Od 4. do 8. srpnja, u suradnji sa Tehničkim Veleučilištem Zagreb, Poslovna inteligencija održala je Big Data Summer School. Ljetna škola bazira se na idealnoj kombinaciji teorije i prakse, a sudionici su se mogli upoznati sa konceptom i najboljim praksama koje je moguće kombinirati sa stvarnim studijama slučaja uz prijenos znanja. Big Data Summer School namijenjena je studentima ali i svima koji žele naučiti i testirati Big Data koncept i tehnologiju, te steći dublje praktično znanje na tom području.
Nastavu su vodili stručnjaci iz područja Big Data među kojima su direktor Inovacija i Razvoja Poslovne inteligencije Marko Štajcer, viša konzultantica BI odjela Lada Banić, Hrvoje Gabelica, stručnjak za vizualizacije i Tableau, Sergej Lugović s TVZ-a, te Iva Sorić, konzultantica Poslovne inteligencije.
Big Data Summer School bazira se na tri glavna koncepta:
Naše edukacije i prijenose znanja vode stručnjaci i znanstvenici te korisnici s područja Big Data koncepata i Big Data Analitike. Svi naši terninzi zasnivaju se na opširnom praktičnom znanju i stručnosti u različitim poslovnim segmentima i industrijama, s mnoštvo praktičnih primjera.
Pročitajte cijeli program:
Day 1: Business Aspect
Results: Requirements definitions
Detailed overview: Introduction, Way of Thinking in the age of Big Data, A Taxonomy of Dirty Data, Fighting entropy by ingesting information into the system – from the early days of Cybernetics till today, Limitations of current concepts in Big Data Information System Design, Business and Informaton needs as a driver of Big Data projects and source of the variety, Case Study: «Mission: (Almost) Impossible», Process of Data Curation of the political eco system in the EU (structuring in one place financial reports and social media content of all EU political parties) by Hrvoje Appelt, A Framework for Evaluating Information Needs
Learning approach: Concept overview, Case study, Practical exercise
Way of thinking: Sociotechnical systems way of thinking, Think like a Data Scientist
Day 2: Big data concept / design
Results: Data collections, Database design, management and monitor
Detailed overviw: Intro to Big Data, Hadoop overview and Ecosystem, Comparison of Hadoop distributions (Cloudera, HortonWorks, MapR), Storing & analyzing data in Big Data environment (MapReduce, HDFS, HBase, Hive), Important building blocks for Big Data platform (Flume, Kafka, Pig, Hive, Hbase, Impala, Solr), Management and monitoring tools
Learning approach: Concept overview, Discussions, Best Practice, Case studies
Practical exercises (HDFS, Hive, Sqoop, Hue, Cloudera Manager)
Way of thinking: Think like a Big Data Architect
Day 3: Big Data Architecture Ingestion and Processing Big Data
Results: Apache Hadoop architecture overview, configuration, preparation data for analytics
Detailed overview: Apache Hadoop, YARN and HDFS in more detail, Modeling structured data as tables in Impala and Hive, Storing & analyzing data in Big Data environment (NoSQL), Proper cluster configuration and deployment, Best practices for maintaining Apache Hadoop in Production, Deep dive into different Hadoop components: Sqoop, Flume, HDFS, HBase, Pig, Hive, Impala, Solr, Use Sqoop and Flume to ingest data
Learning approach: Hadoop architecture overview, Best practices
Practical exercises (Hbase, Flume, Hive, Hue, Solr, Cloudera Search)
Way of thinking: Think like a Big Data Architect
Day 4: Big Data Analytics
Results: Introduction to big data analytics
Detailed overview: Relational Data Analysis within Big Data platform, Defining Big Data analytics, Difference between batch processing and real-time data processing, Analytics with Spark, Handling streaming data, Delivering business benefit from Big Data, Defining Big Data Strategy
Learning approach: Concept Overview, Best Practice, Case studies, Practical exercises (Oozie, Spark, Solr, Cloudera Search)
Way of thinking: Think like a Data Scientist
Day 5: Machine Learning and Visualization
Results: Using machine learning and visualization to improve Big Data analytics
Detailed overview: Machine Learning – Introduction, Knowledge discovery process, Machine learning tools (Mahout, H2O, MLlib) and application, Machine learning on Hadoop examples, Visualization – Basic Concepts, Visualization practical examples
Learning approach: Concept Overview,Best Practices, Practical Exercises – Spark MLlib (Python, Scala), SparkR (R), Tableau, Case studies
Way of thinking: Think like a Data Scientist
School Fees
Fee for Big Data Summer School (5 day) is 4.999 HRK (+VAT). Student of TVZ will receive a 50% discount on this Big Data Summer School fee. Payment should be made in advanced before the start of the course, to an account specified by the organizers.
