Our roles in the Data Science & Business Intelligence Academy programme
Every year, the University of Economics, Prague, or more specifically the Faculty of Information Technology, organises the DS&BI Academy educational project. It took place for the third time during the 2019/2020 academic year. Adastra participates in this workshop project together with other organisations in the field which want to take part in educating future professionals in the area of Data Science & Business Intelligence.
It consists of a series of lessons focused on Data Science and Business Intelligence. This project came into existence as a continuation of previous workshops which the faculty organised together with the 4FIS student club. The great benefit of the entire project is its close and comprehensive collaboration with practice, and high interaction with all participants. Great emphasis is placed on practical demonstrations, real case studies and student teamwork.
How does the whole programme work?
Dagmar Bínová and Jakub Augustín, who are our long-term ambassadors in external education, are responsible for Adastra's part of the programme, and they guide participants through it for its entire duration. The programme is usually divided into several sessions, whereby every company is in charge of its specialised blocks.
Adastra covers four blocks with its part which focuses on Data Science. Introduction to Data Science, preparation of data for analysis, creation of analytical models, and to conclude evaluation and interpretation of analytical models.
After successfully completing the entire course, graduates are able to actually implement complete project solutions. They can systematically acquire and process data, visualise key information, and use it for business tasks.
In what way is the teaching specific?
These are not classic lectures; the teaching is theoretical only in part. We show everything using practical examples, and we assign tasks from block to block so that the entire teaching is as intensive as possible. Students deal with tasks in individual teams, just like it will also be in subsequent practice. We place great emphasis on the final project and its defence, during which we evaluate the best team for the current year.
Our task in this programme is therefore to minimise its theoretical part, and on the contrary bring the entire project's practicality to the fore. For the entire part of the course, we provide participants with many consultations and advice sessions, so that they can manage and realise relatively difficult assignments. Every year, together with the University of Economics, we innovate the teaching and resolve tasks using tools which correspond to the technological progress in the field. We want to point out that it is not sufficient to merely take data and use it to create a model. You have to be able to play with the data, understand it, derive interesting information from it, and most importantly verify and implement key findings in the final part.
Our collaboration over the years
The 2017/2018 year was our first
16 students, working together in 4 teams, participated in the first pilot year. This year was based on the game League of Legends. For the teaching, we used actual data from this game. In the cloud environment of the Big Data platform (Spark, Zeppelin), students learned to design and create a prediction model, with the aim of guessing the winner. The practical demonstration explained the importance of data preparation, particularly the role of deriving suitable variables, and their appropriate use in prediction.
Lectures are mostly more theoretical, maximally with a demonstration of how it would look, but here we have the opportunity to work directly with real data and tools.
The first year ended with an informal meeting of lecturers and graduates, where it was possible to discuss impressions of the entire programme; there was also room for questions. We also managed to find a new member for our Big Data team in the academy.
Classic teaching is very theoretical, and it's usually a problem to put the theory into practice and find some real application. What's great about the academy is that you have the chance to examine various tools, and find which one suits you.
2nd year (2018/2019)
In the 2nd year, there was an increase in the number of those interested in the course, so the number of students was slightly higher, with 20 divided into 4 teams. We also changed the analytical environment and the data. This time we used the Jupyter notebook tool on cloud, and we worked with actual data relating to passengers on the Titanic. Students had access to data about the passengers, such as for example gender, cabin, when and where the person in question was born, name, titles, and how much they paid for their boarding pass. Thanks to this information, we managed to predict whether the passenger will survive or drown.
The lecturers are experts and they understand their field, but they're also there voluntarily and do it because they want to. And you can really see it. That they live and breathe it, really love it, and want us to get something from it. And on the other side there are also people who want the same thing, resulting in really good collaboration. I found it very refreshing.
3rd year (2019/2020)
We approached this year similarly to the previous one, with the number of students being the same. We changed the cloud environment for the University of Economics data centre's servers, and the sample case analysis took place using housing data. Once again, teaching took place 1x per week in a 5-hour block, and after that every team worked on its own project. The most difficult part of most of the individual projects was the initial reflection on what the data should help us with, i.e. to pose a question for which we look for an answer using the model. The most skilful team used real and up-to-date data regarding road traffic accidents in the Czech Republic. The five-member team evaluated the circumstances under which fatal road traffic accidents (accidents ending in the death of a person or animal) take place.
I liked the interconnection of theory and practice the most. We weren't just educated about the issue theoretically; we also subsequently tried everything out practically, and moreover we acquired valuable “best practice“ directly from people who work in the area on a daily basis.
Why do we participate in the project?
Data Science is a complex field which combines several skills, so there aren't many specialists in this area. That's why we want to participate in quality education, and thereby acquire educated professionals and potential colleagues as partners.
- We're experts in practice, we understand the field, and we have experience which we're happy to pass on to younger generations.
- We enjoy working with our professionally younger colleagues.
- We want to educate competitive professionals in the field.
- Our goal is to show that the world of Data Science is achievable if you have the desire to learn everything.
“We care about delivering solutions to customers, i.e. about technology helping business. That's why we need people for our team who have a certain overlap from technologies to the customer's requirements, or people who can name everything that they need to secure technically in order for their company to function effectively. This intertwining of two worlds is relatively important for the successful realisation of projects in BI, which is why we need educated colleagues not only on our side, but also on the other – the customer's. It appears that today there are generally few of these people, so we must also do something to ensure that graduates leave schools with the education which is currently necessary. With its graduate profile, the University of Economics, Prague is very close to Adastra, so it was the obvious choice.“ (interview with Dagmar Bínová for Student magazine).
It appears that today there are generally few people who have a certain overlap from technologies to the customer's requirements, so we must also do something to ensure that graduates leave schools with the education which is currently necessary. With its graduate profile, the University of Economics, Prague is very close to Adastra, so it was the obvious choice.
Big Data Science Lead
Big Data Competency Lead
Big Data Expert
Senior Data Scientist
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