Impact Assessment of Big Data analysis | CHEDTEB | Erasmus+ Strategic Partnership
Impact Assessment of Big Data Analysis
Impact Assessment of Big Data Analysis

What is Big Data? How we can analyse it ? How and in which areas can we use it ? Are there any limitations (ethical, technological, legal, expert …)? What could be the impact of (Big) Data Analysis on business?

Our academic experts from Bielefeld University of Applied Sciences, Brno University of Technology and Tartu University in cooperation with business sphere will try to answer some above-mentioned key questions about Big Data and Data Analysis.

Almost every minute each of us produces new data when using mobile phones, laptops, cars or being recorded by public cameras, streaming music, using social media sites or booking tickets. Nowadays, almost every device collects user data via built-in sensors and cameras and exchanges the data in real-time with other devices via automatic interfaces and/or transmits it into a data network. The pure volume of data generated every minute and its growth rates are gigantic. Furthermore, the pool of data is of high quality for data analytics as it contains a huge range of unstructured data from various sources.

The task of Big Data analytics is now to identify hidden patterns or correlations in the mass of raw data in order to transform the data into information and then into contextualized knowledge to solve a particular problem.

Welcome! We would like to offer you some answers to your possible questions about Big Data Analytics. Enjoy!

Big Data Introduction City Smart Impact Assessment of Big Data Analysis

Have you ever thought about consequences, limitations, boundaries and future?

Continue to Ethics & Law of Big Data

Are there some regulations for using Big Data?

Can I use this type of data for my vizualization?

What about some EU Directive?

The ethics of algorithms and AI?

GDPR?

If you ever had a similar questions we have prepared a perfect answer for you!

  • Reasons for regulation

  • Ethics of Algorithms and AI

  • European Union Legislation

  • Risks of Big Data

  • General Data Protection Regulation


…and much more in our detailed document

Would you like to know more?

What are the technologies that could be used? Are there any limitations, advantages and necessary expertise?

Continue to Big Data technologies

What are the technologies that could be used for Big Data processing and analysis?

Over the last decade, the MapReduce framework, and its open source realization, Hadoop, has emerged as a highly successful framework that has created such a lot of momentum that it has become the de facto standard of big data processing platforms. However, in recent years, academia and industry have started to recognize the limitations of the Hadoop framework in several application domains and big data processing scenarios such as large-scale processing of structured data, graph data and streaming data. Thus, we have witnessed an unprecedented interest to tackle these challenges with new solutions, which constituted a new wave of mostly domain-specific, optimized big data processing engines. In this documentation, we refer to this new wave of systems as "Big Data 2.0 processing systems". We provide a taxonomy and analysis of the state-of-the-art in this domain. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research.

We have prepared for you a presentation series about Big Data technologies and systems as a framework for the working and analysis of Big Data. This series is based on presentations of prof. Sherif Sakr that took place as a part of Brno Data Week 2018 event.

Join our series

Presentation 1: Introduction to Big Data and Big Data 1.0 

Today we will start with a brief introduction to Big Data. We will talk about how Big Data is generated, where we can apply it and also about the first world-wide famous platform of BigData 1.0 System, which is Hadoop. 

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Presentation 2: Available platforms for Big Data 2.0

Spark, Flink, Presto and many others. This is just a sample of frameworks which are used in real companies and we will talk about some of them.

In the previous episode of this Big Data series, we talked about the basic information concerning Big Data. This presentation, however, will be much more technical as we will be covering the most popular platforms you can use to deal with Big Data 2.0 Systems and learn about the key differences between these platforms. Let’s go!

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Presentation 3: Big Stream Processing Systems, Big Graphs

After the two previous episodes you know the basics about Big Data. Yet, it might get a bit more complicated than that. Usually when you have to deal with data which is generated in real-time. In this case, you are dealing with Big Stream.

This episode of our series will be focussed on processing systems capable of dealing with Big Streams. But analysing data lacking graphical representation will not be very convenient for us. And this is where we have to use a platform capable of visualising Big Graphs. All these topics will be covered in today’s presentation.

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Presentation 4: Big Machine Learning Libraries, Open Challenges

The time has come when we reached our last part of the series. We have already discussed some history concerning Big Data and also platforms available for Big Data. But doing all this stuff manually will take so much time that we are going to try to automatize it by machine learning. This is one part of our last episode. The second is focussed on a view to the future. Because we still have a lot of procedures which do not provide the perfect solution and we have to try to find one.

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Do you want to know more? Are you looking for additional resources?

