The aim of this course is to teach the basics of big data tools techniques. Many of the world's greatest discoveries and decisions in science and technology as a whole are now based on analyzing large data sets. This course promises an extensive and practical introduction to big data: data analysis techniques, including data storage, feature engineering, statistical analysis, machine learning, and deep learning; data analysis tools such as spreadsheets and Python. While tools and techniques are practical, they are at a convenient level, providing a basis for future discoveries and applications. Prerequisites: comfort with basic logic and mathematical concepts as well as concepts of computer science and programming experience.
Prerequisite(s)
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Corequisite(s)
-
Special Requisite(s)
Instructor(s)
Assis. Professor Fatma P. AKBULUT
Course Assistant(s)
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Schedule
The course is not opened for this semester.
Office Hour(s)
The course is not opened for this semester.
Teaching Methods and Techniques
- Lecture, discussion
Principle Sources
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Other Sources
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Course Schedules
Week
Contents
Learning Methods
1. Week
Overview of big data analytics
Laboratory Assignment with Spreadsheet
2. Week
Big data analytics platforms
Laboratory Assignment with Python
3. Week
Big data storage and analytics
Laboratory Assignment with Python
4. Week
Clustering and classification
Laboratory Assignment with Python
5. Week
Big data analytics Algorithms-II
Laboratory Assignment with Python
6. Week
Spark and Data analytics
Laboratory Assignment with Python
7. Week
Linked big data - Graph computing
Laboratory Assignment with Python
8. Week
Midterm
Written exam
9. Week
Linked big data - Graph analytics
Laboratory Assignment with Python
10. Week
Graphical models
Laboratory Assignment with Python
11. Week
Big data visualisation
Laboratory Assignment with Python
12. Week
Cognitive mobile analysis
Laboratory Assignment with Python
13. Week
Big data in the real world
Introduction to Deep Learning
Laboratory Assignment with Python
14. Week
Deep Learning
Project Presentations
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
20
Homework / Term Projects / Presentations
1
25
Final Exam
1
35
Program Outcomes
PO-1
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
PO-2
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
PO-3
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
PO-4
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5
Ability to design and conduct experiments, gather data, analyse and interpret results for investigating engineering problems.
PO-6
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7
Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9
Awareness of professional and ethical responsibility.
PO-10
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11
Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1
Understand the benefits that Big Data can offer to businesses and organizations
LO-2
Ability to obtain, clean/process and transform big data
LO-3
Analyze and interpret data using an ethically responsible approach
LO-4
Students will develop relevant programming abilities to build and assess data-based models
LO-5
Students will demonstrate proficiency with statistical analysis of data
LO-6
Interpret data findings effectively to any audience, orally, visually, and in written formats
LO-7
Students will apply data science concepts and methods to solve problems in real-world contexts and will communicate these solutions effectively
