Data Science and Data Analysis

Објавено: June 28, 2022
1. Course Title Data Science and Data Analysis
2. Code 4ФЕИТ07Л016
3. Study program КТИ
4. Organizer of the study program (unit, institute, department) Faculty of Electrical Engineering and Information Technologies
5. Degree (first, second, third cycle) First cycle
6. Academic year/semester III/6 7. Number of ECTS credits 6
8. Lecturer D-r Hristijan Gjoreski
9. Course Prerequisites
 Passed: Programming and Аlgorithms, Data Structures and Programming
10. Course Goals (acquired competencies): Introduction to the concept of Data Science, statistical data analysis, application of algorithms for Machine Intelligence. With this course, the student will gain knowledge for practical use of tools and software for data processing, model development, evaluation and comparison of results. You will also gain theoretical and practical knowledge with tools for working with Data warehouses and Big Data.
11. Course Syllabus: Introduction to data science and data analysis with machine intelligence algorithms.
Analyzing data sets with different algorithms: Decision trees, K nearest neighbors, Naive Bayes, Random Forests, Ensemble Models.
The analysis includes, pre-processing of the data (filtering), extraction of attributes, building of classification and regression models, clustering of data, visualization of data and models, as well as analysis and comparison of different types of evaluation of built models.
Implementation and evaluation of algorithms and models using the Java and Python environment.
12. Learning methods: Lectures, auditory and laboratory exercises, independent learning, independent work on project tasks
13. Total number of course hours 2 + 2 + 1 + 0
14. Distribution of course hours 180
15. Forms of teaching 15.1. Lectures-theoretical teaching 30
15.2. Exercises (laboratory, practice classes), seminars, teamwork 45
16. Other course activities 16.1. Projects, seminar papers 0
16.2. Individual tasks 45
16.3. Homework and self-learning 60
17. Grading 17.1. Exams 0
17.2. Seminar work/project (presentation: written and oral) 0
17.3. Activity and participation 10
17.4. Final exam 90
18. Grading criteria (points) up to 50 points 5 (five) (F)
from 51to 60 points 6 (six) (E)
from 61to 70 points 7 (seven) (D)
from 71to 80 points 8 (eight) (C)
from 81to 90 points 9 (nine) (B)
from 91to 100 points 10 (ten) (A)
19. Conditions for acquiring teacher’s signature and for taking final exam Practical (laboratory) exercises
20. Forms of assessment Two partial exams during the semester lasting 120 minutes each or one final written exam in an appropriate exam session lasting 120 minutes. Evaluation of laboratory exercises. Possibility to do a seminar work that will be part of the evaluation.
21. Language Macedonian and English
22. Method of monitoring of teaching quality Internal evaluation and surveys.
23. Literature
23.1. Additional Literature
No. Author Title Publisher Year
1 Aurélien Géron Hands-On Machine Learning with Scikit-Learn and TensorFlow O’Reilly Media; 1st edition (April 25, 2017) 2017
2 Ian H. Witten, Eibe Frank, Eibe Frank Data Mining: Practical Machine Learning Tools and Techniques Morgan Kaufmann; 3rd edition (January 20, 2011) 2011