"Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it...” – Dan Ariely. This sentence was true about 2-3 years ago, now, everyone is doing big data, whether they know it or not.
In this education session, we are going to demystify the main concepts around using data science by both giving a descriptive overview of machine learning and big data capabilities and critical points as well as doing some hands-on thought-exercises.
On the first half of the day we are going to dig into the concepts and technologies, like distributed computing, machine learning, neural networks etc. We’ll see, how to run a data science project and what are the use cases and risks related to it.
On the second half of the day we are putting more focus on the practical execution side of effective product development practices, starting from a question about where data science can be used in products, how you validate them as viable at low-burning budget and how you turn the ones that pass the validation, gradually into actual products.
Transforming a business to be as data centric as possible is a great way to increase efficiency and find new revenue models. As a separate focus area are the challenges where machine learning methods unlock analysis and decision-making on top of big data. Applications include real-time location-based prediction systems for telecoms, fraud and credibility prediction tools for financial services, chat bots and lots of other stuff. More information: https://mooncascade.com/services/data-science
Minimum Viable Product (MVP)
MVP is a way of thinking. The goal of an MVP is to get real market feedback as early as possible, provide cost and time efficient ways to correct course, eliminate critical errors early, focus on core features (no bells or whistles), and deploy early. MVP is minimal, as it focuses on key aspects of your business model, and proves or disproves the key hypotheses about your market success. At the same time MVP seeks viability by validating market and the user experience as early as possible.
Asko has been working in software industry for 20+ years. He was the one to build up Skype Mobile Team from 0 to 40 people in 2 years and also helped to put a start to an international hackathon Garage48. These days Asko leads the region's fastest growing software and product development company, Mooncascade, of which he is Co-Founder and CEO.
Taavi started off his professional career as a radio engineer in one of the largest Telecommunications companies in the world - Ericsson. He studied Telecommunications engineering and signals processing at the Tallinn Technical University and worked for several years in a field that provides data communications to the masses. While working at Tele2 he was a part of a team that demonstrated what actual business value machine learning can bring and he has been working with data science ever since. He is currently building a team of mad scientists at Mooncascade to develop the next generation of predictive analytics and AI assisted cognitive services.
Mooncascade is the leading Estonian software and product development company founded by four software engineers, including two former Skype engineers. The team of about 100 people offers data science and data analytics based custom development services, and full-stack software product (mainly mobile and web application systems) development services, starting from idea and product discovery and going through design, full-stack (from front-end to back-end) engineering, quality assurance and production deployment. For more information: https://mooncascade.com/services/data-science