I have always been interested in computers, but I ended up studying petroleum engineering. A main reason for this is that the oil industry was one of few options in my country Libya with international exposure. My dream has always been to have a fast-growing career, work with different people and travel to different countries.
The oil industry only made some of my dreams possible. I enjoyed my career as a petroleum engineer with data analytics skills, but I wanted to utilize my creativity more. I am passionate about using advanced programing skills on real-life business problems. Therefore, I decided to follow my passion and study the Master in Applied Data Science at Frankfurt School. Only 3 months after starting my studies, I landed my first job as a Data Science Working Student. After 6 months, I was promoted to a Junior Data Scientist and my journey continues!
Understanding Data and Machine Learning
Since starting my master’s my perspective about data has changed a lot. Before, I knew that data drives many hidden insights and I can extract some of these insights “manually”. Now, I can not only get insights from data, but also extract these insights and use them to predict future cases. Machine Learning is a great way to do that “automatically”.
You may ask how Machine Learning can be combined with business. To understand that, it is necessary to have some business knowledge and our programme covers this pretty well. When we use machine learning to get insights about the future, we can then make decisions to bring additional value to the business. This is something you really need to do in practice and the Hackathons at Frankfurt School are a good way to practice this.
Machine Learning in Business
It is true that you can extract insights from data based on intuition and without machine learning. Actually, this is what people have been doing for many years. However, we are now living in challenging times where efficient decisions often have to be made in milliseconds based on large data. This is why we need machine learning. Consider the following examples:
- Online Retailing: when you buy something from an online retailer, a decision is made to recommend something else to you. Is someone making this decision for you? No, absolutely not. A machine learning model is doing the heavy work and making efficient recommendations to you and many other customers around the globe within milliseconds. These automatic decisions improve sales and make online retailers data-driven.
- Online Payments: you may have noticed that when you buy something online, you see a waiting window that says: checking your payment. This is a Machine Learning model, which takes only a few seconds, is making sure that your payment is not suspicious.
Data Science is about making efficient decisions based on data that bring additional value to the business. This also explains why artificial intelligence has very big impact on business today. Companies now have to compete against each other making efficient decisions quickly to stay relevant.
Advice for future students
Here is my advice to prospective students who are interested in Data Science. If you want to be impactful in your future career, you have to look for something that improves your technical skills in a business-oriented manner, such as in the Master in Applied Data Science which is focused on business.
I hope this blog makes you curious about data science. Maybe you could be sharing your interesting and impactful thoughts about data on the blog next!