Since the very beginning, our mission has always been to democratize investment research and make it effective, powerful, and accessible to everyone.
We want to live in a world where literally anyone can get access to financial data and be able to draw insights from it using state-of-the-art analysis tools and data science techniques.
To achieve such an ambitious goal, there are three main pillars that need to be fulfilled.
Analysis tools & Data science techniques
We already covered (1) and (2) in our latest Terminal 2.0 & SDK launch, which provides access to data from 92 data providers, and a set of analytics tools & AI/ML techniques covering 8 different asset classes. If you did not get the opportunity to attend our Launch event with over 1300 registrations from users and partners, you can watch the recording here.
For the third pillar, financial knowledge, we are a strong advocate of financial literacy and educating the community on how to make data-informed investment decisions. That said, we always wanted to do more than that. That's why when the opportunity to partner with Lisbon Data Science Academy arose, we jumped at the chance. We want to come on board and help train the future generation of data scientists, by offering practical, real-life financial challenges. On top of that, we felt strongly resonated with the strategic goals and values of LDSA, which has continuously provided high-quality, open-source, and affordable Data Science training to thousands of students over the past 6 years of existence. After more than almost half a year of preparation, our partnership with Lisbon Data Science Academy came to fruition, led by Minh Hoang, and a team of experienced product & data professionals including, James Maslek, Jeroen Bouma, and Martin Bufi.
Over the course of one month, we offered the Data Wrangling specialization, which contains 4 weeks of theory and 1 final Hackathon for students to consolidate and apply what they have learned. This specialization helps aspiring data scientists extract data from a variety of sources, deal with common data challenges and ultimately unlock its value.
Our final Hackathon was a great success, with over 50 participants from a variety of backgrounds coming together to tackle the data-wrangling challenges. Starting at 8h30 and ending at 19h, the hackathon was filled with excitement, challenges, and shall I say, a little bit of friendly competition. We brought a real-life financial challenge to the participants, which goal is to classify whether you should buy Bitcoin now or not. The challenge itself is already hard enough, but we decided to make it one level more difficult, by splitting data in a variety of places, from a set of files with distinct formats and data structures, an API, SQL databases, and via web scraping.
Teams worked tirelessly for 8 hours to collect financial data accessible through OpenBB from a variety of sources, deal with missing data and common data challenges, and finally train a classification model to predict whether to buy Bitcoin at that time. At the end of the hackathon, teams presented their projects to a panel of judges, who evaluated the projects based on scores on the Leadership Board, and presentations. And in the end, there could be only one winner - though all participants left the hackathon with new skills, connections, and a newfound appreciation for the art of data wrangling.
Overall, the hackathon was a great opportunity for students to learn and master data-wrangling skills and techniques, and to work with other fellow students to solve real-world data challenges. The partnership between OpenBB and the Lisbon Data Science Academy was instrumental in making this Specialization, and Hackathon a success. We look forward to future collaborations with Lisbon Data Science Academy, and continue our ongoing effort to promote financial literacy and the use of open-source tools and techniques in finance.