ECTS - Big Data Programming
Big Data Programming (SE421) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Big Data Programming | SE421 | Area Elective | 2 | 2 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Drill and Practice. |
| Course Lecturer(s) |
|
| Course Objectives | Upon completing this course, the student will be able to design and implement map-reduce programs for various large data set processing tasks, and will be able to design and implement programs using Apache Spark. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | What is "Big Data"; the dimensions of Big Data; scaling problems; HDFS and the Hadoop ecosystem; the basics of HDFS, MapReduce and Hadoop cluster; writing MapReduce programs to answer questions about data; MapReduce design patterns; basic Spark architecture; common operations; Use Resilient Distributed Datasets (RDD) operations. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to Big Data and Hadoop | Chapter 1 |
| 2 | Setting Up a Hadoop Cluster | Chapter 9 |
| 3 | Hadoop Distributed Filesystem (HDFS) | Chapter 3 |
| 4 | Hadoop Distributed Filesystem (HDFS) | Chapter 4 |
| 5 | MapReduce | Chapter 2 |
| 6 | MapReduce | Chapter 5 |
| 7 | MapReduce | Chapter 6 |
| 8 | MapReduce | Chapter 7-8 |
| 9 | Administering Hadoop | Chapter 10 |
| 10 | Pig | Chapter 11 |
| 11 | Hive | Chapter 12 |
| 12 | HBase | Chapter 13 |
| 13 | Spark Programming | Other resources 2 |
| 14 | Spark Programming | Other resources 2 |
| 15 | Final Exam | |
| 16 | Final Exam |
Sources
| Course Book | 1. Hadoop: The Definitive Guide, Tom White, 3rd. Ed., O'Reilly Media, 2012 |
|---|---|
| Other Sources | 2. MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems, Donald Miner, Adam Shook, O'Reilly Media, November 2012 |
| 3. Learning Spark: Lightning-Fast Big Data Analysis, Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia, O'Reilly Media, January 2015 |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | 5 | 30 |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 30 |
| Final Exam/Final Jury | 1 | 40 |
| Toplam | 7 | 100 |
| Percentage of Semester Work | |
|---|---|
| Percentage of Final Work | 100 |
| 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 | Gain sufficient knowledge in mathematics, science and computing; be able to use theoretical and applied knowledge in these areas to solve engineering problems related to information systems. | |||||
| 2 | To be able to identify, define, formulate and solve complex engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose. | |||||
| 3 | Designs a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose. | |||||
| 4 | To be able to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in information systems engineering applications; to be able to use information technologies effectively. | |||||
| 5 | Designs and conducts experiments, collects data, analyzes and interprets results to investigate complex engineering problems or research topics specific to the discipline of information systems engineering. | X | ||||
| 6 | Can work effectively in disciplinary and multidisciplinary teams; can work individually. | |||||
| 7 | a. Communicates effectively both orally and in writing; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions. b. Knows at least one foreign language. | |||||
| 8 | To be aware of the necessity of lifelong learning; to be able to access information, to be able to follow developments in science and technology and to be able to renew himself/herself continuously. | |||||
| 9 | a. Acts in accordance with the principles of ethics, gains awareness of professional and ethical responsibility. b. Gains knowledge about the standards used in information systems engineering applications. | |||||
| 10 | a. Gains knowledge about business life practices such as project management, risk management and change management. b. Gains awareness about entrepreneurship and innovation. c. Gains knowledge about sustainable development. | |||||
| 11 | a. To be able to acquire knowledge about the universal and social effects of information systems engineering applications on health, environment and safety and the problems of the era reflected in the field of engineering. b. Gains awareness of the legal consequences of engineering solutions. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | |||
| Laboratory | 14 | 2 | 28 |
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | |||
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | 5 | 6 | 30 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 15 | 15 |
| Prepration of Final Exams/Final Jury | 1 | 20 | 20 |
| Total Workload | 93 | ||