COURSE UNIT TITLE

: DATA PREPROCESSING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
FSH 0054 DATA PREPROCESSING ELECTIVE 2 0 0 2

Offered By

Faculty Of Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR NESLIHAN DEMIREL

Offered to

Biology
Chemistry
Computer Science
Statistics
Mathematics
Physics

Course Objective

The aim of this course is to give the applications of data preprocessing, which is a basic and preliminary step for performing data analysis properly and obtaining correct information.

Learning Outcomes of the Course Unit

1   Being able to describe data and data types
2   Being able to obtain descritive statistics
3   Being able to visualize by data types
4   Being able to impute missing data
5   Being able to clean outliers and noise
6   Being able to convert data
7   Being able to reduce data

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Course introduction: Basic concepts
2 Data and types of data
3 Calculate and interpret descriptive statistics
4 Visualize by data types
5 Imputation of missing values
6 Detection of outliers and remove noise
7 Perform analysis using Statistical software
8 Midterm Exam
9 Data conversion-Normality check
10 Data conversion-Normalization-Standardization
11 Data Reduction
12 Perform analysis using Statistical software
13 Project Presentation
14 Project Presentation

Recomended or Required Reading

Main Reference: Cebeci, Zeynel (2020). Veri Biliminde R ile Veri Önişleme.
Auxiliary references:
Toktamış, Öniz & Türkan, Semra (2017). R Programı Istatistiğe Giriş.
Toktamış, Öniz & Türkan, Semra (2017). R Programı Ile Temel Istatistiksel Yöntem

Planned Learning Activities and Teaching Methods

1. Lecturing
2. Question-Answer
3. Discussion
4. Project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRJ PROJECT
3 FIN FINAL EXAM
4 FCGR FINAL COURSE GRADE (RESIT) VZ * 0.30 + PRJ * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) VZ * 0.30 + PRJ * 0.30 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

Midterm, Project and final

Language of Instruction

Turkish

Course Policies and Rules

1. It is obligated to attend to at least 70% of lessons .
2. Every trial to copying will be finalized with disciplinary proceedings.
The instructor has right to make practical quizzes. The scores obtained from quizzes will be directly added to exam scores.

You can obtain the regulation on the teaching and examination application principles of the D.E.U. Faculty of Science at http://web.deu.edu.tr/fen.

Contact Details for the Lecturer(s)

DEU Faculty of Science, Statistics Department
e-mail: neslihan.ortabas@deu.edu.tr
Phone:+90.232.301 8600

Office Hours

Send an e-mail for a meeting request.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Preparations before/after weekly lectures 10 1 10
Preparation for midterm exam 1 3 3
Preparation for final exam 1 4 4
Project Preparation 1 5 5
Midterm 1 1 1
Final 1 1 1
TOTAL WORKLOAD (hours) 50

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15PO.16PO.17
LO.12
LO.22
LO.32
LO.42
LO.52
LO.62
LO.72

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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