Complete Summary Data Science and Society

DATA SCIENCE AND SOCIETY

Complete summary (INFOMDSS-2024)




Hamdi, M. (Majdouline)

Utrecht University

, Contents
Part A ........................................................................................................................................ 3
Data Science Concepts............................................................................................................... 3
CRISP-DM and SEMMA Models.................................................................................................. 3
Case Studies on Data Science Applications and Challenges ................................................ 3
Containerization (Docker) ......................................................................................................... 3
Current Challenges in Containerization ................................................................................... 4
Important pictures: .................................................................................................................... 4

Part B: ....................................................................................................................................... 6
Data Science and Strategic Alignment .................................................................................... 6
Business Performance Management (BPM) Tools .................................................................. 6
Data Warehousing ...................................................................................................................... 6
Data Warehousing Process and Architecture .......................................................................... 7
Data Modeling.............................................................................................................................. 7
Key Takeaways and Challenges................................................................................................ 7
Data Integration Methods .......................................................................................................... 8
Data Formats for Integration .................................................................................................... 8
Remote Data Access ................................................................................................................... 8
API Examples for Data Access .................................................................................................. 8
SQL Basics ................................................................................................................................... 9
Graphical Data Modeling Interfaces ......................................................................................... 9
Learning Objectives .................................................................................................................... 9

Part C: ......................................................................................................................................10
Describing Univariate Data ...................................................................................................... 10
Describing Bivariate and Multivariate Data........................................................................... 12
Clustering objectives ................................................................................................................ 12
Data Quality and Integrity ....................................................................................................... 13
Visualization and Dashboards ................................................................................................. 14

Part D: ......................................................................................................................................15
Predictive Analytics Overview ................................................................................................. 15
Classification .............................................................................................................................. 15
Evaluation of Classifiers ........................................................................................................... 16




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