The programme combines courses from data science and marketing into one coherent master’s specialisation that is focused on using data to solve business challenges. As no advanced prior knowledge is required, core courses in computer and data science lay the groundwork for the more advanced seminars. The latter explore state of the art machine learning methods and apply them to real-life business problems.
The curriculum
This master’s specialisation consists of core courses, seminars, electives and a master's thesis distributed over 5 blocks of 8 weeks. In the first block, 3 core courses of 4 credits will each introduce you to the broad range of both knowledge and skills that you will acquire and perfect in the Master Data Science and Marketing Analytics.
The second and third block start with a focus on learning new methods, followed by a seminar where you will work on real-life business problems, putting the methods and skills obtained in earlier blocks to the test. These interactive seminars are a very important component of our programme. You will be involved full time in a seminar for an entire block, allowing you to dive deep into the material, guided by the lecturers. For these intensive courses, active participation and strong commitment is a must.
In the fourth block you will follow one final course and choose one elective.
You should already start thinking about your master’s thesis in January, but block 4 and 5 will be dedicated entirely to this task, which is based on research you have conducted yourself under supervision by a member of our academic staff. Erasmus Research & Business Support (ERBS) offers job market preparation sessions in block 5.
The curriculum consists of:
- 50 % computer science/machine learning
- 50 % marketing/business
In class
The key strength of machine learning techniques is their predictive ability. For marketing analysts this opens up many opportunities to improve customer experience, making shopping both more pleasant and effective, for example by providing accurate recommendations. Interesting business cases that will be covered include the composition of a set of relevant products, but also the identification of the customer’s stage in the buying process, allowing firms to provide the information that is most valuable at that stage to the customer.
Study schedule
The Take-Off is the introduction programme for all new students at Erasmus School of Economics. During the Take-Off you will meet your fellow students, get acquainted with our study associations and learn all the ins and outs of your new study programme, supporting information systems and life on campus and in the city.
Data science provides a new paradigm for the analysis of data. In this course, an introductory overview is given of data science. The main goal is to introduce several data science techniques. Amongst the techniques discussed are (penalized) linear and logistic regression including ridge, lasso, and elastic net regression, and principal components analysis. The techniques are categorized in supervised and unsupervised learning methods. Special attention is given to the bias-variance trade-off and penalty approaches that reduce the variance in prediction at the cost of some bias. The practical implication is to use cross validation to select optimal parameters with respect to predictive performance. The differences between statistical and computational approaches such as bootstrap and permutation testing for hypothesis testing are also discussed. Students apply the methods using the statistical language R in several group assignments.
In this course, students will have insights on:
- Introduction to marketing models
- Customer value models
- Segmentation and targeting analysis
- Techniques to position products
- Conjoint analysis
- Pricing and promotion decisions
- Analytics for digital marketing
Introduction to the R language design
- Data exploration and manipulation in R (base R / tidyverse)
- Graphical visualizations (base graphs / ggplot)
- Dynamic reporting with R-Markdown
- Getting data in and out of R
- Programming in R through functions and loops
- Simple web scraping with rvest
In this seminar several data science/machine learning methods are treated sequentially. First, students acquire the basics behind various machine learning techniques after which they deepen their knowledge and understanding of the methods by implementing and applying the methods to real data.
We discuss and apply several "state-of-the-art" machine learning methods. Students are asked to critically assess, present and discuss the material. Reviewing state-of-the art methods and their application in business settings will help students to assess whether (and how) data can be used to help answer real world business problems.
What students say about the course:
"Exhaustive coverage of the main state-of-the-art machine learning techniques, from students to students learning approach"
“The best way to learn something, is by trying to explain it to others. You are forced to know every part and detail of the subject.”
In the seminar Case Studies in Data Science and Marketing Analytics, students get the unique opportunity to solve real-world management problems working on real data and collaborating closely with real companies. Solving the business case hinges upon six data-science talents that determine the success of a data science project. These talents are the foundational pillars of the course structure: subject expertise, data wrangling, data analysis, storytelling, design, and project management (Berinato 2019, Harvard Business Review).
The case starts with formulation and conceptualization of the research question and immersion into the relevant literature to gain subject expertise. Next, students wrangle the data acquired from the company behind the case. Then, students first conduct relatively simple data analysis and obtain preliminary insights. Afterwards, they use more advanced data-science methods to validate these preliminary insights and enrich them. Based on these insights, students develop their stories to engage the company to take action. Finally, students design effective visual communication and present it to company management. Throughout this period, students apply project management to organize effectively various activities and teamwork.
What students say about this course:
- Challenge ourselves with a real-life case from a well-known company under demanding conditions.
- The seminar was for us the ultimate test of applying those methods we learned during the previous courses using a real-life dataset.
- The course showed that real life business problems are much more challenging than many of the toy examples we learn in other courses.
In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys or phone logs. This course provides students with tools that enable them to effectively implement and use text analytics in a marketing context. In the course the complete analysis process, from data input, summarizing and visualizing and rigorous statistical analysis, will be covered.
Next to the weekly lectures, students will make a group assignment to gain hands on experience with the techniques from the course.
Students choose one of the following courses:
- Data Science in Context
- Digital Marketing and Data Science
- other Economics and Business master's course
The thesis is an individual assignment about a subject from your Master's specialisation. More information about thesis subjects, thesis supervisors and the writing process can be found on the Master thesis website.
Disclaimer
The overview above provides an impression of the curriculum for this programme for the academic year 2024-2025. It is not an up-to-date study schedule for current students. They can find their full study schedules on MyEUR. Please note that minor changes to this schedule are possible in future academic years.