The objective of “Big Data Analytics and Data Mining” course is to teach principal data mining and machine learning techniques to store, maintain and effectively analyse Big Data. In this context, one of the main goals of this course is to provide a solid understanding of retrieving valuable knowledge from Big Data to be used for decision support systems.
Prerequisite(s)
None
Corequisite(s)
None
Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
-“Big Data Science & Analytics: A Hands-On Approach”, A. Bahga, V.Madisetti, VPT, 2016.
Other Sources
-“Mining of Massive Datasets”, 2nd ed., J.Leskovec, A.Rajamaran, J.D.Ullman, Cambridge University Press, 2014.
-“Big Data Analytics with R and Hadoop”, V. Prajapati, Packt Publishing, 2013.
Course Schedules
Week
Contents
Learning Methods
1. Week
What is Big Data?
Theory
2. Week
Platforms of Big Data Analytics – I (MapReduce)
Theory, Practice
3. Week
Platforms of Big Data Analytics – II (Hadoop)
Theory, Practice
4. Week
Platforms of Big Data Analytics – III (GraphLab)
Theory, Practice
5. Week
Data Preprocessing, Data Cleansing and Data Standartization
Theory, Practice
6. Week
Clustering and Classification
Theory, Practice
7. Week
Structured Data Mining
Theory, Practice
8. Week
Text Analysis
Theory, Practice
9. Week
Dimension Reduction
Theory, Practice
10. Week
Web Mining
Theory, Practice
11. Week
Linked Big Data
Theory, Practice
12. Week
R Language - Statistical Analysis and Data Visualization
Theory, Practice
13. Week
Visualization of Big Data
Theory, Practice
14. Week
Advance Topics and Research Areas
Theory, Practice
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
30
Project(s)
1
30
Final Exam
1
40
Program Outcomes
PO-1
Have scientific research in mathematics and computer science in the level of theoretical and practical knowledge.
PO-2
On the basis of undergraduate level qualifications, develop and deepen the same or a different areas of information at the level of expertise, and analyze and interpret by using statistical methods
PO-3
Develop new strategic approaches for the solution of complex problems encountered in applications related to the field and unforeseen and take responsibility for the solution.
PO-4
Evaluate critically skills acquired in the field of information in the level of expertise and assess the learning guides.
PO-5
Transfer current developments in the field and their work to the groups inside and outside the area supporting with quantitative and qualitative datas as written, verbal and visual by a systematic way.
PO-6
Use information and communication technologies with computer software in advanced level.
PO-7
Develop efficient algorithms by modeling problems faced in the field and solve such problems by using actual programming languages.
PO-8
Respect to social, scientific, cultural and ethical values at the stages of data collection related to the field, interpretation, and implementation.
PO-9
To solve problems related to the field, establish functional interacts by using strategic decision making processes.
PO-10
Establish and discuss in written, oral and visual communication in an advanced level by using at least one foreign language.
Learning Outcomes
LO-1
Identify main differences between Big Data and Traditional Data.
LO-2
Identify different platforms for storing Big Data.
LO-3
Learn Machine Learning techniques that are used for Big Data Analytics.
LO-4
Learn the techniques of retrieving valuable knowledge from Big Data as well as visualizing those data for a better understanding.
LO-5
Identify recent problems and application fields of Big Data Analytics and gain fundamental knowledge on planning and solving those problems.
LO-6
Learn statistical analysis and data visualization using R language.
