Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
3AIN212Elements of Data Science3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course This course is designed to provide students with a general understanding of the basic concepts and core techniques of data science. Students explore the computational methods and statistical tools to analyze and make sense of data.
Course Content Overview of the data science
Data collection and data management
Visualization and basic statistics
Hypothesis testing and causality
Similarity, neighbors and clusters
Large scale data analysis
Collaborative filtering
Ethical issues in data science
Course Methods and Techniques Lecture, Problem Solving, Preparing and/or Presenting Reports
Prerequisites and co-requisities ( BBM103 ) and ( BBM101 ) and ( AIN214 )
Course Coordinator None
Name of Lecturers Associate Prof. Nazlı İkizler Cinbiş
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Adhikari, A. and DeNero, J. (2019). Computational and Inferential Thinking: The Foundations of Data Science
Course Notes Adhikari, A. and DeNero, J. (2019). Computational and Inferential Thinking: The Foundations of Data Science


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm Exam 1 % 30
Attendance 1 % 5
Project 1 % 25
Final examination 1 % 40
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 4 56
Project 1 45 45
Preparation for Midterm Exam 1 15 15
General Exam Preparation 1 20 20
Total Work Load   Number of ECTS Credits 5,93333333333333 178

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Upon completing the course, students are able to: utilize tools to collect, clean and visualize data; employ data management techniques to effectively access, manipulate and store data; apply statistical methods to make predictions based on data; comm
2  
3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Overview of data science
2 Database programming with Python
3 Exploratory data analysis and visualization
4 Introduction to statistical inference
5 Models, hypothesis testing
6 A/B Testing, causality
7 Regression analysis
8 Midterm exam
9 Similarity, nearest neighbors
10 K-Means clustering
11 Dimensionality reduction
12 Large-scale data analysis: Databases, SQL, mapReduce, Spark and Hadoop
13 Collaborative filtering
14 Ethical issues in data science
15 Final exam preparation
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 2 4 5 5 4 4 4 4 3 5
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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