I have a master’s degree in Finance and Banking with a final grade of 106/110. Although I did not win the scholarship, I got the opportuniy of a 4-month paid internship at SOSE– solutions for the economic system s.p.a. As for my master’s experience, the first module is the one I preferred. Having a small background in statistics, it was not too difficult to follow the lessons. I enjoyed all the courses given in this module, especially the lectures in Unsupervised Learning. As for the two subsequent modules, I found it difficult to adequately follow all the subjects, not having a solid background in computer science. Furthermore, the concentration of different subjects, which require knowledge of different programming languages and IT tools without a background, has made it more complicated. I would suggest greater coordination between courses and a common line between them to made it easier. I am applying different machine learning algorithms on a dataset supplied by the company. As for the teaching secretary and the professors individually, my experience is positive. Both have always been helpful and kind.
I obtained a master’s degree in Engineering and Architecture at the University of Rome Tor Vergata with a final grade of 110/110. The experience of the master was altogether positive, thanks to an educational program that deals with current issues with a multidisciplinary approach and the presence of highly trained teachers. I am currently completing the master’s course with an internship at BayWa r.e. Operation Services, a company operating in the maintenance of photovoltaic systems, where I am dealing with analysis of data from field sensors, both with a “classic” statistical approach and with a machine learning approach, and the consequent implementation in reporting tools.
Daniele Del Monte
I graduated in Molecular and Cell Biology at the University of Tor Vergata with a final mark of 109/110. I’ve always been passionate about informatics, statistics and coding, so that after working few years as researcher and biomedical scientist in clinical field, I decided to leave my job in order to follow my passion in computer science. In 2019, I had the opportunity to attend the master’s course in Big Data in Business, winning the scholarship offered by Procter & Gamble. I started to study these subjects in a tireless way, falling in love with Data Science. This master gave me extensive knowledge in statistics, machine learning, coding and all other powerful tools to handle big data. That experience culminated with the opportunity to test what I learned in P&G and I am currently a Data Analyst in the Human Resources team.
Daniele Di Simone
After graduating in Economics and Business Administration, I graduated in Statistics, Economics and Business (curriculum quantitative methods for economic decisions) at the University of Bologna, with grades 101/110. I decided to enroll in the master in Big Data in Business to become more familiar with computer programming and to meet the new demands of the business world. The Master – in addition to having provided me with interesting ideas for study – allowed me to get in touch with other students with a course of study different from mine, making participation more interesting and profitable. I am currently a research fellow at the National Research Council (CNR) in Pisa and a PhD student in Public Administration Economics and Finance at the University of Bari.
I graduated in Economics at the University of Tor Vergata with a final mark of 110/110 com laude. In the last 2 years of studies I discovered a passion for statistics and data, which led me to investigate the sector. Born into a family of computer scientists, the link was immediate, and the opportunity to take a path as a Data Scientist offered by the Master in Big Data in Business was something that was going to unite a process started two years earlier. After winning the scholarship offered by Procter & Gamble, I completely dedicated myself to the master’s course, very intense both from the point of view of the study load and the expectations of the teachers. I laid an excellent foundation on the knowledge of the subject, going into detail with the teachers, really available throughout the course of studies, culminating with the opportunity to test what I learned in a role of “Data Analyst in Sales” at P&G.
I obtained the Master’s Degree in Management Engineering (110/110 cum laude) which allowed me to deepen the interesting field of Operation Research, besides providing me technical basis as programming and managing databases. Especially after my final dissertation in Machine Learning, I decided to take this II level Master in Data Science, which allowed me to acquire important knowledge not only on typical Machine Learning problems, but also on more specific topics such as Multivariate Time Series Analysis and Natural Language Processing. I won the scholarship with Enel, where I am currently finishing my thesis. The background I had when I started to work after the 6 months of exams in the Master, was absolutely solid and good to face a real Data Science problem. Besides that, I have been receiving many offers abroad, where I found out that such Master is definitely considered as a proper Master’s Degree in Data Science.
Three years ago I finished my Master in Big Data in Business and became a data scientist. 2016, between lectures and internship at the company, has not been easy. Many times at the beginning of that path I thought about giving up: I was 24 years old and I was among the youngest in the class, a graduate in management of innovation from a private university, together with many engineers and physicists (many of whom with a doctorate) and I didn’t absolutely know how to program. I have felt so many times behind. Many times I wondered why I did not go for an internship in consultancy like most of my peers. But then I remembered why I had chosen that path: if I really wanted to deal with innovation I had to acquire hard skills or I would have been just fluff. Looking back today I am happy to have resisted, “if you are the smartest person in a room you are in the wrong room”. I definitely never felt smarter that year, but I worked on my weaknesses and built on my strengths; I realized that even though I wasn’t the strongest with algorithms, thanks to my background in business, I was faster to understand what insights my managers needed. Today I look at this photo and smile at what I’ve done and at what I still have to do.