COURSE UNIT TITLE

: BIG DATA TECHNOLOGIES AND APPLICATIONS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DSM 5013 BIG DATA TECHNOLOGIES AND APPLICATIONS 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 ENGIN YILDIZTEPE

Offered to

Data Science
Data Science (Non-Thesis-Evening)

Course Objective

The objective of this course is to introduce students to the core concepts of Big Data analysis and application

Learning Outcomes of the Course Unit

1   Understand the fundamental concepts, principles, and approaches to the description of the Big Data.
2   Understand the main problems of the Big Data Analysis.
3   Learn and get experience in using some data analysis and management tools such as Hadoop MapReduce, and others.
4   Learn about common algorithmic and statistical techniques used to perform big data analysis.
5   Learn about different types of scenarios and applications in big data analysis, including for structured, semi-structured, and unstructured data.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Big data, Basic terminology, Characteristics of Big data
2 Representing Data, Basics of iPython, NumPy, Pandas. Data abstractions: DataFrames, tables, maps, matrices. Data wrangling, summarization and visualization.
3 Big data Systems and programming, distributed file system, Programming models for big data
4 Introduction to Apache Hadoop, Big data Storage/HDFS
5 Introduction to MapReduce
6 Big data modeling and management
7 Big data modeling and management
8 New alternatives to traditional database systems and access methods, NoSQL
9 Retrieving big data
10 Big data integration tools
11 Processing and analysis of big data using Apache Spark
12 Processing and analysis of big data using Apache Spark
13 Machine Learning with big data
14 Student presentations

Recomended or Required Reading

1. Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive datasets. Cambridge university press.
2. White, T. (2012). Hadoop-The Definitive Guide: Storage and Analysis at Internet Scale
3. Karau, H., Konwinski, A., Wendell, P., & Zaharia, M. (2015). Learning spark: lightning-fast big data analysis. O'Reilly.
Materials: Lecture Slides, Web sources

Planned Learning Activities and Teaching Methods

The course consists of lecture and projects.

Assessment Methods

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


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams and homework/presentation.

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)

DEU Faculty of Science Department of Statistics
e-mail: sedat.capar@deu.edu.tr
phone: +90 232 301 86 01

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 1 14
Preparation for midterm exam 1 40 40
Preparation for final exam 1 45 45
Preparing assignments 1 25 25
Preparing presentations 1 25 25
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 195

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.14544
LO.24544
LO.34544
LO.44544
LO.54544

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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