Students of the first Data Science Academy spent three months on a journey through the world of data science, big data and analytics. Although most of them are coming from the world where Data Science is not the hottest thing, they bring home many impressions from the journey. “The knowledge is never a burden, new knowledge is always welcome”, Vip Mobile Jovana Barjaktarevic described the three-month journey.
At the end of the trip, they were welcomed again by the organisers – CEO of Things Solver Darko Marjanovic, Data Science Serbia president Branko Kovac, ICT Hub executive director Kosta Andric. All students were given diplomas and all the lecturers received special congratulations because their joint efforts resulted in spreading the basic knowledge about Data Science.
“In the short period of time, we made the students very interested in the Data Science and they realised how it looks to be a Data Scientist. The biggest victory for me was the fact that they spread that knowledge and talked about it to their colleagues. For us as a company, it is very important to raise awareness and the level of knowledge in this area”, Darko Marjanovic says. “The first step was to gather people that get to work with Data Science and to analyse the problems together. During the past three months, we stayed longer and worked more, enjoying to see the participants and mentors at work”, Kosta Andric says.
Everybody that came to the ICT Hub for the final evening had the chance to enjoy the works of the Data Science Academy participants who presented their conclusions in three cases covered during the past three months. Each group tackled a different company’s problem and the groups were made out of participants from different companies, working at different positions.
Delhaize Serbia Case
The key task for this team was to find the solution for the question how could Delhaize Serbia make the most efficient sales of their products, how long the sales should last and what should be the level of discounts in order to attract the most buyers.
As in every learning process, the team first formulated the key question: “Based on our analysis, we realised that the key optimal combination is the duration of sale and the size of discounts – this should produce a successful sale”. Still, the learning curve has mistakes and troubles in to, so the method of linear regression produced the margin error of 36% and was then substituted with the Random Forrest Regressor, decreasing the mistake to 20 percent.
The team concluded that the best data is produces when only one specific chain of shops is separated from the sample and then combined with specific product categories, while the most efficient sales are those when the data is analysed on the daily basis.
Team Members: Srdjan Nesic, Bojan Baralic, Tamara Stanojevic, Zarko Milojevic.
Societe Generale Serbia Case
The team’s task was to discover how could the bank encourage more clients for using e-banking and m-banking. “Our task was to ask the right questions, get to know the whole subject, analyse data and test the model, making our conclusions at the end”.
They discovered how important it is to keep the data properly formatted and in order, placed in a single database. Using the Random Forrest Classifier method, they discovered that the precision of their conclusion is 73 percent.
The team also concluded that the age of the client is the most important criteria for recognising which client would turn from a passive to an active client of e-banking and m-banking.
Team Members: Marko Cebic, Tamara Rendulic, Dragana Rosic, Aleksandar Kovala.
Vip Mobile Case
The team faced 11,5 million lines of geolocated data on using the mobile network in a Serbian town. “We should have identified the movement of users, their habits, needs, expenses, the use of base stations, presence of foreign users and their habits, and finally – the potential for outsourcing the depersonalised data”.
First, the team learned that it is very hard to work with the big data, so they kept it separated in two databases. They learned also that different unexpected and new problems may emerge during the analysis, such as anomalies in user behaviour or fraud prevention.
They concluded what remains one of the main suggestions in the Academy – some of them will present the findings to their teams in their workplaces. So it seems that although Data Science Academy finished its “formal” first year of life, but what was said there will certainly continue the journey.
Team Members: Luka Turudija, Milica Tomasevic, Jovana Barjaktarevic, Goran Kukobat.