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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS YBS 6011 BIG DATA MANAGEMENT ELECTIVE 3 0 0 7

Offered By

Management Information Systems

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR CAN AYDIN

Offered to

Management Information Systems

Course Objective

This course aims at sharing of big data visualization, classification and analysis to provide for the necessary methods and technologies.

Learning Outcomes of the Course Unit

1   Web-based application development with a NoSQL. 2   Hadoop, to create queries using Hive and Shark. 3   visualization of large data sets to investigate assessment strategies. 4   Create data processing business lines using the MapReduce model and data conversion workflows. 5   analyze large data using R.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction 2 Big Data Hosting and Sharing 3 Non-Relational Databases 4 Big Data Processing 5 Using Hadoop 6 Data Panel Development 7 Big Data Visualization 8 Mid-Term 9 MapReduce Model 10 Data Conversion Workflows 11 Classification of data with Mahout 12 Statistical Analysis with R 13 Analysis Workflow Development 14 Analysis Workflow Development 15 Analysis Workflow Development

Recomended or Required Reading

1. Data Just Right: Introduction to Large-Scale Data & Analytics , M. Manoochehri, Addison-Wesley, 2013

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 PRJ PROJECT 3 PRS PRESENTATION 4 FIN FINAL EXAM 5 FCG FINAL COURSE GRADE MTE* 0.20 + PRJ* 0.20 + PRS* 0.20 + FIN* 0.40 6 RST RESIT 7 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + PRJ * 0.20 + PRS * 0.20 + RST* 0.40

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

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 4 52 Preparation for midterm exam 1 10 10 Preparation for final exam 1 10 10 Preparing assignments 1 30 30 Preparing presentations 1 40 40 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 183

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 PO.9 LO.1 5 3 3 4 LO.2 5 3 3 4 LO.3 5 3 3 4 LO.4 5 3 3 4 LO.5 5 3 3 4