Information Package / Course Catalogue
Data Science and Machine Learning With Python
Course Code: UTIF415
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 5
Objectives of the Course

The aim of the course is to provide students with an introduction to data science and machine learning using the Python programming language. Students will learn basic Python commands and topics that provide an introduction to data science and machine learning with the Python programming language. The course will help students understand the use of basic Python commands and how to apply these commands to data science and machine learning. The basic concepts of data science and the working principles of machine learning algorithms will be explained to the students. In addition, students will also learn basic data science and machine learning concepts such as extracting summary statistics from datasets, summarizing and visualizing data, testing arrangements and reliability in datasets, developing, training and predicting machine learning models using Python.

Course Content

"Data Science and Machine Learning with Python" is designed as a course that covers the fundamentals of data science and machine learning. Students will receive extensive training in the Python programming language, which can be used in data science, machine learning and deep learning. The course will include teaching the basic concepts of the Python programming language and will cover the use of specialized libraries in Python that can be used for data science and machine learning. Within the scope of the course, topics such as data visualization, data exploration, feature selection, prediction models, and model performance evaluation will be covered, among the core areas of data science using Python. In addition, information about Python libraries used in deep learning and machine learning are also given.

Name of Lecturer(s)
Assoc. Prof. Sadullah ÇELİK
Learning Outcomes
1.Students will learn the necessary coding to develop data science and machine learning models with the help of Python language.
2.Students will have the ability to identify and solve data scientific and machine learning problems.
3.Students will learn to measure the performance of data science and machine learning models with the help of the Python language.
4.Students will learn how to make the outputs of data scientific and machine learning models understandable and understandable with the help of Python language.
5.Students will learn the necessary preprocessing and feature clustering techniques to improve the prediction performance of data scientific and machine learning models with the help of Python language.
Recommended or Required Reading
1.Ramalho, L. (2022). Fluent python. " O'Reilly Media, Inc.".
2.Haslwanter, T. (2016). An Introduction to Statistics with Python. With Applications in the Life Sciences.. Switzerland: Springer International Publishing.
Weekly Detailed Course Contents
Week 1 - Theoretical
Overview of Data Science and Machine Learning. Python basics.
Week 2 - Theoretical
Basic Data Classes and Operators.
Week 3 - Theoretical
Data Structures and Programming Control Structures.
Week 4 - Theoretical
Data Visualization: Matplotlib, Seaborn, ggplot.
Week 5 - Theoretical
Data preprocessing: Pandas, Numpy, Scikit-Learn.
Week 6 - Theoretical
Linear Regression.
Week 7 - Theoretical
K-Nearest Neighbor Algorithm.
Week 8 - Theoretical
Midterm Exam
Week 9 - Theoretical
Decision trees and Random Forests.
Week 10 - Theoretical
Artificial neural networks.
Week 11 - Theoretical
Deep Learning.
Week 12 - Theoretical
Machine Learning Applications.
Week 13 - Theoretical
Advanced Applications of Machine Learning.
Week 14 - Theoretical
Data Science and Machine Learning Projects.
Week 15 - Theoretical
Project Presentation and Evaluation
Week 16 - Theoretical
Final Exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory153390
Assignment2228
Individual Work33215
Midterm Examination1134
Final Examination1134
TOTAL WORKLOAD (hours)121
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
OÇ-1
1
2
1
5
1
1
1
2
4
2
OÇ-2
1
2
1
5
1
1
1
2
4
2
OÇ-3
1
2
1
5
1
1
1
2
4
2
OÇ-4
1
2
1
5
1
1
1
2
4
2
OÇ-5
1
2
1
5
1
1
1
2
4
2
Adnan Menderes University - Information Package / Course Catalogue
2026