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Summary Data wrangling and data analysis
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Applied Data Science Utrecht University (UU): Data handling and preparation, supervised & non-supervised machine learning, using SQL, Python, and R.
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Silberschatz Et Al. 2019 – Database Systems Concepts1.1. Database-System Applications
Database-management system (DBMS): collection of interrelated data and a set of
programs to access those data goal of a DBMS is information storage and manipulation
- Back-office: database internal of an organisation
- End-users: interaction between user and database within organisation
Two modes of databases usage:
- Online transaction processing: where large number users use the database, with
each user retrieving relatively small amounts of data, and performing small updates
- Data analytics: the processing of data to draw conclusions, and infer rules or decision
procedures, which are then used to drive business decisions
The field of data mining combines knowledge-discovery techniques invented by artificial
intelligence researchers and statistical analysts with efficient implementation techniques that
enable them to be used on extremely large databases
1.2. Purpose of Database Systems
File-processing system: store permanent records in various files, and it needs different
application programs to extract records from, and add records to, the appropriate files.
Disadvantages organizational information in file-processing system:
- Data redundancy and inconsistency: different programmers / structures /
programming languages or double data per identifier over different groups
o Redundancy leads to higher storage and costs
o Inconsistency leads to disagreement of data
- Difficulty in accessing data: conventional file-processing environments do not
allow needed data to be retrieved in a convenient and efficient manner. More
responsive data-retrieval systems are required for general use
- Data isolation: because data is scattered in various files, and files may be in different
formats, writing new application programs to retrieve the appropriate data is difficult
- Integrity problems: data values stored in the data base must satisfy certain types of
consistency constraints, because new data and software may be dissimilar
- Atomicity problems: a computer system is subject to failure; data transfer must be
atomic — it must happen in its entirety or not at all
- Concurrent access anomalies: systems must allow multiple users to update data
simultaneously. The system must maintain some form of supervision
- Security problems: not every user of the database system should be able to access
all the data
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, 1.3. View of Data
The data models can be classified into four different categories:
- Relational model: collection of tables to represent both data and the relationships
among those data (record-based model; matrix / excel sheet)
- Entity-relationship (E-R) model: collection of basic objects, called entities, and
relationships among these objects
- Semi-structured data model: permit the specification of data where individual
data items of the same type may have different sets of attributes (JSON / XML)
- Object-based data model: database systems allow procedures to be stored in the
database system and executed by the database system (Java, C++, or C#)
Database-system users are not computer trained, developers hide the complexity from users
through several levels of data abstraction, to simplify users’ interactions with the system:
- Physical level: lowest level of abstraction describes how the data are stored. The
physical level describes complex low-level data structures in detail
- Logical level: next-higher level of abstraction describes what data are stored in the
database, and what relationships exist among those data. The logical level thus
describes the entire database in terms of a small number of relatively simple structures
- View level: highest level of abstraction describes only part of the entire database.
Even though the logical level uses simpler structures, complexity remains because of
the variety of information stored in a large database
Instance: collection of information stored in the database at a particular moment
Schema: overall design of the database (physical; logical schema; view level subschema)
1.4. Database Languages
Database systems provide a data-definition language (DDL) to specify database schema
and a data-manipulation language (DML) to express database queries and updates (SQL)
Database systems implement only integrity constraints testable with minimal overhead:
- Domain constraints: domain of possible values must be associated with every
attribute (for example, integer types, character types, date/time types)
- Referential integrity: ensure that a value that appears in one relation for a given set
of attributes also appears in a certain set of attributes in another relation
- Authorisation: differentiate among users as far as type of access they are permitted
on various data values in the database; read / insert / update / delete authorisation
Data-definition language: SQL provides a rich DDL that allows one to define tables with
data types and integrity constraints
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