Study Guide

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

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.

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.

Evaluation scale

H-5

Assessment methods and criteria

The course is graded on a scale of 0-5.
*
In order to receive an approved performance and pass the course, the student must receive an acceptable mark for the three assignments.
*
You can get at most 40, 30, and 30 points for each phase of the project (assignments 1-3). You can therefore get a maximum of 100 points from all phases of the project.
*
The passive students of the groups won't earn the assignment points.

Assessment criteria, fail (0)

Less than 50% in assignments not passed.

Assessment criteria, satisfactory (1-2)

50% - 65% from the total points of the assignments

Assessment criteria, good (3-4)

66% - 80% from the total points of the assignments

Assessment criteria, excellent (5)

81%- 100% from the total points of the assignments