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 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| 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 | An ability to apply advanced knowledge of computing and/or informatics to solve software engineering problems. | X | ||||
| 2 | Develop solutions using different technologies, software architectures and life-cycle approaches. | X | ||||
| 3 | An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices. | X | ||||
| 4 | An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements. | X | ||||
| 5 | Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects. | |||||
| 6 | An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain. | |||||
| 7 | An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering. | |||||
| 8 | Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies. | |||||
| 9 | An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions. | |||||
| 10 | Promote the development, adoption and sustained use of standards of excellence for software engineering practices. | |||||
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 | ||