Statistics - MSc Data Science capstone project

MSc Data Science students-

The project provided an effective way to apply various concepts learned in theory into a more practical environment. It also allowed to improve soft skills such as teamwork, leadership and presenting.

Our company supervisors were very friendly, helpful and approachable. They gave us all the resources we needed to conduct our project.

Capstone projects provided a good opportunity to learn and apply knowledge to real life problems from big companies. The all-hands meetings for the Capstone Projects were a good mechanism to learn about other's work and sharpen soft skills (i.e. public speaking).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Names

Milan Vojnovic

Department

Statistics

Overview

We introduced a capstone project module in our MSc in Data Science programme, which enables our students to apply the knowledge gained in our programme to real-world data science projects, by working in collaboration with capstone project partners. 

Target audience

Students of the LSE MSc in Data Science programme. 

Details

We developed a new capstone project module for our MSc in Data Science programme which began in 2017-18. The projects enable our students to apply what they learn in the programme by working on real-world data science projects in partnership with capstone project partners. The capstone project partners are either companies or, in some instances, LSE academics and Professional Service divisions. The capstone project partner defines the project scope and objectives, provides access to datasets, and actively participates in the project supervision together with LSE supervisors.

We have established partnerships with a wide-range of companies covering different industries, including software industry, retail, online services, and marketing agencies. During the first two years of our programme, our capstone project partners have included companies such as Adobe, Facebook, Google, Microsoft, Proximity London, Telefonica and Tesco. Our capstone project partners also include several LSE academics from Statistics and other departments such as Government, and Professional Service divisions such as Student Services.

The capstone projects enable our students to study important research questions using real-world datasets and cutting-edge data science technologies. The research questions are largely concerned with understanding user behaviour in a wide range of daily tasks such as those that arise in work environments, when using online services, and online and in-store shopping scenarios. Some of our projects are aimed at understanding national government systems by using large amounts of publicly available transcripts of political debates in parliaments.

Our capstone project module allows our students to put their knowledge in to practice, but also to practice and develop their communication skills and best business practices. This is achieved through interaction with capstone project partners, one-to-one meetings with LSE supervisors, and a series of “all-hands” meetings that are held throughout the year in which students present the progress of their work to the whole class. The all-hands meetings provide our students with an opportunity to learn about the work of their classmates, think critically, provide and receive feedback, and develop their communication skills. In addition, we try our students present their work at relevant data science workshops, such as the Workshop on Data Science Theory and Practice, which we organised in March 2018.

Project supervision and liaising with industry partners requires substantial staff input.  Supervision is shared by academics from across the department.

The key motivation was to enrich our MSc in Data Science programme with a module that would enable our students to work on real-world data science projects in collaboration with industrial partners. We believe that such a module greatly assists in student development and increasing their employability. Being a modern data scientist requires a wide-range of technical skills, including methods and tools in the areas such as computing, machine learning and statistics, some special domain knowledge, as well as communication and presentation skills. We believe that our capstone project programme greatly assists in training future data scientists.

Impact

The module has had a great impact on both students and staff. Students gained valuable experience by working on projects, in particular, understanding what it means to work on a real-world data science project, how to apply theoretical concepts to practice, and how to interact with business clients. We received very positive feedback from students, both informally and formally through an end-of-course anonymous survey. We have also observed a high demand for our programme, which may be partly attributed to our capstone project module.

Our staff also benefited from engaging with industry by learning about interesting problems, getting access to data, and building long-term industrial connections.

The quotes displayed to the left are taken from a departmental survey where students were able to comment anonymously on their programme, including the Capstone projects.

Next steps

Our plan is to continue running our capstone project module with a portfolio of our capstone project partners. We would like to maintain long-term partnerships with various companies, but also allow for new partners to join our programme. This allows us to offer interesting project topics to our students and ensures we keepup-to-date with interesting data science challenges that arise in various organisations.

We believe it is important for academics to invest effort in initiating and maintaining relationships with industrial partners and to actively engage in formulating and supervising capstone projects. This ensures that our students work on challenging and interesting research problems, and at the same time, it allows our academics to conduct research using interesting real-world datasets and cutting-edge software technologies.