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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EBT 6029 INSTRUCTIONAL DATA PROCESSING AND MANAGEMENT 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 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 15 Final

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

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 40 40 Preparing report 1 30 30 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 4 2 2 2 LO.4 2 2 4 5 4 2 2 2