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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS BLP 4230 DATA SCIENCE AND ANALYSIS ELECTIVE 3 0 0 3

Offered By

Computer Programming

Level of Course Unit

Short Cycle Programmes (Associate's Degree)

Course Coordinator

DOCTOR ÖZER KESTANE

Offered to

Computer Programming (Evening) Computer Programming

Course Objective

In this class, data science environment and appliction creating, coding, designing, searching ability will be given to the student.

Learning Outcomes of the Course Unit

1   Knows Statistical main data science and analysis metods. 2   Knows the basic features of image processing using data, can develop applications. 3   Know web framwork development tools and features for data science systems. 4   Develop applications that use data storage methods for web frameworks and image processing systems. 5   Know machine learning usage of data science operations.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Intoduction to Python programming language and data science 2 Decision and Loop structures, dictionaries, functions 3 Data structures, analysis, classes and objects 4 File operations, numpy , pandas libraries 5 Data visualization, matplotlib, scipy libraries 6 Statistical Data Analysis 7 Statistical Data Analysis 8 Midterm 9 Introduction to OpenCV and Image processing 10 Django Framework and web page (site) creation 11 GUI Thinker, PYQT usage 12 Database and ve sqlite usage 13 Machine Learning ,supervised and unsupervised learning, 14 Neural networks and other application examples. 15 Neural networks and other application examples.

Recomended or Required Reading

Textbook(s): Python ile Veri Bilimi Ilker Arslan Supplementary Book(s):: Lecturer's notes References: Materials:

Planned Learning Activities and Teaching Methods

Lectures Case Study

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 RO Rapor/Ödev 3 FN Final 4 FCG FINAL COURSE GRADE VZ*0.20 + RO*0.20 + FN* 0.60 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) VZ*0.20 + RO*0.20 + BUT* 0.60

Further Notes About Assessment Methods

None

Assessment Criteria

Mid-term exam,final exams and project are measuring learning outcomes in-class applications and this is the stage of the student to achieve the learning outcomes will be monitored.

Language of Instruction

Turkish

Course Policies and Rules

70% of the classes is compulsory to attend. Disciplinary investigation will be concluded with the opening of any act of dishonesty.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 2 28 Tutorials 14 1 14 Preparations before/after weekly lectures 14 1 14 Preparation for midterm exam 1 12 12 Preparation for final exam 1 12 12 Final 1 1 1 Midterm 1 1 1 TOTAL WORKLOAD (hours) 82

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 PO.12 PO.13 PO.14 PO.15 LO.1 5 2 4 3 4 2 LO.2 5 2 4 3 4 2 LO.3 5 2 4 3 4 2 LO.4 5 2 4 3 4 2 LO.5 5 2 4 3 4 2