Undergraduate
Faculty of Engineering and Architecture
Industrial Engineering
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Big Data Analytics

Course CodeSemester Course Name LE/RC/LA Course Type Language of Instruction ECTS
CSE0448 Big Data Analytics 2/0/2 DE English 6
Course Goals
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) -
Corequisite(s) -
Special Requisite(s) -
Instructor(s) Assist. Prof. Dr. Fatma P. AKBULUT
Course Assistant(s) -
Schedule -
Office Hour(s) -
Teaching Methods and Techniques -Lecture, discussion
Principle Sources -
Other Sources -
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 Spreadsheet
3. Week Big data storage and analytics Laboratory Assignment with Spreadsheet
4. Week Clustering and classification Laboratory Assignment with Spreadsheet
5. Week Big data analytics Algorithms-II Laboratory Assignment with Spreadsheet
6. Week Spark and Data analytics Laboratory Assignment with Spreadsheet
7. Week Linked big data - Graph computing Laboratory Assignment with Spreadsheet
8. Week Midterm Exam
9. Week Linked big data - Graph analytics Laboratory Assignment with Spreadsheet
10. Week Graphical models Laboratory Assignment with Spreadsheet
11. Week Big data visualisation Laboratory Assignment with Spreadsheet
12. Week Cognitive mobile analysis Laboratory Assignment with Spreadsheet
13. Week Big data in the real world Introduction to Deep Learning Laboratory Assignment with Spreadsheet
14. Week Deep Learning Laboratory Assignment with Spreadsheet
15. Week Final Exam
16. Week Final Exam
17. Week Final Exam
Assessments
Evaluation tools Quantity Weight(%)
Midterm(s) 1 30
Homework / Term Projects / Presentations 1 30
Final Exam 1 40


Program Outcomes
PO-1Ability to apply theoretical and practical knowledge gained by Mathematics, Science and their engineering fields and ability to use their knowledge in solving complex engineering problems.
PO-2Ability of determining, defining, formulating and solving complex engineering problems; for that purpose develop the ability of selecting and implementing suitable models and methods of analysis.
PO-3Ability of designing a complex system, process, device or product under real world constraints and conditions serving certain needs; for this purpose ability of applying modern design techniques
PO-4Ability of selecting and using the modern techniques and devices which are necessary for analyzing and solving complex problems in engineering implementations; ability of efficient usage of information technologies.
PO-5Ability of designing experiments, conducting tests, collecting data and analyzing and interpreting the solutions to investigate of complex engineering problems or discipline-specific research topics.
PO-6Ability of working efficiently in intra-disciplinary and multi-disciplinary teams; individual working ability and habits.
PO-7Ability of verbal and written communication skills; and at least one foreign language skills, ability to write effective reports and understand written reports, ability to prepare design and production reports, ability to make impressive presentation, ability to give and receive clear and understandable instructions
PO-8Awareness of importance of lifelong learning; ability to access data, to follow up the recent innovation in science and technology for continuous self-improvement.
PO-9Conformity to ethical principles; knowledge about occupational and ethical responsibility, and standards used in engineering applications.
PO-10Knowledge about work life implementations such as project management, risk management and change management; awareness about entrepreneurship and innovativeness; knowledge about sustainable development.
PO-11Knowledge about effects of engineering applications on health, environment and security in global and social dimensions, and on the problems of the modern age in engineering; awareness about legal outcomes of engineering solutions.
Learning Outcomes
LO-1Understand the benefits that Big Data can offer to businesses and organizations
LO-2 Ability to obtain, clean/process and transform big data
LO-3 Students will develop relevant programming abilities to build and assess data-based models
Course Assessment Matrix:
Program Outcomes - Learning Outcomes Matrix
 PO 1PO 2PO 3PO 4PO 5PO 6PO 7PO 8PO 9PO 10PO 11
LO 1
LO 2
LO 3