Faculty Of Engıneerıng
Computer And Software Engıneerıng
Course Information
| BIG DATA APPLICATIONS | |||||
|---|---|---|---|---|---|
| Code | Semester | Theoretical | Practice | National Credit | ECTS Credit |
| Hour / Week | |||||
| CSE437 | Fall | 3 | 0 | 3 | 5 |
| Prerequisites and co-requisites | None |
|---|---|
| Language of instruction | English |
| Type | Elective |
| Level of Course | Bachelor's |
| Lecturer | Lect. Volkan Kadir GÜNGÖR |
| Mode of Delivery | Face to Face |
| Suggested Subject | None |
| Professional practise ( internship ) | None |
| Objectives of the Course | This course aims to provide an introduction to the data science and data analytics using the methods of statistical learning, an approach blending classical statistical methods with recent advances in computational and machine learning. The course will cover the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the covered methods. |
| Contents of the Course | Data Science and Big Data Analytics, Relational Databases and Data Modeling, Data Warehousing and Integration, Parallel Databases, Hadoop/ Mapreduce/Spark, Data Visualization, Machine Learning, Classification and Regression, Clustering, Natural Language Processing, Information Retrieval, Network Analysis |
Learning Outcomes of Course
| # | Learning Outcomes |
|---|---|
| 1 | Knowledge about the basic methodologies in data science and big data analytics. |
| 2 | Ability to use knowledge to formulate, and solve practical problems using data science and big data analytics techniques. |
| 3 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating data science and big data analysis problems or discipline specific research questions. |
| 4 | Having sufficient knowledge to be able to realize various applications on Linked Big Data and, graphical representation and analytics of it. |
Course Syllabus
| # | Subjects | Teaching Methods and Technics |
|---|---|---|
| 1 | Introduction to Data Science and Big Data Analytics | Lecture, discussion, presentation |
| 2 | Relational Databases and Data Modeling | Lecture, discussion, presentation |
| 3 | Data Warehousing and Integration | Lecture, discussion, presentation |
| 4 | Parallel Databases/Hadoop | Lecture, discussion, presentation |
| 5 | Mapreduce/Spark | Lecture, discussion, presentation |
| 6 | Data Visualization | Lecture, discussion, presentation |
| 7 | Introduction to Machine Learning | Lecture, discussion, presentation |
| 8 | Midterm Exam | Exam |
| 9 | Classification and Regression | Lecture, discussion, presentation |
| 10 | Clustering | Lecture, discussion, presentation |
| 11 | Introduction to Natural Language Processing | Lecture, discussion, presentation |
| 12 | Introduction to Information Retrieval | Lecture, discussion, presentation |
| 13 | Network Analysis | Lecture, discussion, presentation |
| 14 | Project Presentations | Lecture, discussion, presentation |
| 15 | Project Presentations | Lecture, discussion, presentation |
| 16 | Final Exam | Exam |
Course Syllabus
| # | Material / Resources | Information About Resources | Reference / Recommended Resources |
|---|
Method of Assessment
| # | Weight | Work Type | Work Title |
|---|---|---|---|
| 1 | 40% | Mid-Term Exam | Mid-Term Exam |
| 2 | 60% | Final Exam | Final Exam |
Relationship between Learning Outcomes of Course and Program Outcomes
| # | Learning Outcomes | Program Outcomes | Method of Assessment |
|---|---|---|---|
| 1 | Knowledge about the basic methodologies in data science and big data analytics. | 2͵3͵4 | 1͵2 |
| 2 | Ability to use knowledge to formulate, and solve practical problems using data science and big data analytics techniques. | 2͵3͵4 | 1͵2 |
| 3 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating data science and big data analysis problems or discipline specific research questions. | 2͵3͵4 | 1͵2 |
| 4 | Having sufficient knowledge to be able to realize various applications on Linked Big Data and, graphical representation and analytics of it. | 2͵3͵4 | 1͵2 |
Work Load Details
| # | Type of Work | Quantity | Time (Hour) | Work Load |
|---|---|---|---|---|
| 1 | Course Duration | 14 | 3 | 42 |
| 2 | Course Duration Except Class (Preliminary Study, Enhancement) | 14 | 3 | 42 |
| 3 | Presentation and Seminar Preparation | 0 | 0 | 0 |
| 4 | Web Research, Library and Archival Work | 0 | 0 | 0 |
| 5 | Document/Information Listing | 0 | 0 | 0 |
| 6 | Workshop | 0 | 0 | 0 |
| 7 | Preparation for Midterm Exam | 1 | 4 | 4 |
| 8 | Midterm Exam | 1 | 2 | 2 |
| 9 | Quiz | 0 | 0 | 0 |
| 10 | Homework | 3 | 4 | 12 |
| 11 | Midterm Project | 1 | 4 | 4 |
| 12 | Midterm Exercise | 0 | 0 | 0 |
| 13 | Final Project | 1 | 10 | 10 |
| 14 | Final Exercise | 0 | 0 | 0 |
| 15 | Preparation for Final Exam | 1 | 2 | 2 |
| 16 | Final Exam | 1 | 2 | 2 |
| 120 | ||||