1	Course Title:	DATA SCIENCE BY PYTHON
2	Course Code:	IYZ4218
3	Type of Course:	Compulsory
4	Level of Course:	First Cycle
5	Year of Study:	4
6	Semester:	8
7	ECTS Credits Allocated:	3
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Doç. Dr. MELİH ENGİN
16	Course Lecturers:	Doç.Dr. Melih ENGİN
17	Contactinformation of the Course Coordinator:	Doç.Dr. Melih ENGİN melihengin@uludag.edu.tr Uludağ Üniversitesi İnegöl İşletme Fakültesi İnegöl Yerleşkesi Cerrah Yolu 16400 İnegöl /BURSA TÜRKİYE 0224 294 26 95
18	Website:
19	Objective of the Course:	Getting to know different platforms with Python, doing basic coding with Python and developing data science applications
20	Contribution of the Course to Professional Development	Python Programming Language; Python data types; Python data entry; Regression applications with Python; Python discrete variables and tests; Logit with Python; Bayesian statistics with Python; Applications

21

Learning Outcomes:

1 Makes Python installation; 2 Recognize platform running Python; 3 Python uses the basic commands; 4 Create projects Python; 5 Develops Data Science Applications with Python;

22	Course Content:

Week	Theoretical	Practical
1	Python Introduction
2	Python Installation
3	Python variables
4	Python print command strings
5	Save and Run Project
6	security
7	Series
8	Decision Tree
9	Logistic Regression
10	Support Vector Machines
11	K-Nearlest Neighbor
12	Hierarchical Clustering
13	Hidden Markov Model
14	The project implementation

23	Textbooks, References and/or Other Materials:	“An Introduction to Statistics with Python”, Thomas Halswater, Springer-Verlag, 2016. Think Stats 2e Allen B. Downey Ders Notları
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	40
Quiz	0	0
Homeworks, Performances	0	0
Final Exam	1	60
Total	2	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	Relative Evaluation
Information

25

ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	3	42
Practicals/Labs	0	0	0
Self Study and Preparation	0	0	0
Homeworks, Performances	0	0	0
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	20	20
Others	0	0	0
Final Exams	1	35	35
Total WorkLoad			97
Total workload/ 30 hr			3,23
ECTS Credit of the Course			3

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 LO1 0 0 0 2 0 0 0 0 0 0 0 LO2 0 0 0 2 0 0 0 0 0 0 0 LO3 0 0 0 2 0 0 0 0 0 0 0 LO4 0 0 0 2 0 0 0 0 0 0 0 LO5 0 5 5 5 5 5 0 0 0 0 0

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High