Data Warehouses and Data Analysis Techniques – DWDA

Course Coordinator: Georgia Garani, Vasiliki Koutsonikola     ECTS: 7.5,      Semester: B (D)

Syllabus

Introduction to Data Warehouses, ETL processes, conceptual-logical-physical design, dimensional model, data cubes, data marts, OLAP requests, practical training, development of programming tools for communicating with a data warehouse with the aim of retrieving and pre-processing data using the R language, application of data analysis techniques and qualitative/quantitative evaluation of the results, visualization methods, modern trends in storing large volumes of data and their analysis techniques.

Recommended Bibliography

  • Νανόπουλος Αλ., Μανωλόπουλος Γ. Εισαγωγή στην Εξόρυξη Δεδομένων και τις Αποθήκες Δεδομένων, Εκδόσεις Νέων Τεχνολογιών, 2010
  • Kimball The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition, Wiley, 2013
  • Golfarelli and  Rizzi S. Data Warehouse Design: Modern Principles and Methodologies, McGraw-Hill Education, 2009
  • Bolton J. (Editor) Data Warehousing Essentials. Larsen and Keller Education, 2019
  • Pang-Ning Tan, M.Steinbach and V.Kumar, “Εισαγωγή στην Εξόρυξη Δεδομένων – 2η Έκδοση”, ISBN: 978-960-418-813-0, Εκδόσεις Τζιόλα, 2018
  • Mailund T. Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist, 1st Edition, Apress, 2017
  • Nagabhushana S. Data Warehousing OLAP and Data Mining. New Age International Publisher, 2006

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