Data Science

Cloud Analysis

  • Class 30
  • Practice 30
  • Independent work 120
Total 180

Course title

Cloud Analysis

Lecture type


Course code






Lecturers and associates

Course objectives

One of the areas that will experience big changes with increasing computing in the cloud is data analysis and quantitative modeling. The increasing volume and speed of data generation on the one hand and the increasing need for cost efficiency, speed of reaction and flexibility on the other, increasingly direct organizations to using analytic clouds. Analytical cloud services are now the dominant form of large data analysis based on distributed technologies such as Apache Hadoop, Apache Spark, Dremel, etc. HPC infrastructure becomes key to gaining competitive advantage and optimizing data monetization. By further developing analytical cloud services, they will become an inevitable solution to the most problems of most organizations, such as: low utilization of internal and external data, poor understanding of client needs, obscurity in response to external changes, large total cost of ownership of information infrastructure, etc. Objective of this course is to introduce students with cloud analytic concepts and general insights into analytical services in the cloud, including also 3 major market players (IBM, Oracle and Microsoft).


The introduction to the cloud computing and benefit andimportance of the cloud. Importance of collecting data, storing and converting data into infromation with quality analyzes in the cloud. Benefits of cloud processing of large data sets. DWH in the cloud and the difference between the traditional DWH compared to the one on cloud. The differnce between cloud modeling and traditional modeling. "Proof of Concept" for DWH implementation in the cloud and ROI on Investment. Preparation and transfer of DWH data in the cloud. Difference between ETL and ELT process. Client tools for cloud analytics. Search and allocate virtual resources (Amazon AWS, IaaS), contextualization, snapshot, volume, network creation, security. Face recognition, age, gender and person emotion estimation in front of camera, using cloud services. IBM Watson and the use of artificial intelligence in the cloud. Microsoft and Oracle Cloud Services and Autonomous Databases. Container basics. Trends and Future.

Required reading

Yael Onn et. al., Privacy in the Digital Environment (Haifa Center of Law and Technology, Niva Elkin-Koren, Michael Birnhack, eds., 2005).

Georg Hager, Gerhard Wellein "Introduction to High Performance Computing for Scientists and Engineers",, dostupan pdf: 

Additional reading

Anil Maheshwari, Big Data Essentials (Amazon Digital Services LLC, 2016)
Nathan Marz, Big Data analytics (Manning Publications, 2015)

"Victor Eijkhout ""Introduction to High Performance Scientific Computing"", Dec 2015 (, pdf dostupan na
Fayez Gebali ""Algorithms and Parallel Computing"" (, pdf dostupan na:"

Minimal learning outcomes

  • Understanding of basic elements, tools and concepts of cloud computing.
  • Understand cloud DWH and basic concepts (benefits and drawbacks).
  • Understand BI concept in the cloud and select key BI cloud tools common characteristics.
  • Identify which already developed services in the cloud can be used, how we can use them (including those based on artificial intelligence).

Preferred learning outcomes

  • Explain cloud computing posibilities using business example
  • Valuate key difference between cloud DWH and "on premise" (traditional) DWH
  • Understand BI in the cloud elements using organization and cloud analysis project.
  • Apply cloud services to the concrete data set.
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