At the end of this course, the students; 1) Learn big data concepts, terminology, data analytics features, big data types. 2) Comprehend analysis techniques such as qualitative and quantitative data mining, statistical analysis, A/B testing, correlation, regression analysis. 3) Master storage concepts such as clustering, distributed file systems, relational database systems, in-memory storage and big data processing concepts such as parallel, distributed, batch data processing.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
Big data in general terms refers to data that is structured, semi-structured or unstructured, requires large storage areas, and is difficult to process in reasonable times with known software tools. The aim of this course is to introduce the basic concepts and methods in big data analysis and data science and to enable students to understand the basic features of the use of "Big Data" in the real world.
COURSE CONTENTS
WEEK
TOPICS
1st Week
What is big data?
2nd Week
Big data and open data: key concepts
3rd Week
Big data technologies and tools
4th Week
Data mining with big data
5th Week
Big data security - privacy
6th Week
Data visualization, communication and machine learning
7th Week
Big data analysis and method
8th Week
Midterm
9th Week
Cyber security
10th Week
Big data political and legal framework in Turkey
11th Week
Big data use in public-private organizations - examples
12th Week
Big data and applications in the mobile communications industry
13th Week
Social media analytics data
14th Week
Social media analytics techniques and their applications
RECOMENDED OR REQUIRED READING
1. Big Data Fundamentals: Concepts, Drivers & Techniques (1st ed.). Thomas Erl, Wajid Khattak, and Paul Buhler. Prentice Hall Press, Upper Saddle River, NJ, USA. 2016. 2. Big Data, Principles and Best Practices of Scalable Realtime Data Systems, Nathan Marz and James Warren, Manning Publications 2015.
