Introduction to Data EngineeringLaajuus (5 op)
Tunnus: TT00CN68
Laajuus
5 op
Osaamistavoitteet
After completing the course the student is able to:
Understand and describe the data engineering process life cycle
Sisältö
What is Data Engineering
Data Storage and Retrieval
Data Engineering Lifecycle
Extract, Transform and Load (ETL) process
Introduction to Big Data Frameworks
Ilmoittautumisaika
01.06.2024 - 09.09.2024
Ajoitus
02.09.2024 - 15.12.2024
Opintopistemäärä
5 op
Toteutustapa
Lähiopetus
Yksikkö
Tekniikka ja liiketoiminta
Toimipiste
Kupittaan kampus
Opetuskielet
- Englanti
Paikat
30 - 65
Opettaja
- Golnaz Sahebi
Ajoitusryhmät
- Subgroup 1 (Koko: 35. Avoin AMK: 0.)
- Subgroup 2 (Koko: 35. Avoin AMK: 0.)
Ryhmät
-
PTIETS23deaiData Engineering and Artificial Intelligence
-
PTIVIS23IData Engineering and Artificial Intelligence
Pienryhmät
- Subgroup 1
- Subgroup 2
Tavoitteet
After completing the course the student is able to:
Understand and describe the data engineering process life cycle
Sisältö
What is Data Engineering
Data Storage and Retrieval
Data Engineering Lifecycle
Extract, Transform and Load (ETL) process
Introduction to Big Data Frameworks
Oppimateriaalit
The learning materials will be introduced during the first lecture.
We may also use the AWS Academy material for studying some parts of the course.
Opetusmenetelmät
- Participating in lectures (theory and practice)
- Learning through hands-on programming (classwork assignments)
- Completing homework assignments
- Interacting with the teacher and classmates
- Enhancing knowledge through teamwork projects
Tenttien ajankohdat ja uusintamahdollisuudet
No Exam.
There is a final teamwork project where students must demonstrate their work during a presentation event in week 48.
Pedagogiset toimintatavat ja kestävä kehitys
The course includes approximately 12 theory sessions and practice sessions where students work with practical tasks.
Additionally, there are 4 online Q&A sessions for extra support.
Furthermore, exercises for classwork and home work that will be partly demonstrated in during contact sessions.
We may also use a flipped-classroom model for some lectures, where students study the theoretical part at home and engage in practical implementation and discussions in class.
Opiskelijan ajankäyttö ja kuormitus
- Contact teaching:
• We have 12 theory and practice sessions, each lasting 3.5 hours, conducted weekly: 12 x 3.5 = 42
• Additionally, there are 4 online Q&A sessions, each lasting 1 hour.
• Total contact teaching hours per course: 46 hours.
- Homework and teamwork assignment:
• Personal assignments (homework) and independent studies: 69 hours
• Teamwork assignment: 20 hours
Total: approximately 135 hours (5 x 27h)
Sisällön jaksotus
Course Topics (pre-planning):
1. Course Overview and Introduction to Data Engineering
2. The Data Engineering Ecosystem
3. Exercise Demo (I)
4. Apache Airflow
5. Data Engineering Life Cycle - Data wrangling and ETL
6. Data Engineering Life Cycle - Data Wrangling and ETL in Airflow
7. Data Engineering Lifecycle - Governance and Compliance
8: Exercise demo
9. Cloud-based data engineering
10 and 11. working on final projects within groups at classroom
12: Final Project presentations
The course includes approximately 12 supervised practice work and theory sessions.
Additionally, 8 personal exercises for homework that will be partly demonstrate in during contact session.
And some classwork assignment that must be submit during the class hours to demonstrate the participation.
Furthermore, the course has a teamwork project that must be done in a group of 4 students.
Note: Exercise work is done individually outside the instructional sessions. The topic of the assignment is specified during the first month of the course.
Viestintäkanava ja lisätietoja
Prerequisites:
1- Python Programming:
- Basic Syntax and Control Structures
- Functions and Modules
- Error Handling
2- Object-Oriented Programming (OOP) in Python
3- Pandas Library:
- DataFrames and Series
- Data Manipulation: Skills in reading, writing, filtering, and transforming data using Pandas.
4- Databases
Additionally, experience with a Version Control System is recommended.
Arviointiasteikko
H-5
Arviointimenetelmät ja arvioinnin perusteet
1) The course is graded on a scale of 0-5
2) Students can achieve 100 points from this course that contains:
- Participation and classwork submission: participating on each lecture and submitting the related classwork assignment during the class hours 3p => 12 X 3 = 36 points.
- Homework assignments: each homework assignment has 5 points. There are 8 homework assignments => 8 x 5 = 40 points.
- Teamwork assignment: 24 points
Note: the teamwork assignment will be graded on scale 0-5 on Itslearning.