Join the presentation of prof. Sherif Sakr that took place as a part of Brno Data Week 2018 event.

VIEW THE PRESENTATION

If you want more information about Brno Data Week 2018 look at here:

With all those technologies, how could we analyse Big Data?

Continue to Big Data Analysis

Have you ever thought about how to analyse Big Data? And about the procedure of data analysis? Have you ever heard about Data Science? Have you ever thought about using it in healthcare domain? Are you looking for some practical and complex example?

Healthcare providers have moved (or currently moving) from paper-based information management to electronic health record (EHR) systems. In practice, effective digital health system is one of the main pillars of Saudi 2030 vision. Nowadays, data from large numbers of patients is being collected and stored in an electronic format, and these accumulated data should potentially enhance healthcare services.

In general, the wide adoption of EHR systems has enabled the availability of huge amounts of data in different types and formats (e.g. structured data, free text, image, video, audio, etc). This situation paved the way for data analytics techniques to find their ways into the health information management domain not just for improving patient care outcomes but also to improve the quality of care, reduce costs and improve patient population health.

The aim is to provide an overview of how data analytics techniques can provide methods and processes for extracting and transforming raw medical data into meaningful insights, new discoveries and knowledge that supports efficient and effective healthcare decisions.

Join our recordings from the webinar led by Prof. Sherif Sakr from University of Tartu in Estonia. We divided the recording into the three thematic parts that represent the analysis of the environment, procedure and practical applications.

PART 1: Healthcare domain

PART 2: Data science


PART 3: Case studies

More information about the webinar here

Have you ever thought about the variety of applications?
Where could you meet Big Data Analysis?

Continue to Application in Data Analysis

Do you know about possible applications of Big Data Analysis? Who? Why? How?

We have introduced some of the applications of Big Data Analysis on Healthcare domain. But have you thought about other applications? We would like to provide you an inspirational catalogue of the possible use of Big Data Analysis from the local conditions of the Czech Republic, Germany and Estonia and one more detailed example from the insurance domain.

Impact Assessment of Big Data Analysis and Application Cases
– A Cross-Country Comparative Analysis

Are you interested in the real use of Big Data in companies? Check out our use cases in companies from the Czech Republic, Germany and Estonia!

  • Logistics in a smart port – Hamburg Port (DE)
  • Personalized Tumor treatment – National Center for Tumor Diseases (DE)
  • Smart agriculture – CleverFarm (CZ)
  • FinTech business model – TransferWise (ES)
... and more in the catalogue!

Would you like to know more interesting examples from the local environment and from different sectors?

The Age of Big Data Analytics: How Big Data is Reshaping the Insurance Industry

Driving safely?

Save money on your car insurance!

While driving, data about your behaviour in the car is collected by multiple systems.

If you drive according to traffic regulations, you get a good rating and pay less for insurance!

  • Explanation of the method
  • Used systems and technologies
  • Customer classification
... and more in the catalogue!

Would you like one more complex example of application in insurance industry?

Would you like to go more practical? Or try some analytics on your own?

Continue to Practical approach for data analysis

What more do we need for successful data analysis? What knowledge do we need? What technology could we easily use?

We have prepared Brief Guide for Data Analysis covering the main topics from the area of Data Science, Statistics, Machine Learning and more with a handful of reference sections. And then you can try your own analysis according to our step-by-step tutorial.

Brief Guide for Data Analysis





Want to learn about data science, but don‘t know where to start?

We have a guide for you full of information you should know before your first analyse!

  • Introduction to data science
  • Must know from statistics
  • Statistical and graphical language
  • Machine learning
…and much more in our guide

Would you like to know more interesting examples from current literature and research?

Data Analysis for Financial Markets

Financial Markets

Python

Pandas

Plotly

Matplotlib

Jupyter notebook

Quantopian

Morning star



Would you like to try some analysis? Are you interested in Financial markets?

Do you want to try some technologies like Jupiter Notebook, Pandas or Python programming?

  • Detailed step by step tutorial
  • Overview about all used tools
  • Full code explained
  • Explained graphs and charts
  • Tips for further study
…and much more in our detailed tutorial

Join our tutorial where we will guide you through the whole process of analysis!

Do you want more?

Join our CHEDTEB community!

via social networks or personal contacts

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About

This project shared by the three partner universities Bielefeld University of Applied Sciences/D, Brno University of Technology/CZ and University of Tartu/EST aims to provide the framework for a future joint master's degree on Digital Transformation of Corporate Business.

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