TÜRKÇE
ENGLISH
Information
| Code | CEN136 |
| Name | Introduction to Data Science |
| Term | 2024-2025 Academic Year |
| Semester | 2. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Mehmet SARIGÜL |
| Course Instructor |
1 |
Course Goal / Objective
This lecture provides a wide overview of the main concepts in data science for beginners. It introduces a set of preliminary tools and techniques to perform data science tasks. By the end of the course, students will learn the basics of the different properties of data (structure, size, and type) and will be able to categorize data based on their properties.
Course Content
Data science in science, society, business, Different kinds of data (statistical, structured, unstructured, big data, ...), jobs of a data scientist, data collection, data preprocessing, exploratory data analysis: summary statistics, presentation, visualisation
Course Precondition
Simple algorithm knowledge
Resources
Lecture notes
Notes
Introducing Data Science Big Data, Machine Learning, and More, Using Python Tools
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Students define the basic concepts and principles of data science. |
| LO02 | Students identify different types of data and how they can be obtained from various sources. |
| LO03 | Students understand the stages of data collection, cleaning, discovery, analysis, and interpretation of results |
| LO04 | Students recognize and can use tools and technologies commonly used for data science at a basic level. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Has capability in the fields of mathematics, science and computer that form the foundations of engineering | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Identifies, formulates, and solves engineering problems, selects and applies appropriate analytical methods and modeling techniques, | 2 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Analyzes a system, its component, or process and designs under realistic constraints to meet the desired requirements,gains the ability to apply the methods of modern design accordingly. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Ability to use modern techniques and tools necessary for engineering practice and information technologies effectively. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to design and to conduct experiments, to collect data, to analyze and to interpret results | 5 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Has ability to work effectively as an individual and in multi-disciplinary teams, take sresponsibility and builds self-confidence | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Can access information,gains the ability to do resource research and uses information resources | 3 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Awareness of the requirement of lifelong learning, to follow developments in science and technology and continuous self-renewal ability | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Ability to communicate effectively orally and in writing, and to read and understand technical publications in at least one foreign language | |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Professional and ethical responsibility, | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Awareness about project management, workplace practices, employee health, environmental and occupational safety, and the legal implications of engineering applications, | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Becomes aware of universal and social effects of engineering solutions and applications, entrepreneurship and innovation, and knowledge of contemporary issues |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Course introduction and basic concepts | Course introduction and basic concepts | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Data types, data sources and data science process | Data types, data sources and data science process | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Evaluation of data collection methods and data sources | Evaluation of data collection methods and data sources | Öğretim Yöntemleri: Anlatım |
| 4 | Data cleaning techniques and data quality | Data cleaning techniques and data quality | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 5 | Basic techniques and visualization tools for data exploration | Basic techniques and visualization tools for data exploration | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 6 | Data visualization applications and visual analysis | Data visualization applications and visual analysis | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 7 | Statistical foundations and basic probability concepts | Statistical foundations and basic probability concepts | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Probability distributions and statistical inference | Probability distributions and statistical inference | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | What is machine learning? Basic concepts and applications | What is machine learning? Basic concepts and applications | Öğretim Yöntemleri: Anlatım, Tartışma |
| 11 | Supervised and unsupervised learning, basic algorithms and sample applications | Supervised and unsupervised learning, basic algorithms and sample applications | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 12 | Deep learning fundamentals and artificial neural networks | Deep learning fundamentals and artificial neural networks | Öğretim Yöntemleri: Anlatım, Tartışma |
| 13 | Big data and parallel computing, frameworks like Apache Spark and Hadoop | Big data and parallel computing, frameworks like Apache Spark and Hadoop | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 14 | Identifying concepts for data science projects in groups | Identifying concepts for data science projects in groups | Öğretim Yöntemleri: Beyin Fırtınası |
| 15 | Data science ethics, data privacy and regulations | Data science ethics, data privacy and regulations | Öğretim Yöntemleri: Anlatım, Tartışma, Beyin Fırtınası |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | 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 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 3 | 8 | 24 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 14 | 14 |
| Final Exam | 1 | 28 | 28 |
| Total Workload (Hour) | 150 | ||
| Total Workload / 25 (h) | 6,00 | ||
| ECTS | 6 ECTS | ||