3) Evaluation:
50% of total to pass: 50% from participation and classwork + 50% from homework assignments + 50% from the teamwork projects to pass
Note: Grades will be rounded down if they include decimals less than 0.5; otherwise, they will be rounded up. (e.g., 3.4 is rounded down to 3.0, but 3.5 or higher is rounded up to 4.0)
Hylätty (0)
The student does NOT get at least 50% of the points in teamwork assignment OR does not get at least 50% of the points in the homework assignments OR does not get at least 50% of the points in participation and classwork submission.
Arviointikriteerit, tyydyttävä (1-2)
The student got 50-59% of the points for the homework assignments AND got 50-59% of the points for the participation and classwork assignments submission AND got a grade of 1 - 3 for the teamwork assignment.
Arviointikriteerit, hyvä (3-4)
The student got 66-85% of the points for the homework assignments AND got 66-85% of the points for the participation and classwork assignments submission AND got a grade of 4 for the teamwork assignment.
Arviointikriteerit, kiitettävä (5)
The student got at least 86% of the points for the homework assignments AND got at least 86% of the points for the participation and classwork assignments submission AND got a grade of 5 for the teamwork assignment.
Ilmoittautumisaika
01.06.2024 - 09.09.2024
Ajoitus
02.09.2024 - 15.12.2024
Opintopistemäärä
5 op
Toteutustapa
Lähiopetus
Yksikkö
Tekniikka ja liiketoiminta
Toimipiste
Kupittaan kampus
Opetuskielet
- Suomi
- Englanti
Paikat
0 - 35
Opettaja
- Golnaz Sahebi
Vastuuopettaja
Golnaz Sahebi
Ryhmät
-
PTIVIS22HHealth Technology
Tavoitteet
After completing the course the student is able to:
Understand and describe the data engineering process life cycle
Sisältö
What is Data Engineering
Data Storage and Retrieval
Data Engineering Lifecycle
Extract, Transform and Load (ETL) process
Introduction to Big Data Frameworks
Oppimateriaalit
The course materials will be introduced at the first lecture.
We may also use the AWS Academy material for studying some parts of the course.
Opetusmenetelmät
- Participating in lectures (theory and practice)
- Learning through hands-on programming (classwork assignments)
- Completing homework assignments
- Interacting with the teacher and classmates
- Enhancing knowledge through teamwork projects
- Independent studies: some part of theories will be assigned as student's self-study tasks. Then these studies will be utilized during the practical sessions.
Tenttien ajankohdat ja uusintamahdollisuudet
No Exam.
There is a final teamwork project where students must demonstrate their work during a presentation event in week 48.
Pedagogiset toimintatavat ja kestävä kehitys
- The course includes approximately 12 theory sessions and practice sessions where students work with practical tasks.
- Additionally, there are 4 online Q&A sessions for extra support.
- Furthermore, exercises for home work that will be partly demonstrated in during contact sessions.
- A teamwork project will be introduced in the second month, requiring students to apply their teamwork skills and knowledge gained from the course to implement their final project
- We may also utilize a flipped-classroom model for some lectures, where students will study the theoretical part at home and engage in practical implementation and discussions during class.
Opiskelijan ajankäyttö ja kuormitus
- Contact teaching:
• We have 12 theory and practice sessions, each lasting 3 hours, conducted weekly. (36 hours)
• Additionally, there are 4 online Q&A sessions, each lasting 1 hour.
• Total contact teaching hours per course: 40 hours.
- Homework and teamwork assignment:
• Personal assignments (homework) and independent studies: 70 hours
• Teamwork assignment: 25 hours
Total: approximately 135 hours (5 x 27h)
Sisällön jaksotus
Course Topics (pre-planning):
1. Course Overview and Introduction to Data Engineering
2. The Data Engineering Ecosystem
3. Exercise Demo (I)
4. Apache Airflow
5. Data Engineering Life Cycle - Data wrangling and ETL
6. Data Engineering Life Cycle - Data Wrangling and ETL in Airflow
7. Data Engineering Lifecycle - Governance and Compliance
8: Exercise demo
9. working independently on final projects within groups
10: Final Project presentations
The course includes approximately 12 supervised practice work and theory sessions.
Additionally, 10 personal exercises for homework that will be partly demonstrate in during contact session.
Furthermore, the course has a teamwork project that must be done in a group of 4 students.
Exercise work is done individually outside the instructional sessions. The topic of the assignment is specified during the first month of the course.
Viestintäkanava ja lisätietoja
Prerequisites:
1- Python Programming:
- Basic Syntax and Control Structures
- Functions and Modules
- Error Handling
2- Object-Oriented Programming (OOP) in Python
3- Pandas Library:
- DataFrames and Series
- Data Manipulation: Skills in reading, writing, filtering, and transforming data using Pandas.