Course Logistics
9:00 – 16:30. This includes a one-hour break for lunch and two 15 minute coffee breaks.
Location: The Big Data Summer School will be held in Zagreb, at TVZ, The Lifelong Learning Centre.
Requirements: Our Lifelong Learning Center has computer equipment that is necessary to take this school and follow agenda so participants are not required to bring their own laptops.
About the Instructors
Marko Štajcer is Director of Innovation & Development department in Poslovna Inteligencija. Graduated on University of Zagreb on Faculty of Organization and Informatics, Information Systems, 2008. After graduation Mr. Štajcer started working at Poslovna inteligencija where he gained huge experience in the field of data warehousing systems, data integration, business intelligence and Big Data analytics on numerous national and international projects. Today, Mr. Štajcer is director of Innovation and development department in the same company, where he is responsible for research and the introduction of new technologies and the development of Big Data applications. He also participates in complex projects of data integration and advanced analytics in the role of system architect, lead consultant and project manager.
Ms. Lada Banić is senior consultant in BI department and works on innovative projects that consolidate data mining, text mining and big data technologies. She has graduated from the Faculty of Science and Mathematics, University of Zagreb. She has also finished international program: Diploma Study in Management (for engineers) also on Faculty of Electrical Engineering and Computing, University of Zagreb. She is working as a consultant in PI (since 2008). Before that she worked in different companies and on different projects some of which are: Hendal market research company (Data Processing Manager, different market research projects, Internal audit), telecommunication company VIPnet d.o.o. (development of revenue assurance application, business processes development and reengineering, SOX project, reports and data analysis), Privredna banka (DWH project, design and development of application for personal bankers), Optima–osn, design and development of applications for retail and back office bank departments). She has specific knowledge in specific IT platforms, tools, and fields such as Data mining, Statistics, Modelling, Business process management, ARIS developer, Oracle, PL/SQL, Reporting services etc.
Iva Sorić graduated in 2015 at the University of Zagreb, Faculty of Science – Department of Mathematics. After graduation she joined the Innovation and Development department at Poslovna inteligencija. Since then she has been working on projects concerning Big Data analytics and actively participates in exploring new Big Data solutions.
Hrvoje Gabelica is business intelligence consultant certified for data analytics and visualization in Tableau Software and Tableau User Group Zagreb leader. He has worked on various domestic and foreign projects on various business intelligence platforms. Also he is author of 50+ articles about business intelligence, data mining and Big Data and guest speaker on conferences and faculties.
MSc Sergej Lugović, MBA is a senior lecturer teaching Information Economy, Technology Entrepreneurship and e-business at the Polytechnic of Zagreb, Croatia. He’s also a PhD candidate at the Information Science department of the Faculty of Humanities and Social Sciences, University of Zagreb. His research interests are information behavior and needs in intelligent socio-technical systems. He holds a Master of Science degree from Plekhanov Russian University of Economics and an MBA from The London College UCK. Along with his academic career, he had a business career in Moscow, London, and Zagreb, working for blue chip companies, for the government of the Republic of Croatia, in technology ventures, and in the fashion and the music industries. He holds and continuous education degrees from MIT (Big Data) and University of Amsterdam (Digital Methods).
Hrvoje Appelt was awarded by Marija Juric Zagorka reword which hands out HND for the Investigative journalism. He is being working for leading Croatian media (EPH, HRT) for more than 10 years before going freelance work as journalist and pursuing further his passion for the hockey and ice skating. He was competing in the prestigious global Red Bull Crashed Ice championship in extreme skating. Currently he is building skate rings such as one at Kings Tomislav Square in Zagreb among others and spend time doing data journalism and finalizing his new startup Political Data that act as a main source supporting EU political ecosystem related data journalism.
