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 | Adequate knowledge in mathematics, science and subjects specific to the computer engineering discipline; the ability to apply theoretical and practical knowledge of these areas to complex engineering problems. | X | ||||
| 2 | The ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose. | X | ||||
| 3 | The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. | X | ||||
| 4 | The ability to develop, select and utilize modern techniques and tools essential for the analysis and determination of complex problems in computer engineering applications; the ability to utilize information technologies effectively. | X | ||||
| 5 | The ability to design experiments, conduct experiments, gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the computer engineering discipline. | X | ||||
| 6 | The ability to work effectively in inter/inner disciplinary teams; ability to work individually | X | ||||
| 7 | Effective oral and writen communication skills in Turkish; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions. | |||||
| 8 | The knowledge of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions. | |||||
| 9 | Recognition of the need for lifelong learning; the ability to access information, to follow recent developments in science and technology. | X | ||||
| 10 | The ability to behave according to ethical principles, awareness of professional and ethical responsibility; | X | ||||
| 11 | Knowledge of the standards utilized in software engineering applications | |||||
| 12 | Knowledge on business practices such as project management, risk management and change management; | X | ||||
| 13 | Awareness about entrepreneurship, innovation | |||||
| 14 | Knowledge on sustainable development | |||||
| 15 | Knowledge on the effects of computer engineering applications on the universal and social dimensions of health, environment and safety; | X | ||||
| 16 | Awareness of the legal consequences of engineering solutions | |||||
| 17 | An ability to describe, analyze and design digital computing and representation systems. | X | ||||
| 18 | An ability to use appropriate computer engineering concepts and programming languages in solving computing problems. | X | ||||
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 | ||