Administracja Centralna Uczelni - Wymiana międzynarodowa (S2)
Sylabus przedmiotu Big Data analytics tools and software:
Informacje podstawowe
| Kierunek studiów | Wymiana międzynarodowa | ||
|---|---|---|---|
| Forma studiów | studia stacjonarne | Poziom | drugiego 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
Wymagania wstępne
| KOD | Wymaganie wstępne |
|---|---|
| W-1 | Basic understanding of main business processes |
Cele przedmiotu
| KOD | Cel modułu/przedmiotu |
|---|---|
| C-1 | Familiar with the tools and software for large scale datasets |
| C-2 | The 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-3 | Be able to design Data Warehouse and use MDX effectively. |
Treści programowe z podziałem na formy zajęć
| KOD | Treść programowa | Godziny |
|---|---|---|
| laboratoria | ||
| T-L-1 | Instructions for Downloading and Installing the Exercise Environment | 1 |
| T-L-2 | HIVE: Creating Databases and Tables, SQL SELECT Essentials, Working with Data Types, Working with File Types, Loading Files into HDFS | 7 |
| T-L-3 | Working with Spark in Python: Use Spark core concepts such as RDDs, transformations, actions to operate on large datasets | 7 |
| T-L-4 | Application of information extraction methods and techniques | 5 |
| T-L-5 | Big data processing and analysis tools | 6 |
| T-L-6 | Big Data Visualization tools | 4 |
| 30 | ||
| wykłady | ||
| T-W-1 | Classic Data vs. Big Data | 2 |
| T-W-2 | Big Data Essentials: Hadoop, HDFS, MapReduce | 2 |
| T-W-3 | The Hadoop Stack Ecosystem | 2 |
| T-W-4 | Introduction to NoSQL Databases | 2 |
| T-W-5 | Orientation to SQL on Big Data | 2 |
| T-W-6 | Managing Big Data in Clusters: Hive, Hue | 2 |
| T-W-7 | Introduction to Apache Spark | 2 |
| T-W-8 | Information extraction from text | 2 |
| T-W-9 | Methods and techniques for information extraction | 4 |
| T-W-10 | Big data processing and analysis tools | 4 |
| T-W-11 | Big Data Visualization tools | 4 |
| T-W-12 | Exam | 2 |
| 30 | ||
Obciążenie pracą studenta - formy aktywności
| KOD | Forma aktywności | Godziny |
|---|---|---|
| laboratoria | ||
| A-L-1 | Laboratory attendance | 30 |
| A-L-2 | Student individual work | 45 |
| 75 | ||
| wykłady | ||
| A-W-1 | Lectures attendance | 30 |
| A-W-2 | Student individual work | 20 |
| 50 | ||
Metody nauczania / narzędzia dydaktyczne
| KOD | Metoda nauczania / narzędzie dydaktyczne |
|---|---|
| M-1 | Informative lectures |
| M-2 | Discussion |
| M-3 | Work with computers at laboratories |
Sposoby oceny
| KOD | Sposób oceny |
|---|---|
| S-1 | Ocena formująca: Written exam |
| S-2 | Ocena formująca: Continuous assessment |
Zamierzone efekty uczenia się - wiedza
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób 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-2 | T-W-2, T-W-7, T-W-5, T-W-8, T-W-10, T-W-3, T-W-11, T-W-6, T-W-4, T-W-12, T-W-9, T-W-1 | M-1, M-2 | S-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-2, C-1 | T-W-12, T-W-1, T-W-8, T-W-7, T-W-6, T-W-3, T-W-4, T-W-2, T-W-9, T-W-10, T-W-5, T-W-11 | M-1, M-2 | S-1 |
| WM-WI_1-_??_W03 Student will know how to integrate the Big Data and Data Warehousing. | — | — | C-1, C-2 | T-W-7, T-W-1, T-W-2, T-W-5, T-W-12, T-W-11, T-W-4, T-W-10, T-W-9, T-W-8, T-W-6, T-W-3 | M-2, M-1 | S-1 |
Zamierzone efekty uczenia się - umiejętności
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|
| WM-WI_1-_??_U01 The student should know how to use methods and tools for data analysis on large data sets. | — | — | C-3, C-1, C-2 | T-L-2, T-L-1, T-L-5, T-L-3, T-L-4, T-L-6 | M-3, M-2 | S-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-3, C-2 | T-L-3, T-L-1, T-L-5, T-L-6, T-L-4, T-L-2 | M-3, M-2 | S-2 |
| WM-WI_1-_??_U03 Student is able to design and querying Data Warehouse. | — | — | C-3, C-1 | T-L-2, T-L-1, T-L-4, T-L-3, T-L-5 | M-3, M-2 | S-2 |
Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób 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-2 | T-W-2, T-W-8, T-L-5, T-W-3, T-L-4, T-W-7, T-W-1, T-W-5, T-L-6, T-W-4, T-L-2, T-W-6, T-W-9, T-W-10, T-L-3, T-L-1, T-W-11 | M-3, M-2, M-1 | S-2 |
Kryterium oceny - wiedza
| Efekt uczenia się | Ocena | Kryterium 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 | |
| 3,0 | Student 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 | |
| 3,0 | Student 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 | |
| 3,0 | Student knows how to integrate the Big Data and Data Warehousing. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 |
Kryterium oceny - umiejętności
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| WM-WI_1-_??_U01 The student should know how to use methods and tools for data analysis on large data sets. | 2,0 | |
| 3,0 | Student 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 | |
| 3,0 | Student 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 | |
| 3,0 | Student 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 się | Ocena | Kryterium 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 | |
| 3,0 | Student is able to apply knowledge and skills to solve specific problems. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 |
Literatura podstawowa
- Martin Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, O'Reilly, United States of America, 2017
- Tom White, Hadoop: The Definitive Guide (4th Edition), O'Reilly, 2015, ISBN: 9781491901632
- 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
- 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
- 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