ODSC Europe Hybrid Conference | June 15 - 16, 2022
Big Data Analytics
Extract and Analyze data sets that are too large and complex to be dealt with by traditional data-processing
Learn New Skills with Big Data Analytic Tools
Big Data Analytics has seen rapid advances in recent years. With some of the sharpest minds in data science presenting, learn the latest techniques and processes to collect, clean and analyze growing volumes of structured data. You will hear use-cases on making data-driven decisions that can improve business-related outcomes.
This focus area will cover many of the techniques for drawing conclusions and insights from big data. You’ll learn from leading experts in the field and complete the conference with an understanding of how to efficiently and accurately analyze data by demonstrating your knowledge in Text Mining, Data Mining, Predictive Analytics, and Data Storytelling.
What You'll Learn
The field of big data analytics has many topics, tools, and frameworks. The goal of this focus area to accelerate your knowledge of big data and data analytics by offering a series of training sessions, talks, and workshops on the most important tools and topics.
ETL, Pipelines, and Data Wrangling
Big Data, Data Lakes and, Data Warehouses
Data Analytics and Text Analytics
Macine Learning
Real-Time and Streaming Analytics
In-Memory Platforms
Distributed Data Systems
Graph Databases
Apache Spark and Apache Kakfa
Hadoop, Cassandra, MongoDB, and Redis
Presto, SQLFlow, and Drill
Apache Storm and Apache Fink
Apache Druid and Apache Arrow
RocketMQ and ActiveMQ
Airflow, Kubernetes, and Kubeflow
Neo4j and ArangoDB
ODSC EUROPE Hybrid Conference 2022 | June 15 - 16th
Register and save 75%Some of Our Previous Speakers
Diego Galar, PhD
Dr. Diego Galar is a Full Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology where he is coordinating several H2020 projects related to different aspects of cyber-physical systems, Industry 4.0, IoT or Industrial AI and Big Data. He was also involved in the SKF UTC center located in Lulea focused on SMART bearings and also actively involved in national projects with the Swedish industry or funded by Swedish national agencies like Vinnova. He is also a principal researcher in Tecnalia (Spain), heading the Maintenance and Reliability research group within the Division of Industry and Transport. He has authored more than five hundred journal and conference papers, books and technical reports in the field of maintenance, working also as a member of editorial boards, scientific committees and chairing international journals and conferences and actively participating in national and international committees for standardization and R&D in the topics of reliability and maintenance. In the international arena, he has been visiting Professor in the Polytechnic of Braganza (Portugal), University of Valencia and NIU (USA), and the Universidad Pontificia Católica de Chile. Currently, he is visiting professor at the University of Sunderland (UK), the University of Maryland (USA), and Chongqing University in China.
Industrial Artificial Intelligence – From automated Process to Cognitive Analytics (Talk)
Julia Schulte-Cloos, PhD
Julia Schulte-Cloos is a Marie Skłodowska-Curie funded LMU Research Fellow at the Geschwister Scholl Institute of Political Science at LMU Munich. Her main research interests lie in comparative politics, political behavior, research methodology, and reproducibility. Julia Schulte-Cloos has earned her PhD from the European University Institute.
Lukáš Csóka
Lukas Csoka, working as Head Big Data Foundations in Swiss Re, one of the biggest global reinsurers, combines data science, consultative thinking and business partnering as the main skills used daily in his job to push design and develop new AI products. His MS in software engineering from Slovak University of Technology and MBA in global management from City University of Seattle are supporting him during this journey. His projects include innovative solutions for farmers using satellites, the application of predictive methods to manage costs inside Swiss Re or the analysis of thousands of client meetings from text and the provision of recommendations to Swiss Re’s traders and many others.
AI Risk to Companies(Workshop)
Hugo Bowne-Anderson, PhD
Hugo Bowne-Anderson is a data scientist, writer, educator & podcaster. His interests include promoting data & AI literacy/fluency, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. He does many of these at DataCamp, a data science training company educating over 3 million learners worldwide through interactive courses on the use of Python, R, SQL, Git, Bash and Spreadsheets in a data science context. He has spearheaded the development of over 25 courses in DataCamp’s Python curriculum, impacting over 170,000 learners worldwide through my own courses. He hosts and produce the data science podcast DataFramed, in which he uses long-format interviews with working data scientists to delve into what actually happens in the space and what impact it can and does have. He earned PhD in Mathematics from the University of New South Wales, Australia and has conducted biomedical research at the Max Planck Institute in Germany and Yale University, New Haven.
Full-stack Machine Learning for Data Scientists(Tutorial)
John K. Thompson
John is an international technology executive with over 30 years of experience in the business intelligence and advanced analytics fields. Currently, John is responsible for the global Advanced Analytics & Artificial Intelligence team and efforts at CSL.
Prior to CSL, John was an Executive Partner at Gartner, where he was management consultant to market leading companies in the areas of digital transformation, data monetization and advanced analytics. Before Gartner, John was responsible for the advanced analytics business unit of the Dell Software Group.
John is the author of the new book – Analytics Teams: Leveraging analytics and artificial intelligence for business improvement. The book was published in June 2020 and outlines how to hire and manage high performance advanced analytics teams. The book outlines how to engage with executives and senior managers. How to select and undertake analytics projects that change and improve how a business operates.
John is co-author of the bestselling book – Analytics: How to win with Intelligence, which debuted on Amazon as the #1 new book in Analytics in 2017. Analytics is a book that guides non-technical executives through the journey of creating an analytics function, funding initiatives and driving change in business operations through data and applied analytical applications.
