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
E-Learning
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
Introduction to data mining
2nd Week
Data mining tasks
3rd Week
Data mining tasks
4th Week
Components of data mining
5th Week
Components of data mining
6th Week
Comparison of data mining and the other analytical methods
7th Week
Comparison of data mining and the other analytical methods
8th Week
Midterm
9th Week
Decision processes, business intelligence and data mining
10th Week
Methodology of data mining
11th Week
Classification of data mining methods
12th Week
Statistical infrastructure of data mining
13th Week
Data mining methods and their applications
14th Week
Data mining methods 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.
