COURSE UNIT TITLE

: DATA PROCESSING AND MANAGEMENT

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DSM 5005 DATA PROCESSING AND MANAGEMENT ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR SEDAT ÇAPAR

Offered to

Data Science
Data Science (Non-Thesis-Evening)

Course Objective

The objective of this course is to construct the basis of probability and statistics for analyzing big data sets.

Learning Outcomes of the Course Unit

1   Use data types and structures
2   Design and manage a database
3   Perform fundamental operations on data
4   Use and interpret fundamental statistical analysis and visualization tools

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Data, information/knowledge concept, data types
2 Statistical analysis in R
3 R data types (vector, matrice, array, dataframe, list)
4 R data types (vector, matrice, array, dataframe, list)
5 Data visualization
6 Databases - history, data models, relational data model
7 Normalization process
8 Midterm Exam
9 Database Management Systems
10 SQL - selection query
11 SQL - insert, update, delete queries
12 SQL - JOIN, UNION, trigger, view
13 Big Data
14 MapReduce, Hadoop

Recomended or Required Reading

Main textbooks:
1. Kuan-Ching Li, Hai Jiang, Albert Y. Zomaya (2017). Big Data Management and Processing, Chapman & Hall.
2. Hadley Wickham, Garrett Grolemund. (2017). R for Data Science, O'Reilly Media.
Other materials: Lecture slides, Web sources

Planned Learning Activities and Teaching Methods

The course consists of lecture, homeworks and applications.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE MTE * 0.40 + FIN * 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams, homeworks and presentations.

Language of Instruction

Turkish

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the graduate policy at http://www.fbe.deu.edu.tr/

Contact Details for the Lecturer(s)

Assoc.Prof.Dr. Sedat Çapar
DEU Faculty of Science Deparment of Statistics
e-mail: sedat.capar@deu.edu.tr

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 40 40
Preparation for final exam 1 45 45
Preparing assignments 4 1 4
Preparing presentations 1 15 15
Lab Preparation 1 25 25
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 189

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.155554
LO.255544
LO.3555555
LO.45555545

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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