COURSE UNIT TITLE

: DATA SCIENCE AND ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BLP 4130 DATA SCIENCE AND ANALYSIS COMPULSORY 2 1 0 5

Offered By

Computer Programming

Level of Course Unit

Short Cycle Programmes (Associate's Degree)

Course Coordinator

DOCTOR ÖZER KESTANE

Offered to

Computer Programming
Computer Programming (Evening)

Course Objective

With this course, the student: Data science, analysis and data processing competencies will be gained.

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): Veri Bilimi için Python - KODLAB
Supplementary Book(s): Projeler ile Python - KODLAB
Python Programming Fundamentals, Kent D. Lee.
https://www.learnpython.org
https://docs.python.org/3/reference/index.html
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 FN Final
3 FCG FINAL COURSE GRADE VZ*0.40 + FN* 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) VZ*0.40 + BUT* 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

Mid-term exam and final exam to measure learning outcomes, and in-class applications with the five outcomes that will be followed in reaching the stage of the student.

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)

Ph.D. Özer Kestane
Telefon: +90 232 301 26 21
E-posta:ozer.kestane@deu.edu.tr

Office Hours

Wednesday 16:00-17:00

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 3 42
Preparation for midterm exam 1 14 14
Preparation for final exam 1 28 28
Final 1 1 1
Midterm 1 1 1
TOTAL WORKLOAD (hours) 128

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15
LO.1524342
LO.2524342
LO.3524342
LO.4524342
LO.5524342

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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