Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Administracja Centralna Uczelni - Wymiana międzynarodowa (S1)

Sylabus przedmiotu Big Data analytics tools and software:

Informacje podstawowe

Kierunek studiów Wymiana międzynarodowa
Forma studiów studia stacjonarne Poziom pierwszego stopnia
Tytuł zawodowy absolwenta
Obszary studiów
Profil
Moduł
Przedmiot Big Data analytics tools and software
Specjalność przedmiot wspólny
Jednostka prowadząca Katedra Inżynierii Oprogramowania
Nauczyciel odpowiedzialny Agnieszka Konys <Agnieszka.Konys@zut.edu.pl>
Inni nauczyciele
ECTS (planowane) 5,0 ECTS (formy) 5,0
Forma zaliczenia zaliczenie Język angielski
Blok obieralny Grupa obieralna

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
wykładyW1 30 2,00,40zaliczenie
laboratoriaL1 30 3,00,60zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Basic understanding of main business processes

Cele przedmiotu

KODCel modułu/przedmiotu
C-1Familiar with the tools and software for large scale datasets
C-2The ability to analyze the characteristics of data reaching the IT system, knowledge of the tasks that need to be dealt with to process this data, and the creation and selection of appropriate methods, computer environment and software in order to effectively solve the tasks.
C-3Be able to design Data Warehouse and use MDX effectively.

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
laboratoria
T-L-1Instructions for Downloading and Installing the Exercise Environment1
T-L-2HIVE: Creating Databases and Tables, SQL SELECT Essentials, Working with Data Types, Working with File Types, Loading Files into HDFS7
T-L-3Working with Spark in Python: Use Spark core concepts such as RDDs, transformations, actions to operate on large datasets7
T-L-4Application of information extraction methods and techniques5
T-L-5Big data processing and analysis tools6
T-L-6Big Data Visualization tools4
30
wykłady
T-W-1Classic Data vs. Big Data2
T-W-2Big Data Essentials: Hadoop, HDFS, MapReduce2
T-W-3The Hadoop Stack Ecosystem2
T-W-4Introduction to NoSQL Databases2
T-W-5Orientation to SQL on Big Data2
T-W-6Managing Big Data in Clusters: Hive, Hue2
T-W-7Introduction to Apache Spark2
T-W-8Information extraction from text2
T-W-9Methods and techniques for information extraction4
T-W-10Big data processing and analysis tools4
T-W-11Big Data Visualization tools4
T-W-12Exam2
30

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
laboratoria
A-L-1Laboratory attendance30
A-L-2Student individual work45
75
wykłady
A-W-1Lectures attendance30
A-W-2Student individual work20
50

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1Informative lectures
M-2Discussion
M-3Work with computers at laboratories

Sposoby oceny

KODSposób oceny
S-1Ocena formująca: Written exam
S-2Ocena formująca: Continuous assessment

Zamierzone efekty uczenia się - wiedza

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WI_1-_??_W01
After the course the student should have knowledge of the methods, algorithms and software to solve particular problems of processing large data sets.
C-1, C-2T-W-11, T-W-1, T-W-2, T-W-3, T-W-4, T-W-5, T-W-6, T-W-7, T-W-10, T-W-12, T-W-8, T-W-9M-1, M-2S-1
WM-WI_1-_??_W02
After the course the student should have knowledge of the methods and tools for data analysis on large data sets.
C-1, C-2T-W-11, T-W-1, T-W-2, T-W-3, T-W-4, T-W-5, T-W-6, T-W-7, T-W-10, T-W-12, T-W-8, T-W-9M-1, M-2S-1
WM-WI_1-_??_W03
Student will know how to integrate the Big Data and Data Warehousing.
C-1, C-2T-W-11, T-W-1, T-W-2, T-W-3, T-W-4, T-W-5, T-W-6, T-W-7, T-W-10, T-W-12, T-W-8, T-W-9M-1, M-2S-1

Zamierzone efekty uczenia się - umiejętności

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WI_1-_??_U01
The student should know how to use methods and tools for data analysis on large data sets.
C-1, C-2, C-3T-L-1, T-L-2, T-L-3, T-L-4, T-L-5, T-L-6M-2, M-3S-2
WM-WI_1-_??_U02
The student should be able to analyze and classify data features, choose the appropriate software and techniques for data processing and apply research results to solve specific problems.
C-1, C-2, C-3T-L-1, T-L-2, T-L-3, T-L-4, T-L-5, T-L-6M-2, M-3S-2
WM-WI_1-_??_U03
Student is able to design and querying Data Warehouse.
C-1, C-3T-L-1, T-L-2, T-L-3, T-L-4, T-L-5M-2, M-3S-2

Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WI_1-_??_K01
The student is competent in solving large data processing tasks using modern methods, algorithms and programs and can apply knowledge and skills in this field to solve specific problems.
C-1, C-2T-W-11, T-W-1, T-W-2, T-W-3, T-W-4, T-W-5, T-W-6, T-W-7, T-W-10, T-W-8, T-W-9, T-L-1, T-L-2, T-L-3, T-L-4, T-L-5, T-L-6M-1, M-2, M-3S-2

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium oceny
WM-WI_1-_??_W01
After the course the student should have knowledge of the methods, algorithms and software to solve particular problems of processing large data sets.
2,0
3,0Student knows and understands basic methods, algorithms and software to solve problems of processing large data sets
3,5
4,0
4,5
5,0
WM-WI_1-_??_W02
After the course the student should have knowledge of the methods and tools for data analysis on large data sets.
2,0
3,0Student knows and understands basic methods and for data analysis.
3,5
4,0
4,5
5,0
WM-WI_1-_??_W03
Student will know how to integrate the Big Data and Data Warehousing.
2,0
3,0Student knows how to integrate the Big Data and Data Warehousing.
3,5
4,0
4,5
5,0

Kryterium oceny - umiejętności

Efekt uczenia sięOcenaKryterium oceny
WM-WI_1-_??_U01
The student should know how to use methods and tools for data analysis on large data sets.
2,0
3,0Student is able to do simple data analysis on large data sets
3,5
4,0
4,5
5,0
WM-WI_1-_??_U02
The student should be able to analyze and classify data features, choose the appropriate software and techniques for data processing and apply research results to solve specific problems.
2,0
3,0Student is able to solve simple data analysis problems.
3,5
4,0
4,5
5,0
WM-WI_1-_??_U03
Student is able to design and querying Data Warehouse.
2,0
3,0Student is able to design and quering Data Warehouse
3,5
4,0
4,5
5,0

Kryterium oceny - inne kompetencje społeczne i personalne

Efekt uczenia sięOcenaKryterium oceny
WM-WI_1-_??_K01
The student is competent in solving large data processing tasks using modern methods, algorithms and programs and can apply knowledge and skills in this field to solve specific problems.
2,0
3,0Student is able to apply knowledge and skills to solve specific problems.
3,5
4,0
4,5
5,0

Literatura podstawowa

  1. Martin Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, O'Reilly, United States of America, 2017
  2. Tom White, Hadoop: The Definitive Guide (4th Edition), O'Reilly, 2015, ISBN: 9781491901632
  3. Vince Reynolds, Big Data For Beginners: Understanding SMART Big Data, Data Mining & Data Analytics For improved Business Performance, Life Decisions & More! (Data ... Computer Programming, Growth Hacking, ITIL), Createspace Independent Publishing Platform, 2016
  4. Alejandro Vaisman Esteban Zimányi, Data Warehouse Systems Design and Implementation, Springer-Verlag Berlin Heidelberg, 2013, DOI: 10.1007/978-3-642-54655-6

Literatura dodatkowa

  1. EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, EMC Education Services, 2015, ISBN: 978-1-118-87613-8

Treści programowe - laboratoria

KODTreść programowaGodziny
T-L-1Instructions for Downloading and Installing the Exercise Environment1
T-L-2HIVE: Creating Databases and Tables, SQL SELECT Essentials, Working with Data Types, Working with File Types, Loading Files into HDFS7
T-L-3Working with Spark in Python: Use Spark core concepts such as RDDs, transformations, actions to operate on large datasets7
T-L-4Application of information extraction methods and techniques5
T-L-5Big data processing and analysis tools6
T-L-6Big Data Visualization tools4
30

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1Classic Data vs. Big Data2
T-W-2Big Data Essentials: Hadoop, HDFS, MapReduce2
T-W-3The Hadoop Stack Ecosystem2
T-W-4Introduction to NoSQL Databases2
T-W-5Orientation to SQL on Big Data2
T-W-6Managing Big Data in Clusters: Hive, Hue2
T-W-7Introduction to Apache Spark2
T-W-8Information extraction from text2
T-W-9Methods and techniques for information extraction4
T-W-10Big data processing and analysis tools4
T-W-11Big Data Visualization tools4
T-W-12Exam2
30