Mr. Thompson’s technology expertise includes all aspects of advanced analytics and information management including – descriptive, predictive and prescriptive analytics, artificial intelligence, analytical applications, deep learning, cognitive computing, big data, data warehousing, business intelligence systems, and high performance computing.
One of John’s primary areas of focus and interest has been to create innovative technologies to increase the value derived by organizations around the world.
John has built start-up organizations from the ground up and he has reengineered business units of Fortune 500 firms to reach their potential. He has directly managed and run – sales, marketing, consulting, support and product development organizations.
He is a technology leader with expertise and experience spanning all operational areas with a focus on strategy, product innovation, growth and efficient execution.
Thompson holds a Bachelor of Science degree in Computer Science from Ferris State University and a MBA in Marketing from DePaul University.
Dr. Alicia Frame
Alicia Frame is the lead product manager for data science at Neo4j. She’s spent the last year translating input from customers, early adopters, and the community into the first truly enterprise product for doing data science with graphs: Neo4j’s Graph Data Science Library. She has a Ph.D. in computational biology from UNC Chapel Hill, and her background is in data science applications in healthcare and life sciences.
She’s worked in academia, government, and the private sector to leverage graph techniques for drug discovery, molecular optimization, and risk assessments — and is super excited to be making it possible for anyone to use advanced graph techniques with Neo4j.
Graph Data Science: What’s the Big Deal?(Talk)
Marta Markiewicz
Currently Senior (Big) Data Scientist at InPost and Lecturer at Wroclaw University of Economics and Business, previously Head of Data Science at Objectivity, with background in Mathematical Statistics. For almost 10 years, she has been discovering the potential of data in various business domains, from medical data, through retail, HR, finance, aviation, real estate, logistics, … She deeply believes in the power of data in every area of life. Articles’ writer, conference speaker and privately – passionate dancer and hand-made jewellery creator.
The Colours of Cleaning(Talk)
Max Novelli
Max Novelli is a Software Engineer, Data Architect, and Data Manager with a “Laurea” degree from Politecnico di Milano, Italy. He currently works as “Head of Informatics and Data” at Rehab Neural Engineering Lab at the University of Pittsburgh.
His latest interests are in Big Data and its management, structure, visualization, and curation applied to research data — although he never lets go any opportunity to play with hardware and customized experimental equipment. In his current position, he is responsible for the entire lab’s IT infrastructure and the safety, integrity, validation, and curation of experimental data. He is also leading R&D projects spanning from data visualization to data analysis and translating them into viable production tools. His focus is in developing visual tools to explore data structure and to assess the integrity of complex experimental data as well as using neural networks to further study and prove specific experimental results. He has been heavily involved in publishing open-access large datasets into public domain under the Open Science initiative of National Institutes of Health.
When Max is not lost in “computer land,” he enjoys spending time with his family and friends, mountain–biking, hiking trails, swimming, walking (better on the beach), cross-country skiing, eating good food, sipping good wines, and drinking good espresso. He also invests a considerable amount of energy practicing, teaching, and experimenting with yoga and body movement. Lately, he has discovered rock climbing and is trying to perfect his climbing skills.
What Do I See in This Data? Visual Tools to Enhance Data Understanding(Talk)
Bogumił Kamiński
Bogumił Kamiński is Head of Decision Analysis and Support Unit at Warsaw School of Economics, Poland, and Adjunct Professor at Data Science Laboratory, Ryerson University, Canada. His research interests are techniques of large scale mathematical modelling of complex systems combining simulation, optimization, and machine learning. A particular area of his expertise are agent based simulation and modeling and analysis of complex networks.
Bogumił is one of the core developers of DataFrames.jl package for data wrangling in the Julia language. He is also a top answerer for the [julia] tag on StackOverflow and regularly discusses a wide range of data science related topics on his blog https://bkamins.github.io/.
Dataframes.jl: a Perfect Sidekick for Your Next Data Science Project(Workshop)
Kayne Putman
Kayne is the Director of Data Science in the UK&I. He has 10 years of experience in helping organisations get value from data science, working both client-side and vendor-side. In his spare time, he is an avid sports player, and is always seeking his next travel adventure…which is currently particularly challenging!
Why No Model is a Black Box(Track Keynote)
Vyas Adhikari
Vyas Adhikari is a Customer Facing Data Scientist at DataRobot, working with customers to solve data science problems across numerous industries.
Why No Model is a Black Box(Track Keynote)
Why Attend
Broaden your knowledge in key areas of data analytics, big data, streaming analytics, and data pipelines.
With numerous introductory level workshops, get hands-on experience to quickly build up your skills
Post-conference, get access to recorded talks online and learn from over 100+ high-quality recording sessions that let you review content at your own pace
Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science
Learn directly from world-class instructors who are the authors of and contributors to many of the tools and frameworks used in big data analytics today
Meet hiring companies ranging from hot startups to Fortune 500s looking to hire professionals with data science skills at all levels
Network at our numerous lunches and events and meet data scientists, enthusiasts, and business professionals
Get access to other focus area content, including ML/DL, Data Visualization, Quant finance, and Open Data Science
More Reasons To Attend?
Download the why attend guideWho Should Attend
The Data Science Big Data Analytics track is ideal for anyone looking to learn the languages, tools, and topics of big data and data analytics
Database experts and data architects
Data Analysts and Business Analysts
Software engineers and software architects looking to utilize big data analytics
Data wranglers and database specialists looking to leverage their existing data assets with data science tools and models
Business professionals interested in big data analytics and looking to gain a deeper understanding
Experienced data scientists eager to enhance their data science skills
Technologists looking to use the latest data tools such as Apache Spark, Fink, Kafka, Drill, and others
Students and academics ready for more practical applied training in big data analytics
Industry experts seeking to understand the impact of big data analytics on their industry