Introduction to Data EngineeringLaajuus (5 cr)
Code: TT00CN68
Credits
5 op
Objective
After completing the course the student is able to:
Understand and describe the data engineering process life cycle
Content
What is Data Engineering
Data Storage and Retrieval
Data Engineering Lifecycle
Extract, Transform and Load (ETL) process
Introduction to Big Data Frameworks
Enrollment
01.06.2024 - 09.09.2024
Timing
02.09.2024 - 15.12.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Engineering and Business
Campus
Kupittaa Campus
Teaching languages
- English
Seats
30 - 65
Teachers
- Golnaz Sahebi
Scheduling groups
- Subgroup 1 (Size: 35. Open UAS: 0.)
- Subgroup 2 (Size: 35. Open UAS: 0.)
Groups
-
PTIETS23deaiData Engineering and Artificial Intelligence
-
PTIVIS23IData Engineering and Artificial Intelligence
Small groups
- Subgroup 1
- Subgroup 2
Objective
After completing the course the student is able to:
Understand and describe the data engineering process life cycle
Content
What is Data Engineering
Data Storage and Retrieval
Data Engineering Lifecycle
Extract, Transform and Load (ETL) process
Introduction to Big Data Frameworks
Materials
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.
Teaching methods
- 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
Exam schedules
No Exam.
There is a final teamwork project where students must demonstrate their work during a presentation event in week 48.
International connections
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.
Student workload
- 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)
Content scheduling
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.
Further information
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.
Evaluation scale
H-5
Assessment methods and criteria
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)
Assessment criteria, fail (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.
Assessment criteria, satisfactory (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.
Assessment criteria, good (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.
Assessment criteria, excellent (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.
Enrollment
01.06.2024 - 09.09.2024
Timing
02.09.2024 - 15.12.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Engineering and Business
Campus
Kupittaa Campus
Teaching languages
- Finnish
- English
Seats
0 - 35
Teachers
- Golnaz Sahebi
Teacher in charge
Golnaz Sahebi
Groups
-
PTIVIS22HHealth Technology
Objective
After completing the course the student is able to:
Understand and describe the data engineering process life cycle
Content
What is Data Engineering
Data Storage and Retrieval
Data Engineering Lifecycle
Extract, Transform and Load (ETL) process
Introduction to Big Data Frameworks
Materials
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.
Teaching methods
- 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.
Exam schedules
No Exam.
There is a final teamwork project where students must demonstrate their work during a presentation event in week 48.
International connections
- 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.
Student workload
- 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)
Content scheduling
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, 8 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.
Further information
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.
Evaluation scale
H-5
Assessment methods and criteria
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)
Assessment criteria, fail (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.
Assessment criteria, satisfactory (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.
Assessment criteria, good (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.
Assessment criteria, excellent (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.
Enrollment
01.06.2023 - 14.09.2023
Timing
04.09.2023 - 15.12.2023
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Engineering and Business
Campus
Kupittaa Campus
Teaching languages
- English
Seats
25 - 35
Teachers
- Golnaz Sahebi
Groups
-
PTIETS22deaiPTIETS22 Data Engineering and Artificial Intelligence
-
PTIVIS22IData Engineering and AI
Objective
After completing the course the student is able to:
Understand and describe the data engineering process life cycle
Content
What is Data Engineering
Data Storage and Retrieval
Data Engineering Lifecycle
Extract, Transform and Load (ETL) process
Introduction to Big Data Frameworks
Materials
Material will be available via the learning environment (ITS).
Teaching methods
Weekly contact sessions when 3-4 hours for theory and practical exercises.
Additionally, there is home work exercises.
International connections
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.
Student workload
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
Content scheduling
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
Further information
ITS.
Evaluation scale
H-5
Assessment methods and criteria
The course is graded on a scale of 0-5.
*
You can achieve a maximum of 60 points from six 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.
Assessment criteria, fail (0)
Less than 50 points in exercises and project not passed.
Assessment criteria, satisfactory (1-2)
50 - 69 points from the total points of the exercises and the final project
Assessment criteria, good (3-4)
70 - 89 points from the total points of the exercises and the final project
Assessment criteria, excellent (5)
90 - 100 points from the total points of the exercises and the final project