TÜRKÇE
ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| ELECTRICAL-ELECTRONICS ENGINEERING (PhD) (ENGLISH) | |
| Code | EE009 |
| Name | Computer Based Data Analytics |
| Term | 2023-2024 Academic Year |
| Term | Fall |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
This course aims to gain students insight and required skills related to data analytics containing SQL and R Programming, data wrangling, data visualization, exploratory data analysis.
Course Content
Introduction to Data Analytics, Introduction to SQL and Database Structure, SQL commands and sample applications, Introduction to R Programming Language, Data Structures, Control Structures, Functions, Data Wrangling, Data Visualisation.
Course Precondition
No Preparation
Resources
[1] R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, H. Wickham & G. Grolemund [2] Guide to Programming and Algorithms Using R, Ö. Ergül [3] Data Visualization and Exploration with R, E. Pimpler [4] Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R, M. Freeman & J. Ross
Notes
[1] Beginning Data Science with R: Data Analysis, Visualization, and Modelling for the Data Scientist, T. Mailund [2] Data Analytics: Concepts, Techniques, and Applications, M. Ahmed & A. K. Pathan [3] A General Introduction to Data Analytics, J. M. Moreira, A. C. P. L. F. De Carvalho & T. Horvath
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Gaining insight about the term Data Base and Data Analytics |
| LO02 | Ability to use SQL and R programming language |
| LO03 | Possessing skills related to computer based data analytics containing data wrangling, data visualization |
| LO04 | The basics of data analytics were understood. |
| LO05 | Projects related to data analytics were understood. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Being able to specialize in at least one of the branches that form the foundations of Electrical and Electronics Engineering by increasing the level of knowledge beyond the master's level | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | To comprehend the integrity of all the subjects included in the field of specialization. | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Having knowledge of the current scientific literature in the field of specialization to analyze the literature critically | 5 |
| PLO04 | Bilgi - Kuramsal, Olgusal | To comprehend the interdisciplinary interaction of the field with other related branches, to suggest similar interactions. | 5 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to do theoretical and experimental work | 3 |
| PLO06 | Bilgi - Kuramsal, Olgusal | To create a complete scientific text by compiling the information obtained from the research | 4 |
| PLO07 | Bilgi - Kuramsal, Olgusal | To work on the thesis topic programmatically, following the logical integrity required by the subject within the framework determined by the advisor. | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | To search for literature in scientific databases, particularly the ability to correctly and accurately scan databases and evaluate and categorize listed items. | 5 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Having a command of English and related English jargon at a level that can easily read and understand a scientific text written in English in the field of specialization and write a similar text | 4 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Ability to write a computer program in a familiar programming language, generally for a specific purpose, specifically related to the field of expertise. | 5 |
| PLO11 | Bilgi - Kuramsal, Olgusal | Ability to plan and teach lessons related to the field of specialization or related fields | 3 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Being able to guide and take the initiative in environments that require solving problems related to the field | |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Ability to communicate with people in an appropriate language | |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | Adopting the ethical values required by both education and research aspects of academician | |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | To be able to produce projects, policies, and processes in the field of expertise and to evaluate these elements | |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | Ability to research new topics based on existing research experience | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Course Introduction and Scope | No Preparation | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 2 | Introduction to SQL and Database Structure | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 3 | SQL commands and sample applications | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 4 | Introduction to Data Analytics | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 5 | Introduction to R Programming Language | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 6 | Data Structures | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 7 | Control Structures | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Proje / Tasarım |
|
| 9 | Functions | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 10 | Data Wrangling I | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 11 | Data Wrangling II | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 12 | Data Visulization I | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 13 | Data Visulization II | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 14 | Case Study I | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 15 | Case Study II | Review of previous lecture | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Proje Temelli Öğrenme |
| 16 | Term Exams | Ölçme Yöntemleri: Ödev, Sözlü Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Ödev, Sözlü Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||