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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EBT 6022 BIG DATA TECHNOLOGIES AND APPLICATIONS IN EDUCATION ELECTIVE 3 0 0 8

Offered By

Educational Technologies

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

Offered to

Educational Technologies

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, NoSQ 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 15 Final exam

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

Face -to- Face and applications

Assessment Methods

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

Further Notes About Assessment Methods

None

Assessment Criteria

Homeworks and presentations, discussions

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

PROF.DR. AYLIN ALIN FEN FAKÜLTESI ISTATISTIK BÖLÜMÜ ISTATISTIK TEORISI ANABILIM DALI aylin.alin@deu.edu.tr +90 232 - 3018572 - 18572

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 13 6 78 Preparation for midterm exam 1 5 5 Preparation for final exam 1 5 5 Preparing assignments 1 30 30 Preparing report 1 40 40 Final 1 2 2 Midterm 1 2 2 TOTAL WORKLOAD (hours) 201

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 LO.1 1 1 3 5 1 1 2 2 LO.2 1 2 4 5 4 2 2 2 LO.3 1 2 4 5 5 2 2 2 LO.4 2 2 4 5 4 2 2 2 LO.5 4 2 4 5 4 2 3 3