Data Science Economy: Data Driven
2 keynotes / 16 talks / 36+ speakers
18.05.2018 Workshops: Three great workshops for (almost) free!

18.05. 2018, @HUB385, Zagreb, Croatia
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Interest in a workshops and (un)conference is so great, that we decided to open a second day, with few workshops! Here, we will present specific tools and products to work with Data Science! Some of them are paid (additionally), some not, please look at the details!







Leonardo de Marchi

Deep Dive into Deep Learning

In this training, we will see some Deep Learning concepts with Keras. The goal is to provide all the tools and knowledge to make the audience able to start their own Deep Learning projects. We will examine a real image recognition project and use it to show the process to develop the model from start to end.

We will start examining the business needs and designing the solution. We will explain how to create a multi layer network and then we will go through more sophisticated topics such as implementing different types of networks (for example Convolutional Neural Network) for Image Recognition, using dropouts and random noise to improve results, select the proper architecture and use pre-trained models. We will also address some more advanced topics necessary to go behind internet tutorials.

Note: 20 seats limited and paid full day workshop (500kn per attendee)

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Implementation Overview: Predictive Modelling

Implementation overview: Forecasting natural gas consumption using predictive modelling

Data Science has over the years become integral part of every organization. Asking right questions and seeking answers in datasets is a virtue that can be mastered. Workshop will be based on previously presented case study on Forecasting natural gas consumption using predictive modelling demonstrating key methodological and technological aspects.
Workshop is a demonstration of one of many possible approaches to gather, analyze, process and visualize data.

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Alexander Pivk, SAS

SAS Viya: Built for innovative results from analytics

SAS Viya extends the SAS Platform to enable everyone: data scientists, business analysts, developers and executives alike – to collaborate and realize innovative results faster. SAS Viya is a cloud-enabled, in-memory analytics engine that delivers everything you need for quick, accurate and consistent results – every time. Elastic, scalable and fault-tolerant processing addresses the complex analytical challenges of today and effortlessly scales to meet your future needs. Learn what SAS Viya can do for your business and your research.

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Analysis of the reader behaviour using topic modelling & Hierarchical Fine-Grained Categorization Based on Image and Text

In Styria Data Science department ( established in 2015, we work on delivering state-of-the-art machine (deep) learning R&D to Styria’s news and classifieds brands. Utilizing the latest technology and strong data portfolio, artificial intelligence is incorporated into modern media services and products.
The first topic in the presentation will showcase Analysis of the reader behavior using topic modeling implemented on news media industry. The other topic will present hierarchical Fine-Grained Categorization Based on Image and Text implemented on classifieds.

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Cost, latency, and interoperability are the three dimension of IoT or IIoT

Well will examine the story of missing data, the value of IoT, and a number of standards that are open and proprietary, and how global benefit is achieved as IoT becomes more prevalent, through open data, supporting autonomous ‘things’ communicating with other ‘things.’ While open standards will be a point of view I believe critical, what are our options and risk as we build enterprise solutions. API integration, dataStores in containers with microservices, smart gateways, industry/proprietary standards, and even confidence intervals maybe the tools of choice! Break out your dusty copies of Dr Deming’s guide to statistical process control and the PLC workbench guide, because we are going to discuss factories, and the merging of OT and IT.
Lastly, what would an agenda for a data science education look like? Should data scientists or engineers be licensed, certified? What might the curriculum look like, or entail. We will examine what could be topics for aspiring or practicing data workers today.

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Data Warehouse & Big Data: What has Changed?

In the past few yearS, one of the biggest questions in the world of analytics is whether the rise of Big Data implies the dawn of Data Warehouses. Even with the development of Hadoop 2.0, the usage of Data Warehouses didn’t drop – on the opposite, having Big Data technologies by their side gave a possibility of augmenting the scope of analysed data. New concepts of hybrid warehouses built on the top of Data Lake architectures have risen and are currently implemented by major vendors. Whether the analytics system is built from scratch or is evolving from the Data Warehouse, there are several approaches to the design and implementation, each with their benefits. Various tools that enable the interoperabililty of structured and unstructured data on all levels (integration and analytical) have been developed. The workshop will be based on the use cases for different challenged in the analytical system using the tools like Apache Impala.

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