BIG DATA ANALYSIS
- Course
- DASC567 - BIG DATA ANALYSIS
- Department
- Data Science - English - Master
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 8
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
Course Description
In this course, students learn how to create computational tools and techniques that are effective and efficient for analyzing big data, which consists of text, image, video, sound, and other types of data that can occupy terabytes and petabytes of storage space. The aim of the course is to cover analytical techniques for big data extraction, integration, indexing, searching and processing. The course begins with an overview of big data and examines what it means to analyze enormous data as well as the associated technological, conceptual, and ethical problems. Big data processing tools like Hadoop are introduced and machine learning approaches like artificial neural networks are investigated. Upon completion of this course, students will have a broad knowledge of big data analytical tools and techniques.
BIG DATA ANALYSIS
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Üniversitede kullanılan yazılımların tanımı: Webmail, SIS, Moodle |
| Week 2 | MS Word'e Giriş ,Yeni belge açma, var olan belgeyi açma, yazım denetleme, metin işlemleri, belgeyi kaydetme |
| Week 3 | Metin biçimleme, paragraf ve satır biçimleme, biçim kopyalama, sayfa biçimleme |
| Week 4 | Köprü ekleme, altbilgi ekleme, üstbilgi ekleme, sayfa numarası ekleme |
| Week 5 | Tablo işlemleri, grafik verilerini değiştirmek |
| Week 6 | Grafiğin renklerini değiştirmek, grafiğin türünü değiştirmek |
| Week 7 | Çizim araçları |
| Week 8 | Tekrar |
| Week 9 | Ara Sınav(lar) 11-20 kasım 2019 |
| Week 10 | Sayfa düzeni ve yazdırma işlemleri |
| Week 11 | MS PowerPoint'e giriş, sunu oluşturmak, slaytlarla çalışmak |
| Week 12 | Slaytlara resim, içerik ve grafik |
| Week 13 | Ses dosyası, video ve animasyon ekleme |
| Week 14 | Özel Animasyon |
| Week 15 | Tekrar |
Reference Books & Course Materials
- 01 Office 2013 Türkçe, Bayram Yıldız, 1.Baskı, ISBN:9786055201203
- 02 Adım Adım Microsoft Office Professional 2010, Curtis Frye, Joyce Cox, Joan Lambert, ISBN:9789755096971
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
- Review the literature and apply data science theories and methodology in new research and experiments.
- Analyze datasets using supervised and unsupervised machine learning techniques.
- Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
- Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
- Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
- Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
- Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
- Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
- Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.
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