tuition fee for Dutch students, students from another country within the European Economic Area (EEA), the European Union (EU), Switzerland and Suriname who meet the statutory conditions.
**This is the institutional tuition fee for students for whom the statutory tuition fee does not apply. You have to pay the institutional fee if you:
- don’t have an EU-, EEA, Swiss or Surinamese nationality.
- have finished a Dutch program and want to follow another program on the same level.
This school offers programs in:
Last updated August 31, 2018