ECTS - Big Data Analytics
Big Data Analytics (CMPE543) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Big Data Analytics | CMPE543 | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | N/A |
| Course Level | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| Course Objectives | The objective of this course is to present methods and technologies for sharing, visualizing, classifying, and analyzing big data. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Infrastructure as a Service(IaaS), Hadoop framework, hive infrastrucure, data visualization, MapReduce model, NoSQL databases, large-scale data workflows, clustering, using R. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction | Chapter 1 (Text Book) |
| 2 | Hosting and Sharing Big Data | Chapter 2 (Text Book) |
| 3 | Non-relational databases | Chapter 3 (Text Book) |
| 4 | Processing with Big Data | Chapter 4 (Text Book) |
| 5 | Using Hadoop | Chapter 5 (Text Book) |
| 6 | Building a Data Dashboard | Chapter 6 (Text Book) |
| 7 | Visualization Big Data | Chapter 7 (Text Book) |
| 8 | Map Reduce Model | Chapter 8 (Text Book) |
| 9 | Map Reduce Model | Chapter 8 (Text Book) |
| 10 | Data Transformation Workflows | Chapter 9 (Text Book) |
| 11 | Data Classification with Mahout | Chapter 10 (Text Book) |
| 12 | Statistical Analysis with R | Chapter 11 (Text Book) |
| 13 | Building Analytics Workflows | Chapter 12 (Text Book) |
| 14 | Building Analytics Workflows | Chapter 12 (Text Book) |
| 15 | Review | |
| 16 | Review |
Sources
| Course Book | 1. Data Just Right: Introduction to Large-Scale Data & Analytics”, M. Manoochehri, Addison-Wesley, 2013 |
|---|---|
| Other Sources | 2. “Mining of Massive Datasets”, A. Rajaraman & J. D: Ullman, Cambridge University Press, 2011. |
| 3. Apache Hadoop Project, available at http://hadoop.apache.org/ |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | 3 | 30 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 35 |
| Final Exam/Final Jury | 1 | 35 |
| Toplam | 5 | 100 |
| Percentage of Semester Work | 65 |
|---|---|
| Percentage of Final Work | 35 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Ability to apply knowledge on Mathematics, Science and Engineering to advanced systems. | |||||
| 2 | Implementing long-term research and development studies in major areas of Electrical and Electronics Engineering. | |||||
| 3 | Ability to use modern engineering tools, techniques and facilities in design and other engineering applications. | X | ||||
| 4 | Graduating researchers active on innovation and entrepreneurship. | |||||
| 5 | Ability to report and present research results effectively. | |||||
| 6 | Increasing the performance on accessing information resources and on following recent developments in science and technology. | |||||
| 7 | An understanding of professional and ethical responsibility. | |||||
| 8 | Increasing the performance on effective communications in both Turkish and English. | |||||
| 9 | Increasing the performance on project management. | |||||
| 10 | Ability to work successfully at project teams in interdisciplinary fields. | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 2 | 32 |
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | 3 | 5 | 15 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 10 | 10 |
| Prepration of Final Exams/Final Jury | 1 | 20 | 20 |
| Total Workload | 125 | ||