In less than a month, the 2020-21 cohort of the Master in Business Analytics & Big Data will be graduating from an intense year of classes, projects and presentations. Thinking back to all the challenges and rewards, I thought that by jotting down my tips and lessons learned, I would be able to inspire future students of this wonderful multidisciplinary program.
Prior to starting the Master in Business Analytics & Big Data, I was working in customer service and business development for a mobility startup in Madrid. I kept thinking that many of the tasks and projects could be accelerated by leveraging data-focused approaches. For example, new business accounts could’ve been generated in a more cost-effective way by developing a web-scraping solution oriented towards industry-specific blogs and repositories. Or, the data generated from the product’s web application could’ve opened up a new revenue stream by developing an API gateway to share information with other mobility actors complementing the startup’s service.
It was during this experience that I realized that my previous studies were outdated. My learnings didn’t offer me a comprehensive vision of business analytics and big data today. I was sure the solutions that we needed existed, but I didn’t know what the right steps were to implement a data solution, nor what the best data tools were. That’s how I knew it was time to apply for the Master in Business Analytics & Big Data.
What to expect in the Master in Business Analytics & Big Data
The Master in Business Analytics & Big Data at IE University is a challenging program spanning over the course of 10 months. Despite the COVID-19 pandemic, I rose to the challenge to attend class in person, benefitting from the university’s hard work to ensure a safe learning space.
The master’s content spans four main areas of learning: Programming, Data Warehousing, Machine Learning and Distributed Computing. The classes are very hands-on, and require students to team up and present their projects at the end of every trimester. Already having knowledge in the first two topics, I was most looking forward to the latter two topics.
The course structure was well organized and allowed students to home in on specific topics according to their interests and preferences. The first semester was very much introductory and laid the foundations for data structures, coding skills and machine learning. The second trimester focused on programming related to data analysis, as well as learning the tools for distributed computing. The final trimester has really gone deepest in specific subfields of AI and machine learning methods, allowing the student to choose which subfield they’d like to specialize in.
Hard work pays off: the program’s biggest challenges
Throughout the year, tight deadlines kept the pressure high. Group coursework and team presentations were cornerstone moments as they helped us materialize and present the knowledge we acquired in the previous months of learning. Having two datathons organized by external companies acting as stakeholders made the experience much more realistic, helping us understand the challenges present in team-based data projects. We also enhanced our soft skills, which were needed to give presentations, collaborate with others and prioritize tasks.
From the very start, one challenging aspect of the program was clear. Integrating into a class of 30 people from distinct cultural, professional and educational backgrounds was going to be difficult. Some people think and work the same, while others have very different ways of tackling a challenge—and all equipped with a varied skill set. From the get-go, it’s essential to meet as many people as possible and identify those students who complement your skill set. This becomes very important when asked to form workgroups, because it allows you to create a balanced and diverse team with an equal distribution of work. Knowing who to rely on for which tasks is very powerful.
Outside the classroom, though, it’s important to enjoy the journey and remember that everyone is struggling with the same projects and deadlines. Meeting up, exchanging ideas, and asking for help always pays off. The people who have enjoyed the program the most are those that have shared the ups and downs of the master’s together, not alone.
Am I right for the Master in Business Analytics & Big Data?
A very common concern for program applicants is the level of coding fluency needed to apply. Fear not! Some students come to the program without ever having coded in their lives. However, coding should be demystified as soon as possible. The pre-course is highly recommended for this, because starting the semester with some coding basics under your belt will definitely help later on when the topics become more complex.
To a certain degree, this course allows you to choose your preferred programming language, between R and Python. My personal advice would be to choose one of the two to focus on. Knowing the basics for both is essential, but after having worked with both, it’s important to start specializing in one or the other. This means exploring and consistently working with the same packages, building a notebook code library to reuse in ML projects, and earning additional certifications from online courses for an advanced understanding of the language. This will greatly help in the long run to gain mastery in one language and bootstrap ML models much faster.
In the end, as long as you have the motivation and curiosity to learn, this master’s is suitable for any student with a varied academic background. In my group projects, I’ve been inspired by the caliber of work and dedication put in by my peers who had non-technical professional backgrounds. They are the ones who learned the most and will benefit the most from their multidisciplinary knowledge.
Tips and tricks for success in the program
There comes a stage in Term 2 or 3 where all these individual subjects start to make sense in relation to one another. It’s a sort of “eureka” moment where all the topics start fitting together as an ensemble of tools to obtain a final business objective. This is the best moment to take the time to try to understand the bigger picture. Machine learning and distributed computing are two different fields of studies, but when merged, that’s when they bring the biggest impact to business. Obtaining this general perspective also helps to know yourself better, because it’ll allow you to better understand what kind of role you’d like to fill in the data world.
This leads me to my last point. When you get towards the end of the program and start applying for positions, always research the companies, industries, and job roles well in advance. The IE Talent & Careers staff are very helpful in that they offer students the opportunity to first choose a career path, and then tailor their job hunt accordingly. There are very specific certifications, skills, and interview processes depending on each career path chosen. By selecting one career path as soon as possible, it allows you to better prepare and ultimately increase your chances of finding a job you love.
I’m very proud of myself for everything I’ve achieved in this tough and fast-paced program. I’ve learned and applied concepts in machine learning and computer vision that seemed so academic and well above my means to understand just 10 months ago. It’s very empowering to come out of the program and look back at the innumerable coding notebooks, visualization dashboards, and presentations that we delivered. The road ahead to data mastery is still long, but starting my data career with the Master in Business Analytics & Big Data has given me the tools and confidence to deliver data projects in business contexts for years to come.
Luca Fiume, born and raised between Northern Italy and Southern France, has a background in web development and project management and is currently studying the Master in Business Analytics & Big Data at HST. When he’s not working on improving his machine-learning models, he’s probably organizing his next surfing trip. Get in touch with him on LinkedIn.