The document outlines the establishment of a Data Science Club aimed at fostering collaboration between academia and business, focusing on data, analytics, software engineering, and AI. It details the club's structure, goals, and planned activities, including expert talks and workshops on various data science topics throughout the academic year. Additionally, it highlights the roles of key team members and provides contact information for interested participants.

1. INTRO INTO
DATA SCIENCE CLUB
16th OCTOBER
2017

2. TABLE OF CONTENT
INTRO
WHY DATA SCIENCE CLUB AND WHAT ARE THE GOALS?
INTRO INTO DATA SCIENCE

7. AI research isn't just in Silicon Valley
- Alan Turing, born in London
- DeepMind, founded in London
- Amazon AI labs, Berlin
- Yandex, Russia
- Baido, China
- ...

8. Why Data Science Club?
- Network business and academia for mutual good
- Business
- Dedicated to adding a value
- Needs an access to research, talent and innovation
- Academia
- Dedicated to research and teaching
- Can't do all the research and teaching alone

9. Exponea is a company with ...
- Team of Software Engineers that work on applied AI
- 80% from FIIT and Matfyz
- Data and interesting clients from all around the world
- Lots of interesting problems to solve
- Lots of relevant knowledge to share

12. STU FIIT is an education institution with ...
- A study programme Intelligent Software Systems
- Software Engineering
- Artificial Intelligence
- A relevant research and team of experienced researchers
- Lots of graduates with careers related to data science
- Great facilities where all of us can meet

13. What is Data Science Club?
- Community of like minded people with interest in
data, analytics, software engineering and AI
- Regular meetups where we share knowledge and
work on our challenges together

14. When?
- 6 times per semester in both summer and winter
- Monday starting at 16:00 with agenda for 3 hours:
- Expert talk(s)
- Practical workshop
- Networking, consulting, discussions

15. Draft of Agenda
Winter
1. Intro into Data Science
2. Data storage
3. SQL for data analytics
4. Map-Reduce
5. Stream data processing
6. Data visualisation
Summer
1. Applications of machine learning
2. Process of machine learning
3. Classification
4. Recommendation engines
5. Bandit algorithms
6. Reinforcement learning
Final agenda depends on your interest and availability of expert speakers

16. Goals
- Build an active community
- 3+ universities, 10+ companies, 100+ members
- Create and share content
- Reach 2000+ viewers
- Contribute to research and applications
- 5+ publications by B&A authors in 2017/18
- 5+ new features in software products

17. People
- Jozo Kovac, Cofounder, CTO
- Matus Cimerman, Head of AI
- Peter Kovacs, AISW Engineer
- Ondrej Brichta, AISW Engineer
- Jakub Macina, AISW Engineer
- Robert Lacok, AISW Engineer
- Dalibor Meszaros, AISW Engineer
- Lucia Siebestichova, Event Manager
- Martina Kolibasova, QA Engineer

18. Contacts
- FB Page: https://www.facebook.com/ExponeaSocietyBratislava/
- Slack: https://community.exponea.com/
- Wiki: https://github.com/exponea/data-science-club/wiki
- Git: https://github.com/exponea/data-science-club
- Email: matus.cimerman@exponea.com

19. MEET
OUR
TEAM
WRITE HERE SOMETHING
MY SEARCH FOR
A FAMILY VACATION
LETS START WITH A REAL WORLD PROBLEM

22. Challenges
- We pay for traffic, how to increase customer value?
- Can customer history improve the quality of search?
- Search must be fast, render everything under 100ms

23. A quick analysis
- A learn to rank problem

25. https://www.slideshare.net/MrChrisJohnson/interactive-
recommender-systems-with-netflix-and-spotify

28. Designing Data-Intensive Applications

32. WORKSHOP
16th OCTOBER
2017

33. Challenges - step by step
• Collect data
• Preprocess
• Train a model
• Evaluate
• Deploy
• Improve the model
Data Model
Data* InsightModel

34. Collect data
• If you have it, you’re done
• If not, find a way to collect

35. Preprocess
• What:
• Select features
• Normalize
• Fill
• Aggregate...
• How:
• Store
• Scale out processing

36. Create a model
• Learn about your domain
• Go really simple first
• Don’t reinvent the wheel

37. Evaluate
• What metrics make sense?
• Test sets vs live data

38. Deploy
• Online API vs batch
• Latency matters
• Reliable
• Monitor & evaluate
• Retrain on new data

39. Improve the model
• Read more papers and blogs. Is anyone doing it better?
• Lead, innovate and be the best

40. Workshop
1. Set up your environment: dependencies, template & data
2. Preprocess: Clean, normalize, feature select
(pandas/scikit-learn)
3. Train: Choose a model of your liking (scikit-learn)
4. Deploy: Create a REST API (flask)