TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF ENGINEERING |
| ELECTRICAL-ELECTRONIC ENGINEERING PR. (ENGLISH) | |
| Code | EEES406 |
| Name | Data Analytics for Internet of Things |
| Term | 2020-2021 Academic Year |
| Semester | 8. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 4 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Belirsiz |
| Type | Normal |
| Label | E Elective |
| Mode of study | Uzaktan Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi ERCAN AVŞAR |
| Course Instructor |
Dr. Öğr. Üyesi ERCAN AVŞAR
(Bahar)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
Introduction of basic machine learning methods, gaining theoretical and practical knowledge required for utilization of smart decision methods in IoT applications
Course Content
Importance of data analytics for IoT, Fundamentals of probability and random variables, What is machine learning, Classification and regression problems, Supervised learning: Curve fitting, decision trees, k-nearest neighbor algorithm, Unsupervised learning: clustering methods, principal component analysis Implementation of supervised and unsupervised learning methods in Python, Reading analog and digital data from sensors. Sensor calibration, Realization of smart decision methods using a single board computer Big data analytics: Necessity and requirements
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Can perform statistical data analysis and develop smart decision algorithms |
| LO02 | Gains coding ability for IoT applications |
| LO03 | Be able to utilize smart decision algorithms in IoT applications |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | - | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in complex engineering problems. | 4 |
| PLO02 | - | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | 5 |
| PLO03 | - | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | 5 |
| PLO04 | - | Ability to devise, select, and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ computer programming techniques, and information technologies effectively. | 5 |
| PLO05 | - | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | 4 |
| PLO06 | - | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | 5 |
| PLO07 | - | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, | 5 |
| PLO08 | - | 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. | 5 |
| PLO09 | - | Consciousness to behave according to ethical principles and professional and ethical responsibility; knowledge on standards used in engineering practice. | 5 |
| PLO10 | - | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | 5 |
| PLO11 | - | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | 5 |
| PLO12 | - | Ability to apply the knowledge of electrical-electronics engineering to profession-specific tools and devices. | 3 |
| PLO13 | - | Having consciousness about the scientific, social, historical, economical and political facts of the society, world and age lived in. | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Importance of data analytics for IoT | None | |
| 2 | Fundamentals of probability and random variables | Review the previous lecture contents | |
| 3 | What is machine learning. Classification and regression problems | Review the previous lecture contents | |
| 4 | Supervised learning: Curve fitting, decision trees, k-nearest neighbor algorithm | Review the previous lecture contents | |
| 5 | Implementation of supervised learning methods in Python | Review the previous lecture contents | |
| 6 | Unsupervised learning: clustering methods, principal component analysis | Review the previous lecture contents | |
| 7 | Implementation of unsupervised learning methods in Python | Review the previous lecture contents | |
| 8 | Mid-Term Exam | Review the previous lecture contents | |
| 9 | Reading analog and digital data from sensors. Sensor calibration | Review the previous lecture contents | |
| 10 | Realization of smart decision methods using a single board computer | Review the previous lecture contents | |
| 11 | Big data analytics: Necessity and requirements | Review the previous lecture contents | |
| 12 | In-class sample project development | Review the previous lecture contents | |
| 13 | In-class sample project development | Review the previous lecture contents | |
| 14 | Student project studies | Review the previous lecture contents | |
| 15 | Student project studies | Review the previous lecture contents | |
| 16 | Term Exams | Review the previous lecture contents | |
| 17 | Term Exams | Review the previous lecture contents |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |