Artificial intelligence is emerging from all aspects of our daily lives, from customer support to news generation. We live in the era of the so-called AI revolution, where tools are making us more productive, freeing time for creativity, and creating new ways to problem solve. Data is considered to be the new fuel, providing valuable insights and enabling industry and culture change.
Atlantbh has been aboard the Data Science train for over a year. Pioneering in B&H, we wanted to share the experience and knowledge that we gained during this journey. With that in mind, we organised multiple events as part of the Data Science Days project. On Friday, June 9th, it was a great pleasure to welcome numerous attendees to our event, Data Science Days: “Bridging theory and practice”.
The first part of the event guided attendees through data science basics, presenting our own project, PlaceLab, and explaining how to incorporate data science into existing projects.
In the first presentation, we discussed how to create a strategy, what the common roadblocks are, how to formulate a DS problem and how to build a team and be agile in DS.
The next presentation was about our own project, PlaceLab, that we started developing as a data quality platform 5 years ago. The system evolved past traditional data analysis methods and we came to the point where, to solve more complex problems, we needed more sophisticated solutions. We had the opportunity to explore and later implement data science algorithms into PlaceLab and to enrich PlaceLab with three new services used for measuring data reliability.
The third lecture was “Science in ABH Data Science”. Science is a huge field, so before going into anything specific, it was necessary to make distinction between data science and terms such as big data, data analytics, machine learning and artificial intelligence. We explained that data science, in Atlantbh, is our whole process, involving all of those terms but relying on main idea behind data science – the power of tools to unlock the power of data. Then, we revealed the data science cycle in Atlantbh and the way that we incorporate the power-of-data idea in the development of our projects. We briefly showed what is hidden behind our projects, our system architectures and the machine learning algorithms that we use. The audience was mostly interested in the algorithms that we use, how we choose them, how we allow machines to learn and understand the shape and meaning of words and how we validate our systems. (Read more about it in our blog: Natural Language Processing – Can machines think/talk?)
Following a brief break, our team explained the technical part of the whole process. Using an example practical problem, our software engineers discussed the role of software and systems engineering in data science. The last lecture was on the basic concepts of machine learning and how to apply data science projects to work.
Data science is inevitable in our futures and we were very pleased to see other companies and people interested in data science and that many of them, according to their questions, have already started their data science journey.
Here is a short video overview of the event, our view on data science and the problems we solved using it.