| Ders Adı | Kodu | Yarıyıl | T+U Saat | Kredi | AKTS |
|---|---|---|---|---|---|
| Bıg Data | SWE 307 | 5 | 3 + 0 | 3 | 5 |
| Ön Koşul Dersleri | |
| Önerilen Seçmeli Dersler | |
| Dersin Dili | İngilizce |
| Dersin Seviyesi | Lisans |
| Dersin Türü | Zorunlu |
| Dersin Koordinatörü | Prof.Dr. AHMET ÖZMEN |
| Dersi Verenler | |
| Dersin Yardımcıları | |
| Dersin Kategorisi | Alanına Uygun Öğretim |
| Dersin Amacı | The course aims to introduce the concept of big data, data visualization and analysis methods. Programming languages and software tools commonly used in processing big data are introduced in details. The installation of these software tools and the creation of data processing pipelines are illustrated by the example applications. |
| Dersin İçeriği | Basic concepts in data science and big data. Analysis and visualization of big data. Software architectures, tools and applications for collecting, storing and processing big data. |
| # | Ders Öğrenme Çıktıları | Öğretim Yöntemleri | Ölçme Yöntemleri |
|---|---|---|---|
| 1 | Big data analysis and learning the basic concepts | Lecture, Question-Answer, Drilland Practice, | Testing, Homework, Project / Design, |
| 2 | Understanding of model development with programming | Lecture, Question-Answer, Drilland Practice, | Testing, Homework, Project / Design, |
| 3 | Learning big data infrastructure systems | Lecture, Question-Answer, | Testing, Project / Design, |
| 4 | Having information about the graphical representation of data | Lecture, Question-Answer, Drilland Practice, Group Study, Self Study, Project Based Learning, | Project / Design, Performance Task, |
| 5 | To comprehend basic methods in search engines and suggestion systems | Drilland Practice, Group Study, Self Study, Project Based Learning, | Project / Design, Performance Task, |
| Hafta | Ders Konuları | Ön Hazırlık |
|---|---|---|
| 1 | Introduction to big data, basic information | |
| 2 | Introduction to Python | |
| 3 | R language | |
| 4 | Data analysis and visualization | |
| 5 | Hadoop Systems | |
| 6 | Striim, Cloudera | |
| 7 | Machine learning: linear regression, classification, clustering | |
| 8 | Apache Spark, Hive, Cassandra | |
| 9 | Spark with No Sql, Kafka system, RabbitMQ | |
| 10 | Spark ML library applications | |
| 11 | PageRank, search systems | |
| 12 | Analysis with Tensor Flow, VoltDB, Data Flow | |
| 13 | Big data applications | |
| 14 | Evaluation of projects |
| Kaynaklar | |
|---|---|
| Ders Notu | |
| Ders Kaynakları | |
| Sıra | Program Çıktıları | Katkı Düzeyi | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | To have sufficient foundations on engineering subjects such as science and discrete mathematics, probability/statistics; an ability to use theoretical and applied knowledge of these subjects together for engineering solutions. | ||||||
| 2 | An ability to determine, describe, formulate and solve engineering problems; for this purpose, an ability to select and apply proper analytic and modeling methods,al background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations. | ||||||
| 3 | An ability to select and use modern techniques and tools for engineering applications; an ability to use information technologies efficiently. | ||||||
| 4 | An ability to analyze a system, a component or a process and design a system under real limits to meet desired needs; in this direction, an ability to apply modern design methods. | ||||||
| 5 | An ability to design, conduct experiment, collect data, analyze and comment on the results and consciousness of becoming a volunteer on research. | ||||||
| 6 | Understanding, awareness of administration, control, development and security/reliability issues about information technologies. | ||||||
| 7 | An ability to work efficiently in multidisciplinary teams, self confidence to take responsibility. | ||||||
| 8 | An ability to present himself/herself or a problem with oral/written techniques and have efficient communication skills; know at least one extra language. | ||||||
| 9 | An awareness about importance of lifelong learning; an ability to update his/her knowledge continuously by means of following advances in science and technology. | ||||||
| 10 | Understanding, practicing of professional and ethical responsibilities, an ability to disseminate this responsibility on society. | ||||||
| 11 | An understanding of project management, workplace applications, health issues of laborers, environment and job safety; an awareness about legal consequences of engineering applications. | ||||||
| 12 | An understanding universal and local effects of engineering solutions; awareness of entrepreneurial and innovation and to have knowledge about contemporary problems. | ||||||
| Değerlendirme Sistemi | |
|---|---|
| Yarıyıl Çalışmaları | Katkı Oranı |
| 1. Ara Sınav | 40 |
| 1. Kısa Sınav | 10 |
| 2. Kısa Sınav | 10 |
| 1. Performans Görevi (Uygulama) | 40 |
| Toplam | 100 |
| 1. Yıl İçinin Başarıya | 50 |
| 1. Final | 50 |
| Toplam | 100 |
| AKTS - İş Yükü Etkinlik | Sayı | Süre (Saat) | Toplam İş Yükü (Saat) |
|---|---|---|---|
| Course Duration (Including the exam week: 16x Total course hours) | 14 | 3 | 42 |
| Hours for off-the-classroom study (Pre-study, practice) | 14 | 1 | 14 |
| Mid-terms | 1 | 15 | 15 |
| Quiz | 2 | 4 | 8 |
| Performance Task (Application) | 1 | 25 | 25 |
| Final examination | 1 | 20 | 20 |
| Toplam İş Yükü | 124 | ||
| Toplam İş Yükü / 25 (Saat) | 4,96 | ||
| Dersin AKTS Kredisi | 5 | ||