Subject

Introductory Topics in Data Science

Course Title

Introductory Topics in Data Science
Introductory topics for data science

Code

Digital Signal 0

Study Programme

Data science in computer science and engineering

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

Second cycle

Academic year / semester

9 / Winter

Number of ECTS credits

Teacher

Ivan Chorbev, Petre Lameski, Riste Stojanov

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

Within this course, students will be introduced to the principles of formulating and solving problems related to data science, leading teams of scientists and engineers, designing systems and products, and communicating with clients and non-technical audiences. Students will also develop the ability to write program code in the Python programming language, as well as the ability to apply programming in data science.

11.

Course content

Fundamentals of Programming
Python programming language
Python libraries relevant to data science
Practical examples
Processes in projects that adopt data science
Leading teams in projects that apply data science
Communication with clients and businesses
Project Architecture from Data Science
User scenarios and examples from practice

12.

Learning methods

Presentations, class discussions, reviews of real-world examples and case studies, and live coding.

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

90 + 30 + 15 + 15 + 30 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	90 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	30 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	30 hours

17.

Assessment method

17.1.	Tests	100 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	0 points
17.4.	Final exam	0 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

NULL

20.

Language of instruction

English

21.

Method for monitoring the quality of teaching

internal evaluation and surveys

22.

Literature

22.1.	Required literature 1. Jake VanderPlas \| Python Data Science Handbook \| O'REILLY \| 2016 2. Foster Provost and Tom Fawcett \| Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking \| O'Reilly Media \| 2013 3. John W. Creswell and J. David Creswell \| Research Design: Qualitative, Quantitative, and Mixed Methods Approaches \| SAGE Publications, Inc \| 2017
22.2.	Additional literature No. Author Title Publisher Year