Introduction to Data Engineering and AI Technologies (5 cr)
Code: MS00CN43-3002
General information
- Enrollment
-
02.07.2024 - 16.09.2024
Registration for the implementation has ended.
- Timing
-
16.09.2024 - 31.12.2024
Implementation has ended.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- Engineering and Business
- Teaching languages
- Finnish
- English
- Degree programmes
- Master of Engineering, Data Engineering and AI
- Master of Business Administration, Data Engineering and AI
- Master of Business Administration, Interactive Technologies
- Teachers
- Golnaz Sahebi
- Course
- MS00CN43
Realization has 4 reservations. Total duration of reservations is 13 h 0 min.
| Time | Topic | Location |
|---|---|---|
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Mon 16.09.2024 time 08:15 - 11:30 (3 h 15 min) |
Introduction to Data Engineering and AI Technologies MS00CN43-3002 |
EDU_2027
Frans muunto byod
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Tue 08.10.2024 time 08:15 - 11:30 (3 h 15 min) |
Introduction to Data Engineering and AI Technologies MS00CN43-3002 |
EDU_1001
Dromberg Esitystila byod
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|
Mon 04.11.2024 time 08:15 - 11:30 (3 h 15 min) |
Introduction to Data Engineering and AI Technologies MS00CN43-3002 |
EDU_4071
Teoriatila muunto byod
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|
Mon 02.12.2024 time 08:15 - 11:30 (3 h 15 min) |
Introduction to Data Engineering and AI Technologies MS00CN43-3002 |
EDU_4071
Teoriatila muunto byod
|
Evaluation scale
H-5
Content scheduling
Course Outline and Schedule: (preplan)
Session 1: Introduction to the Course, Data, and Data Preprocessing:
• Project overview and requisites
• Initiation of project's initial phase: Data preprocessing and visualization (assignment 1)
Session 2: Machine Learning I
• Students' presentations showcasing outputs and implementation of the first assignment
• Commencement of phase two: Supervised and unsupervised learning (assignment 2)
Session 3: Machine Learning II
• Students' presentations showcasing outputs and implementation of the second assignment
• Tuning and optimizing your Machine Learning algorithms (assignment 3)
Session 4: Students' presentations showcasing outputs and implementation of the third assignment, followed by discussions encompassing all project phases.
Objective
After completing the course, the student can:
- describe basic concepts and processes related to Data Engineering and AI
Content
- Data Engineering process
- Basics of AI
- Fields and evolution of AI
- Big data
- Basics of Machine Learning
Materials
Course book:
Aurélien Géron.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
2nd Edition.
Publisher : O'Reilly Media; 2nd edition
(October 15, 2019)
The course book can be read in electronic form from our institution's eBook Central database.
Additionally, some other materials will be available via the learning environment (ITS).
Teaching methods
- Short lectures are delivered by the teacher (theory and practice)
- Self-study tasks (theory and practice)
- Practical classwork
- Teamwork Project (including three assignments)
Exam schedules
No exam
Student workload
Contact hours:
- 4 times 3h theory and practice: 4 x 4h = 12 hours
Assignments for the final project: approximately 118 hours
Total: approximately: 130 hours
Further information
Qualifications:
Before taking an "Introduction to Data Engineering with Python" course, students typically need a foundational understanding of several key areas. Here are the prerequisite courses and topics:
1. Python Programming: Proficiency in basic Python syntax and programming constructs, understanding of Object-Oriented Programming (OOP) concepts.
2. Data Management: Experience with data manipulation libraries such as Pandas for handling datasets. Data manipulation involves transforming data, cleaning it, organizing it, and preparing it for analysis.
3. Basic Linear Algebra: Understanding of vectors, matrices, and basic operations on them.
4. Statistics and Probability: Knowledge of descriptive statistics (mean, median, mode, variance, etc.), and familiarity with probability distributions.