Technologies for Big Data Analytics
| Title | Τεχνολογίες Ανάλυσης Μεγάλων Δεδομένων / Technologies for Big Data Analytics |
| Code | DWS102 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Apostolos Papadopoulos |
| Common | Yes |
| Status | Active |
| Course ID | 600016256 |
Programme of Study: PMS EPISTĪMĪ DEDOMENŌN KAI PAGKOSMIOU ISTOU (2018 éōs sīmera) PF
Registered students: 19
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | 1 | 1 | 7.5 |
| Academic Year | 2020 – 2021 |
| Class Period | Winter |
| Faculty Instructors | |
| Weekly Hours | 3 |
| Class ID | 600176643
|
Course Type 2016-2020
- Scientific Area
- Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600176643
- At the Website of the School: http://delab.csd.auth.gr/courses/c_bigdata
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=7951
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Learning Outcomes
1. Students will get important knowledge in big data management and analytics
2. They will work in teams
3. They will be more confident by presenting their work in class
4. They will get in contact with modern big data analytics techniques with a lot of applications in Industry.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work autonomously
- Work in teams
- Generate new research ideas
Course Content (Syllabus)
Introduction to Big Data Management and Analytics
- Hadoop: basic and advanved topics
- The Hadoop ecosystem: HDFS, Hbase, Pig, Hive
- NoSQL databases
- Theoretical issues in MapReduce
- The Scala programming language
- The Spark platform: basic and advanced issues
- Streaming, SQL, Machine Learning, GraphΧ: the basic libraries
- Data exploration using SparkR
- Algorithm design in Spark
- Graph databases
- Other systems: Giraph, GraphLab, Hama, BlinlkDB
Keywords
big data, data management, data mining from big data, big data analytics
Educational Material Types
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Reading Assigment | 100 | ✓ | ✓ | ||
| Project | 55 | ✓ | ✓ | ||
| Written assigments | 32 | ✓ | ✓ | ||
| Total | 226 |
Student Assessment
Student Assessment methods
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Formative, Summative)
- Performance / Staging (Formative, Summative)
- Written Exam with Problem Solving (Summative)
- Report (Formative, Summative)
Bibliography
Additional bibliography for study
M. Zaharia, B. Chambers: Spark, the definitive guide, O' Reilly, 2018.
H. Karau, A. Konwinski, P. Wendell, M. Zaharia: Learning Spark, O' Reilly, 2015.
N. Lynch: Distributed algorithms, Morgan Kaufmann, 1996.
I. Robinson, J. Webber, E. Eifrem: Graph databases, O' Reilly, 2013.
S. Ryza, U. Laserson, S Owen, J. Wills: Advanced analytics with Spark, O'Reilly, 2015.
R. Schutt, C. O'Neil: Doing Data Science, O' Reilly, 2014.
C.A. Varela, G. Agha: Programming distributed computing systems: a foundational approach, The MIT Press, 2013.
Last Update
22-01-2020
