SOFTWARE ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
SEN4018	Data Science with Python	Spring	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi BETÜL ERDOĞDU ŞAKAR
Recommended Optional Program Components:	None
Course Objectives:	The aim of this course is teaching students to extract knowledge from data. Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data analysis, predictive modeling, descriptive modeling and evaluation. The focus in the treatment of these topics will be on breadth, rather than depth, and emphasis will be placed on integration and synthesis of concepts and their application to solving problems. To make the learning contextual, real datasets from a variety of disciplines will be used with Python.

Learning Outcomes

The students who have succeeded in this course;
1) Understand and use data exploration techniques.
2) Understand and apply data pre-processing, transformation, normalization and standardization.
3) Interpret data and visualize it accordingly.
4) Interpret and use different supervised and unsupervised learning algorithms
5) Use and understand different supervised and unsupervised learning evaluation method.
6) Demonstrates the ability to use libraries prepared for Python when developing applications.
7) Conduct independent, limited data collection, analysis, and evaluation according to established engineering principles in accordance with current research ethical standards.

Course Content

Introduction and Programming Review with Python
Arrays, Matrices, Mathematical Functions with Numpy
Data Manipulation and Analysis with Pandas
Data Pre-Processing, Transformation, Normalization, Standardization
Data Visualization
Supervised Learning - Regression
Supervised Learning - Classification
Cross Validation
Evaluation Metrics
Unsupervised Learning

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction and Programming Review with Python
2)	Arrays, Matrices, Mathematical Functions with Numpy
3)	Data Manipulation and Analysis with Pandas
4)	Data Pre-Processing, Transformation, Normalization, Standardization
5)	Data Visualization
6)	Supervised Learning - Regression
7)	Supervised Learning - Regression
8)	Supervised Learning - Regression
9)	Supervised Learning - Classification
10)	Supervised Learning - Classification
11)	Supervised Learning - Classification
12)	Cross Validation
13)	Evaluation Metrics
14)	Unsupervised Learning

Sources

Course Notes / Textbooks:	Ethem Alpaydın, Introduction To Machine Learning, 3rd Edition, MIT Press, 2015, ISBN-13: 978-8120350786.
References:	Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining, 1st Edition, Pearson, 2005, ISBN-13: 978-0321321367. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006, ISBN-13: 978-0387310732.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Quizzes	6	% 25
Project	1	% 20
Midterms	1	% 15
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	2	28
Project	6	3	18
Quizzes	6	3	18
Midterms	6	3	18
Final	6	3	18
Total Workload			142

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Be able to specify functional and non-functional attributes of software projects, processes and products.
2)	Be able to design software architecture, components, interfaces and subcomponents of a system for complex engineering problems.
3)	Be able to develop a complex software system with in terms of code development, verification, testing and debugging.
4)	Be able to verify software by testing its program behavior through expected results for a complex engineering problem.
5)	Be able to maintain a complex software system due to working environment changes, new user demands and software errors that occur during operation.
6)	Be able to monitor and control changes in the complex software system, to integrate the software with other systems, and to plan and manage new releases systematically.
7)	Be able to identify, evaluate, measure, manage and apply complex software system life cycle processes in software development by working within and interdisciplinary teams.
8)	Be able to use various tools and methods to collect software requirements, design, develop, test and maintain software under realistic constraints and conditions in complex engineering problems.
9)	Be able to define basic quality metrics, apply software life cycle processes, measure software quality, identify quality model characteristics, apply standards and be able to use them to analyze, design, develop, verify and test complex software system.
10)	Be able to gain technical information about other disciplines such as sustainable development that have common boundaries with software engineering such as mathematics, science, computer engineering, industrial engineering, systems engineering, economics, management and be able to create innovative ideas in entrepreneurship activities.
11)	Be able to grasp software engineering culture and concept of ethics and have the basic information of applying them in the software engineering and learn and successfully apply necessary technical skills through professional life.
12)	Be able to write active reports using foreign languages and Turkish, understand written reports, prepare design and production reports, make effective presentations, give clear and understandable instructions.
13)	Be able to have knowledge about the effects of engineering applications on health, environment and security in universal and societal dimensions and the problems of engineering in the era and the legal consequences of engineering solutions.