It is fun and inspiring learning and working with like-minded people from various disciplines and backgrounds. While each one is different, the common amongst all students is the passion of learning on the world of data and data science, as well as to develop one’s skills, knowledge, and career prospects. Studying in a diverse classroom can also help one to develop cross-cultural competence, communication skills, and adaptability that are essential for working in a globalized world where data science is increasingly relevant. You also get to learn from students' past experiences as well. Collective projects help you strengthen your weaknesses but most importantly work on complex projects that require multiple minds. Importantly, learning from professors, whose courses are updated and adapted every year to the current business trends, on the various and complex data topics in this collective learning environment is truly fun.

I have always been fascinated by data science and how it can help businesses make better decisions. That’s why I decided to pursue a master’s degree in data science, which is an interdisciplinary program that covers topics such as statistics, programming, machine learning, data visualization, etc. This course truly helped me learn lots and provided valuable skills that prepared me for my dream job. My dream job was to become a data scientist in the retail industry, especially in the field of e-commerce. I wanted to apply data science tools and techniques to help e-commerce retailers with their strategic objectives such as customer acquisition, customer retention, product recommendation, pricing optimization, inventory management, fraud detection, etc. I knew that this was a challenging but rewarding career path that required both technical and business acumen. Fortunately, I was able to land my dream job after completing my master’s degree. I applied for a position at France's leading retailer that was looking for a data scientist with experience and passion in retail analytics. I impressed them with my portfolio of projects that I had done during my course work and with my knowledge of the latest trends and technologies in data science.