1. SylabUZ
2. Wydział Informatyki, Elektrotechniki i Automatyki
3. semestr zimowy 2022/2023
4. Informatyka - Program Erasmus drugiego stopnia
5. Big data and business intelligence

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Big data and business intelligence - opis przedmiotu

Cel przedmiotu

Teaching students how to choose the right data analysis techniques depending on the scale of the problem being considered and the type of analysis being carried out.
Teaching students to work using modern platforms for data storage and processing.
Teaching students selected techniques to analyze large data sets, mainly textual.

Wymagania wstępne

Zapisz zmiany

Introduction to databases, Basics of statistics

Zakres tematyczny

Big Data: An introduction to processing large amounts of data.

Non-relational databases: Reminder of the basic issues related to relational databases. Advantages and disadvantages of these databases. Basic problems related to the use of relational databases to store and process larger and larger amounts of increasingly distributed data. Horizontal and vertical scaling of databases. A new concept of databases not based on the traditional relational model. CAP and BASE theory. Aggregate data models. Key-value, column, document and graph databases. Database replication. Sharing resources in databases. Map-Reduce methodology. Presentation of a few selected non-relational database systems (e.g. MongoDB, Cassandra, Redis, Neo4J, Oracle NoSQL Database).

Selected IT systems: Large-scale business analytics: modern solutions used for transmission, storage and processing of large data sets. Basics of data processing using convolutional neural networks (CNN). Tensorflow and Keras libraries. Working in the Google Colaboratory cloud environment.

Elements of Text Mining: Introduction to Text Mining. Pre-processing of text documents. Stemming algorithms.  Keyword searching. Organization of documents in the form of a term-document matrix (TDM). Selected elements of linear algebra and their application to Text Mining. Grouping and classifying of text documents. Create document summaries. Wordclouds. Sentiment analysis. Selected IT systems and libraries for Text Mining.

Metody kształcenia

Lecture, laboratory exercises.

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

Lecture – the passing condition is to obtain a positive mark from the final test.

Laboratory – the passing condition is to obtain positive marks from all laboratory exercises to be planned during the semester.

Calculation of the final grade: lecture 50% + laboratory 50%

Literatura podstawowa

1. Daniel Larose: Discovering Knowledge in Data: An Introduction to Data Mining, Wiley, 2014
2. Zdravko Markov, Daniel Larose: Data Mining the Web. Patterns in Web Content, Structure and Usage, Wiley, 2007
3. Francois Chollet: Deep Learning. Deep Learning with Python, Manning Publications Co., 2018
4. Machale W. Berry, Murray Browne: Understanding Search Engines. Mathematical Modeling and Text Retrieval, SIAM, 1999
5. Lars Elden: Matrix Methods in Data Mining and Pattern Recognition, SIAM, 2007
6. Python, R, Keras and TensorFlow documentation

Literatura uzupełniająca

Uwagi

Zmodyfikowane przez dr hab. inż. Artur Gramacki, prof. UZ (ostatnia modyfikacja: 20-04-2022 23:27)