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 | 2 | 2 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| CMPE438 |
| Course Language | English |
|---|---|
| Course Type | N/A |
| 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 computing fields; ability to apply theoretical and practical knowledge of these fields in solving engineering problems related to information systems. | |||||
| 2 | Ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose. | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose. | |||||
| 4 | Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in information systems engineering applications; ability to use information technologies effectively. | |||||
| 5 | Ability to gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the information systems discipline. | X | ||||
| 6 | Ability to work effectively in inter/inner disciplinary teams; ability to work individually. | |||||
| 7 | a. Effective oral and written communication skills in Turkish; ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. b. Knowledge of at least one foreign language; ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. | |||||
| 8 | Recognition of the need for lifelong learning; the ability to access information and follow recent developments in science and technology with continuous self-development. | |||||
| 9 | a. Ability to behave according to ethical principles, awareness of professional and ethical responsibility. b. Knowledge of the standards utilized in information systems engineering applications. | |||||
| 10 | a. Knowledge on business practices such as project management, risk management and change management. b. Awareness about entrepreneurship, and innovation. c. Knowledge on sustainable development. | |||||
| 11 | a. Knowledge of the effects of information systems engineering applications on the universal and social dimensions of health, environment, and safety. b. 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 | ||