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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 | Adequate knowledge of mathematics, science and related engineering disciplines; ability to use theoretical and applied knowledge in these fields in solving complex engineering problems. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Ability to identify, formulate and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. | 2 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics. | 5 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Ability to work effectively in interdisciplinary and multidisciplinary teams; individual working skills. | |
| PLO07 | Bilgi - Kuramsal, Olgusal | Ability to communicate effectively verbally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and give and receive clear and understandable instructions. | 3 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology, and constantly renew oneself. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Knowledge of ethical principles, professional and ethical responsibility, and standards used in engineering practice. | |
| PLO10 | Bilgi - Kuramsal, Olgusal | Knowledge of business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; knowledge of sustainable development. | |
| PLO11 | Bilgi - Kuramsal, Olgusal | Knowledge of the effects of engineering practices on health, environment and safety in universal and social dimensions and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions. |
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 | ||