4- Databases
Additionally, experience with a Version Control System is recommended.
Arviointiasteikko
H-5
Arviointimenetelmät ja arvioinnin perusteet
1) The course is graded on a scale of 0-5
2) Students can achieve 100 points from this course that contains:
- Participation and classwork submission: participating on each lecture and submitting the related classwork assignment during the class hours 3p => 12 X 3 = 36 points.
- Homework assignments: each homework assignment has 5 points. There are 8 homework assignments => 8 x 5 = 40 points.
- Teamwork assignment: 24 points
Note: the teamwork assignment will be graded on scale 0-5 on Itslearning.
3) Evaluation:
50% of total to pass: 50% from participation and classwork + 50% from homework assignments + 50% from the teamwork projects to pass
Note: Grades will be rounded down if they include decimals less than 0.5; otherwise, they will be rounded up. (e.g., 3.4 is rounded down to 3.0, but 3.5 or higher is rounded up to 4.0)
Hylätty (0)
The student did NOT get at least 50% of the points in teamwork assignment OR did not get at least 50% of the points in the homework assignments OR did not get at least 50% of the points in participation and classwork submission.
Arviointikriteerit, tyydyttävä (1-2)
The student got 50-65% of the points for the homework assignments AND got 50-59% of the points for the participation and classwork assignments submission AND got a grade of 1 - 3 for the teamwork assignment.
Arviointikriteerit, hyvä (3-4)
The student got 66-85% of the points for the homework assignments AND got 66-85% of the points for the participation and classwork assignments submission AND got a grade of 4 for the teamwork assignment.
Arviointikriteerit, kiitettävä (5)
The student got at least 86% of the points for the homework assignments AND got at least 86% of the points for the participation and classwork assignments submission AND got a grade of 5 for the teamwork assignment.
Ilmoittautumisaika
01.06.2023 - 14.09.2023
Ajoitus
04.09.2023 - 15.12.2023
Opintopistemäärä
5 op
Toteutustapa
Lähiopetus
Yksikkö
Tekniikka ja liiketoiminta
Toimipiste
Kupittaan kampus
Opetuskielet
- Englanti
Paikat
25 - 35
Opettaja
- Golnaz Sahebi
Ryhmät
-
PTIETS22deaiPTIETS22 Datatekniikka ja Tekoäly
-
PTIVIS22IData Engineering and AI
Tavoitteet
After completing the course the student is able to:
Understand and describe the data engineering process life cycle
Sisältö
What is Data Engineering
Data Storage and Retrieval
Data Engineering Lifecycle
Extract, Transform and Load (ETL) process
Introduction to Big Data Frameworks
Oppimateriaalit
Material will be available via the learning environment (ITS).
Opetusmenetelmät
Weekly contact sessions when 3-4 hours for theory and practical exercises.
Additionally, there is home work exercises.
Pedagogiset toimintatavat ja kestävä kehitys
The course includes approximately 11 theory sessions and guided exercises sessions where students work with practical tasks.
Additionally, exercises for home work that will be partly demonstrated in during contact sessions.
Opiskelijan ajankäyttö ja kuormitus
Contact hours
- 10 times 3.5h theory and practice: 10 x 3.5h = 35 hours
- Final projects and presentations: 25 hours
Home work: approximately 70 hours
Total: approximately: 130 hours
Sisällön jaksotus
Course Topics and Scheduling (pre-planning):
Week 36: Course Overview and Introduction to Data Engineering
Week 37 - 38: The Data Engineering Ecosystem
Week 39: Big Data Platforms
Week 40: Exercise Demo (I)
Week 41: Week 41: Apache Airflow
Week 43: Data Engineering Life Cycle - Data wrangling
Week 44: Data Engineering Life Cycle - Data Wrangling and ETL in Airflow
Week 45: Data Engineering Lifecycle - Governance and Compliance
Week 46 and 47: Exercise demo and working independently on your final projects within your groups
Week 48: Final Project presentations
Viestintäkanava ja lisätietoja
ITS.
Arviointiasteikko
H-5
Arviointimenetelmät ja arvioinnin perusteet
The course is graded on a scale of 0-5.
*
You can achieve a maximum of 60 points from practical exercises in class room and homework exercises, and a maximum of 40 points from the final project.
*
To pass the course, you need to achieve at least 30 points of the exercises and 20 points of the final project.
Hylätty (0)
Less than 50 points in exercises and project not passed (less than 45% points).
To pass the course, you need to achieve at least 30 points of the exercises and 20 points of the final project.
Arviointikriteerit, tyydyttävä (1-2)
50 - 69 points from the total points of the exercises and the final project
Arviointikriteerit, hyvä (3-4)
70 - 89 points from the total points of the exercises and the final project
Arviointikriteerit, kiitettävä (5)
90 - 100 points from the total points of the exercises and the final project