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Information
| Code | CENG0028 |
| Name | Applied Data Science |
| Term | 2022-2023 Academic Year |
| Semester | . Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator |
Course Goal / Objective
The main objectives of this course are to learn how to use tools to clean, analyze, explore and visualize data; making data-based inferences and decisions.
Course Content
The content of this course will teach you how to use python-based programming tools to develop applications on data.
Course Precondition
Familiarity with Python programming and basic use of NumPy, pandas and matplotlib are required
Resources
Applied Data Science with Python and Jupyter by Alex Galea
Notes
Python Data Science Handbook by Jake VanderPlas
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Learning to clean up and reshape messy datasets |
| LO02 | Use exploratory tools such as clustering and visualization tools to analyze data |
| LO03 | Apply dimensionality reduction tools such as principle component analysis |
| LO04 | Use methods such as logistic regression, nearest neighbors, decision trees, support vector machines, and neural networks to build a classifier |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | 2 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 4 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 4 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 4 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | 2 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | 3 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to data analysis tools in Python | Reading of course notes | Öğretim Yöntemleri: Gösteri, Anlatım |
| 2 | Descriptive statistics | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 3 | Data structures with Pandas | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 4 | Introduction to hypothesis testing and statistical inference | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 5 | Data acquisition via APIs | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 6 | Linear regression | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 7 | Classification methods, including logistic regression, k-nearest neighbors, decision trees, support vector machines, and neural networks | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 8 | Mid-Term Exam | Reading of course notes | Ölçme Yöntemleri: Yazılı Sınav, Ödev |
| 9 | Classification methods continue | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 10 | Data visualization | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 11 | Clustering methods | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 12 | Dimensionality reduction using principle component analysis | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 13 | Network analysis | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 14 | Cleaning and reformatting messy datasets using regular expressions | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 15 | Ethics of big data | Reading of course notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 16 | Term Exams | Reading of course notes | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Reading of course notes | Ölçme Yöntemleri: Yazılı 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 | ||