Formy aktywności - laboratoria

KODForma aktywnościGodziny
A-L-1Laboratory attendance30
A-L-2Student individual work45
75
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1Lectures attendance30
A-W-2Student individual work20
50
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WI_1-_??_W01After the course the student should have knowledge of the methods, algorithms and software to solve particular problems of processing large data sets.
Cel przedmiotuC-1Familiar with the tools and software for large scale datasets
C-2The ability to analyze the characteristics of data reaching the IT system, knowledge of the tasks that need to be dealt with to process this data, and the creation and selection of appropriate methods, computer environment and software in order to effectively solve the tasks.
Treści programoweT-W-11Big Data Visualization tools
T-W-1Classic Data vs. Big Data
T-W-2Big Data Essentials: Hadoop, HDFS, MapReduce
T-W-3The Hadoop Stack Ecosystem
T-W-4Introduction to NoSQL Databases
T-W-5Orientation to SQL on Big Data
T-W-6Managing Big Data in Clusters: Hive, Hue
T-W-7Introduction to Apache Spark
T-W-10Big data processing and analysis tools
T-W-12Exam
T-W-8Information extraction from text
T-W-9Methods and techniques for information extraction
Metody nauczaniaM-1Informative lectures
M-2Discussion
Sposób ocenyS-1Ocena formująca: Written exam
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Student knows and understands basic methods, algorithms and software to solve problems of processing large data sets
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WI_1-_??_W02After the course the student should have knowledge of the methods and tools for data analysis on large data sets.
Cel przedmiotuC-1Familiar with the tools and software for large scale datasets
C-2The ability to analyze the characteristics of data reaching the IT system, knowledge of the tasks that need to be dealt with to process this data, and the creation and selection of appropriate methods, computer environment and software in order to effectively solve the tasks.
Treści programoweT-W-11Big Data Visualization tools
T-W-1Classic Data vs. Big Data
T-W-2Big Data Essentials: Hadoop, HDFS, MapReduce
T-W-3The Hadoop Stack Ecosystem
T-W-4Introduction to NoSQL Databases
T-W-5Orientation to SQL on Big Data
T-W-6Managing Big Data in Clusters: Hive, Hue
T-W-7Introduction to Apache Spark
T-W-10Big data processing and analysis tools
T-W-12Exam
T-W-8Information extraction from text
T-W-9Methods and techniques for information extraction
Metody nauczaniaM-1Informative lectures
M-2Discussion
Sposób ocenyS-1Ocena formująca: Written exam
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Student knows and understands basic methods and for data analysis.
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WI_1-_??_W03Student will know how to integrate the Big Data and Data Warehousing.
Cel przedmiotuC-1Familiar with the tools and software for large scale datasets
C-2The ability to analyze the characteristics of data reaching the IT system, knowledge of the tasks that need to be dealt with to process this data, and the creation and selection of appropriate methods, computer environment and software in order to effectively solve the tasks.
Treści programoweT-W-11Big Data Visualization tools
T-W-1Classic Data vs. Big Data
T-W-2Big Data Essentials: Hadoop, HDFS, MapReduce
T-W-3The Hadoop Stack Ecosystem
T-W-4Introduction to NoSQL Databases
T-W-5Orientation to SQL on Big Data
T-W-6Managing Big Data in Clusters: Hive, Hue
T-W-7Introduction to Apache Spark
T-W-10Big data processing and analysis tools
T-W-12Exam
T-W-8Information extraction from text
T-W-9Methods and techniques for information extraction
Metody nauczaniaM-1Informative lectures
M-2Discussion
Sposób ocenyS-1Ocena formująca: Written exam
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Student knows how to integrate the Big Data and Data Warehousing.
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WI_1-_??_U01The student should know how to use methods and tools for data analysis on large data sets.
Cel przedmiotuC-1Familiar with the tools and software for large scale datasets
C-2The ability to analyze the characteristics of data reaching the IT system, knowledge of the tasks that need to be dealt with to process this data, and the creation and selection of appropriate methods, computer environment and software in order to effectively solve the tasks.
C-3Be able to design Data Warehouse and use MDX effectively.
Treści programoweT-L-1Instructions for Downloading and Installing the Exercise Environment
T-L-2HIVE: Creating Databases and Tables, SQL SELECT Essentials, Working with Data Types, Working with File Types, Loading Files into HDFS
