As a part of the programme MSc Business Analytics programme carry out a practical consulting or independent research-based project with business and support in finding a solution to a problem the company needs solving. They use the skills and knowledge they have developed on the programme to analyse the data and come up with solutions to help the organisation, better its business, change a process or just get more of a throughout understanding of its customers and target market.
MSc Business Analytics alumnus Yukun Zhang shares his experience in finding and carrying out his research project with Syft a company that helps people find flexible work that suits their schedule.
Can you tell us a little about what you have been doing since you graduated from the MSc Business Analytics programme?
I am currently working for a telecommunication technology company where I am responsible for project management, primarily supervising the processes of projects, managing new and updated software releases and monitoring the response times to troubleshooting and bug-solving.
Why did you want to study Business Analytics and why at UCL School of Management?
I studied Business Management for my undergrad. It covered a lot of theories and concepts which for me, were a little bit abstract. Therefore, when choosing the major for the Master’s I wanted to learn some practical skills that could help me apply those theories and concepts to solve real-world problems. That’s why I decided to study Business Analytics.
Before applying I compared the programme structure, course content and other factors like the location and reputation of the university. The main reasons for choosing UCL School of Management were; the location of the School of Management in One Canada Square in London’s Canary Wharf and the content of the programme. The programme contained lots of skills that were essential to become a business analyst, and more importantly, the programme and modules not only taught us how to code and predict data but also how to solve business problems in an analytical way.
How did you find/why did you choose your project at Syft?
Students on the Master’s programme select five companies and then the school allocates three companies for each student to decide from. I looked at the detailed descriptions of the projects that each company offered, and I found that the project offered by Syft was related to what I had learnt on the programme. So I thought this project could be a good opportunity to try and apply something I had learned in class.
Tell us more about the project and how it worked?
When narrowing down the three business project options, we were given more information about the data that companies could offer as well as the information about the proposed projects or the problems that companies wanted to solve. Syft were looking for a student to support with the issue of predicting time series data which was an area of data analytics I studied in the predictive analytics course. So I used the methods and the models I learned on the module and in my video interview explained how I could apply them. The Syft team were interested in the models I planned to use, so I was offered the project. Following this, I came to their London office and met the CTO who was also my project supervisor. We discussed the structure and specifics of the data I would use, the methods I would use and their expectation of the predicted result. After this meeting, I had a full understanding of the project and their demand, then I fed back to my academic supervisor and they provided some suggestions on how to start the project and also how to balance the requirement of an academic dissertation and the expectations of the company.
After discussing with both the company and my academic supervisor, I generated an outline of the project, got their approval and got to work.
What was the problem you identified and how did you help the team resolve it?
Syft wanted support to help them predict the number of workers that would be required in the future when implementing different working methods. They were not concerned with accuracy of the prediction rather they were more interested in the comparisons among different models and the recommendations for future predictive models.
For my project, I tried three different methods for predictive time series data; Supervised Learning, Autoregressive integrated moving average (ARIMA), and Long Short-Term Memory Networks. Based on the performance of those models, I offered some recommendations for their future data collection and model establishment.
Do you think the skills that you learnt on the programme (and which ones specifically) helped you support Syft?
Most definitely. I learnt a lot from the group projects, such as the necessary steps to take when carrying out a business project and the individual assessments helped me a lot when structuring the dissertation project. The programme helped me develop the skills needed to break down a big project into small tasks understand how to work on efficiently, undertaking each stage on a task-by-task basis – a skill I have found extremely useful even in my current role.
What was the most challenging and most rewarding about your work with Syft?
Understanding and applying the Long Short-Term Memory Networks model. This model, although it was covered in our classes, is still very complex and I find it difficult to fully understand and actually apply it. I overcame this by conducting lots of research and viewing many practical examples about how to use it. When I finally finished that model and got the result, I was very excited and also quite proud of myself.
What did you learn from the project?
Throughout the entire time working on the project, I got a deeper understanding of the models and the methods I used to analyse and predict data. I also learned how to use the available resources, ask for advice from tutors, professors, and colleagues and read code and examples written by other data scientists. You can always learn something new and get inspiration from those around you.
Do you think the project helped prepare you for working in the industry and if so how?
I have not had many opportunities since then to use the models I learned in the programme or the project. Although some soft skills, like how to identify, analyse and solve problems are still very useful when handling problems at work.
What advice would you give to future MSc Business Analytics students to succeed with the project and the programme overall?
Use the resources around you! The program offered excellent teachers and colleagues who are always willing to give advice and offer help. Take every lecture, assignment, and project seriously and then when you are faced with the dissertation project or even the future problems at work, you will find that you already have the necessary skills and tools to overcome the difficulties and challenges.
Interested in learning more about how our MSc Business Analytics programme or how UCL School of Management students could help you with a specific project? Learn more here or contact Partnership Manager, Halimah Austin.