Data Warehouses and Data Analysis
Semester: Β,
ECTS: 7.5
Georgia Garani
(Course Coordinator)
Vasiliki Koutsonikola
Syllabus
Week 1: Introduction to Data Warehouses
Week 2: Data Warehouses Features – Data Marts
Week 3: OLTP – OLAP
Week 4: Multidimensional Data Model
Week 5: OLAP Operations – Materialized View
Week 6: Advanced Topics in Data Warehouses
Week 7: Overview of data analysis/mining techniques
Week 8: Introduction to R – Part 1
Week 9: Introduction to R – Part 2
Week 10: Data Analysis with R
Week 11: Sentiment Analysis with R
Week 12: Big Data Overview
Week 13: Data Warehouse Operational Processes – Big Data Tools
Week 2: Data Warehouses Features – Data Marts
Week 3: OLTP – OLAP
Week 4: Multidimensional Data Model
Week 5: OLAP Operations – Materialized View
Week 6: Advanced Topics in Data Warehouses
Week 7: Overview of data analysis/mining techniques
Week 8: Introduction to R – Part 1
Week 9: Introduction to R – Part 2
Week 10: Data Analysis with R
Week 11: Sentiment Analysis with R
Week 12: Big Data Overview
Week 13: Data Warehouse Operational Processes – Big Data Tools
Suggested Bibliography
- Kimball R. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition, Wiley, 2013.
- Golfarelli M., Rizzi S. Data Warehouse Design: Modern Principles and Methodologies, McGraw-Hill Education, 2009.
- Bolton J. (Editor) Data Warehousing Essentials. Larsen and Keller Education, 2019.
- 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.
- Tan P.N., Steinbach M., Karpatne A., Kumar V. Introduction to Data Mining, Pearson Addison Wesley, 2006.
- Mailund T. Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist, 1st ed. Edition, Apress, 2017.
- Nagabhushana S. Data Warehousing OLAP and Data Mining, New Age International Publisher, 2006.