T-L-3Working with Spark in Python: Use Spark core concepts such as RDDs, transformations, actions to operate on large datasets
T-L-4Application of information extraction methods and techniques
T-L-5Big data processing and analysis tools
T-L-6Big Data Visualization tools
Metody nauczaniaM-2Discussion
M-3Work with computers at laboratories
Sposób ocenyS-2Ocena formująca: Continuous assessment
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Student is able to do simple data analysis on large data sets
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WI_1-_??_U02The student should be able to analyze and classify data features, choose the appropriate software and techniques for data processing and apply research results to solve specific problems.
Cel przedmiotuC-1Familiar with the tools and software for large scale datasets
C-2The ability to analyze the characteristics of data reaching the IT system, knowledge of the tasks that need to be dealt with to process this data, and the creation and selection of appropriate methods, computer environment and software in order to effectively solve the tasks.
C-3Be able to design Data Warehouse and use MDX effectively.
Treści programoweT-L-1Instructions for Downloading and Installing the Exercise Environment
T-L-2HIVE: Creating Databases and Tables, SQL SELECT Essentials, Working with Data Types, Working with File Types, Loading Files into HDFS
T-L-3Working with Spark in Python: Use Spark core concepts such as RDDs, transformations, actions to operate on large datasets
T-L-4Application of information extraction methods and techniques
T-L-5Big data processing and analysis tools
T-L-6Big Data Visualization tools
Metody nauczaniaM-2Discussion
M-3Work with computers at laboratories
Sposób ocenyS-2Ocena formująca: Continuous assessment
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Student is able to solve simple data analysis problems.
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WI_1-_??_U03Student is able to design and querying Data Warehouse.
Cel przedmiotuC-1Familiar with the tools and software for large scale datasets
C-3Be able to design Data Warehouse and use MDX effectively.
Treści programoweT-L-1Instructions for Downloading and Installing the Exercise Environment
T-L-2HIVE: Creating Databases and Tables, SQL SELECT Essentials, Working with Data Types, Working with File Types, Loading Files into HDFS
T-L-3Working with Spark in Python: Use Spark core concepts such as RDDs, transformations, actions to operate on large datasets
T-L-4Application of information extraction methods and techniques
T-L-5Big data processing and analysis tools
Metody nauczaniaM-2Discussion
M-3Work with computers at laboratories
Sposób ocenyS-2Ocena formująca: Continuous assessment
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Student is able to design and quering Data Warehouse
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WI_1-_??_K01The student is competent in solving large data processing tasks using modern methods, algorithms and programs and can apply knowledge and skills in this field to solve specific problems.
Cel przedmiotuC-1Familiar with the tools and software for large scale datasets
C-2The ability to analyze the characteristics of data reaching the IT system, knowledge of the tasks that need to be dealt with to process this data, and the creation and selection of appropriate methods, computer environment and software in order to effectively solve the tasks.
Treści programoweT-W-11Big Data Visualization tools
T-W-1Classic Data vs. Big Data
T-W-2Big Data Essentials: Hadoop, HDFS, MapReduce
T-W-3The Hadoop Stack Ecosystem
T-W-4Introduction to NoSQL Databases
T-W-5Orientation to SQL on Big Data
T-W-6Managing Big Data in Clusters: Hive, Hue
T-W-7Introduction to Apache Spark
T-W-10Big data processing and analysis tools
T-W-8Information extraction from text
T-W-9Methods and techniques for information extraction
T-L-1Instructions for Downloading and Installing the Exercise Environment
T-L-2HIVE: Creating Databases and Tables, SQL SELECT Essentials, Working with Data Types, Working with File Types, Loading Files into HDFS
T-L-3Working with Spark in Python: Use Spark core concepts such as RDDs, transformations, actions to operate on large datasets
T-L-4Application of information extraction methods and techniques
T-L-5Big data processing and analysis tools
T-L-6Big Data Visualization tools
Metody nauczaniaM-1Informative lectures
M-2Discussion
M-3Work with computers at laboratories
Sposób ocenyS-2Ocena formująca: Continuous assessment
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Student is able to apply knowledge and skills to solve specific problems.
3,5
4,0
4,5
5,0