1. SylabUZ
2. Wydział Matematyki, Informatyki i Ekonometrii
3. semestr letni 2023/2024
4. Data Engineering - drugiego stopnia z tyt. magistra
5. Big Data - Models, Tools, Data Processing

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Big Data - Models, Tools, Data Processing - opis przedmiotu

Cel przedmiotu

The aim of the course is the introduction of the Big Data notion and also models and tools suitable for handling and processing huge amounts of data.

Wymagania wstępne

Zapisz zmiany

Basics of information technologies.

Zakres tematyczny

1. Big Data - genesis, concepts, basic architectures
2. Data anaylitcs Lifecycle - phases, artifacts, stakeholders.
3. Big Data application design, tools, Agile methodology.
4. Open source tools for handling and processing Big Data: Apache Hadoop (HDFS, Avro, Pig, Hive, Hbase, Spark) and MapReduce algorithm (Python, MrJob, Java).
5. Big Data exploration methods - data classification, clusterization and recommendation.

Metody kształcenia

Traditional lecture; computer laboratories are based on the instructions prepared for separated topics handed by the lecturer. Discussions aiming at the deepening of knowledge and better understanding and managing of the taught concerns.

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

The laboratory grade concerns the student's level of preparation and activity during laboratory classes training.

The grade from the lecture is the result from the written exam.

The final grade for the course consists of two elements: laboratory classes – 50% and written exam – 50%.

Positive grades from laboratory classes and the exam are a condition for passing course.

Literatura podstawowa

1. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. EMC Education Services (Editor). Wiley 2015.
2. Mahmoud Parsian: Data Algorithms. Recipes for Scaling Up with Hadoop and Spark O'Reilly Media 2015.
3. Tom White, Hadoop: The Definitive Guide, Fourth Edition, O'Reilly Media 2015.
4. Holden Karau, Andy Konwinski, Patrick Wendel, Matei Zaharia: Learning Spark - Lightning-Fast Big Data Analysis O'Reilly Media 2015.
5. Victror Mayer-Schonberger, Kenneth Cukier: Big Data - A Revolution That Will Transform How We Live, Work, and Think, John Murray Publishers, 2013.

Literatura uzupełniająca

1. Sean Owen, Robin Anil, Ted Dunning, and Ellen Friedman: Mahout in Action. Wydawnictwo Manning 2015.

Uwagi

Zmodyfikowane przez dr Maciej Niedziela (ostatnia modyfikacja: 11-04-2024 15:51)