Digitise, Optimise, Visualise
USI School on Digitise, Optimise, Visualise in cooperation with FoMICS
The USI DOV School will introduce you in a self-contained way to optimisation, data science and machine learning, while covering cutting-edge applications to economics and finance. You will learn the relevant mathematical foundations and how to formulate, visualise and solve problems. You will also understand how to access data and optimisation libraries, in order to solve a given problem with the appropriate methods. In 4 weeks we will guide you from zero to cutting-edge applications and knowledge in optimisation, data science and machine learning, which will prove useful in your work as a student, researcher, or industry professional.
Our programming environment of choice will be Python, owing to its importance in data science and machine learning. Our learning approach will be interactive and include Python workshops developed within Nuvolos (http://nuvolos.cloud), a browser-based complete cloud in which students and professors will efficiently work together on files, datasets, code and applications.
After completing the four modules in the USI DOV school, you will:
Possess key mathematical foundations for data science and machine learning
Be able to formulate, visualize and solve optimization problems for data science and machine learning
Know how to implement numerically appropriate solution algorithms
Be familiar with key cutting-edge applications in economics and finance
Send questions to [email protected].
I’m Paolo Montemurro, Digital Finance student at USI, where I’m currently writing my thesis with professor Peter Gruber. Before my master I completed a bachelor in managerial economics at UNIUPO, and I was playing and teaching tennis professionally. During this lockdown I’m remotely working for USI (Algorand project) and Covid19Datahub. In addition, I’m allocating my spare time to develop trading algorithms.
Tell us about your experience at Digitise Otpimise Visualise Summer school
Well, it all started with an email of Professor, and director of the DOV school Schneider Paul. Davide Brignoli and I ranked in the top 25 in the ETF portfolio challenge, and he gently offered us the participation at the school. At a first sight, I was very interested in the topics: technology, programming and visualization have always been part of my passions. At the same time, however, I was quite scared because my mathematical background was not of the strongest ones. However, I decided to accept the challenge and attend the DOV school… I didn’t regret that!
When I now think about the experience at DOV, I remember a warm and friendly environment where I met fantastic people. Yes, because in addition to learning, the DOV school gave us an opportunity to build a family: we were meeting at 8 in the morning, having lunch at “mensa” together, finishing lecture at 17 and… Going out together in the nights!
How useful was Digitise Optimise Visualise Summer School for you?
Surely the DOV summer school helped me mastering my digital skills. During the first week held by Professor Gruber, I discovered new technologies such as Anaconda and Jupyter lab, and improved my formal data visualization knowledge. These new technologies helped me a lot in writing the thesis code, and the visualization skills, gave me the boost I needed to create Coronamapper.com.
During the second week I discovered the efficiency and power of CVXPY/CVXOPT library that deals with linear, quadratic and conic programming (and not only, I imagine!). That is extremely useful to backtest trading strategy, but unfortunately I didn’t have the chance to apply it yet.
The last week held by Professor Trojani has been the most difficult for me: I struggled in understanding all the concepts, most likely because I didn’t prepare myself enough before the course.
Which key take aways?
- Usage of Python and visualization techniques.
- Quadratic / Conic programming with CVXOPT.
One personal suggestion? Do not to surrender at the first difficulty. Go again through the material provided, ask question and you’ll overcome the obstacles…
What would you like to accomplish in the future?
I currently have dozens of projects going on right now.
In the short term I would love to finish my master thesis and start earning some positive returns from the trading algorithms that I recently developed.
In the medium-long term I would aim to find (or why not, to create) a job that matches with my passion for Data-science and Finance. I’m sure the skills acquired at DOV and in the master at USI would help succeeding in the mission.
I'm Massimo Caprari, a second year, quantitative finance student at USI and I'm from Italy
What have you studied?
After a bachelor in Economics, I decided to increase my knowledge in the world of finance by attending the master course offered by USI.
Tell us about your experience at Digitise Optimise Visualise Summer school
The Summer School is a full immersion experience that allows you to refine your knowledge in Python, and it gives you the opportunity to gain a complete new approach and vision to financial (and not only) optimization problems.
How useful was Digitise Optimise Visualise Summer School for you?, which key take aways?
It was extremly useful. Thanks to the time I spent in the course, I had access to the knowledge of how to build and use very powerful and efficient Python tools under the theoretical point of view of convexity, and how to transform very difficult problems into convex ones, much easier to solve.
Also one of the hardest thing to do, when you reach a target, is explain such result in the most comprehensible, clear and concise way possible to your boss, your colleagues or your audience. In order to do so, the Visualization component of the course was extremely helpful to unravel the issue of "visualization" in programming languages. Such skills will be helpful in every context.
What would you like to accomplish in the future?
In the future I will continue to develop the skills and the knowledge I gained in the DOV experience by trying to use them in the day by day workload and also for the thesis.