| CYBER SECURITY (ENGLISH, THESIS) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| BDA5001 | Introduction to Big Data | Spring | 3 | 0 | 3 | 8 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi SERKAN AYVAZ |
| Course Lecturer(s): |
Dr. Öğr. Üyesi YÜCEL BATU SALMAN Dr. Öğr. Üyesi SERKAN AYVAZ |
| Course Objectives: | The objective of this course is introduce fundamental concepts and methods in Big Data analytics and Data science, and provide students the insights into the basics of using "Big Data" in real-world scenarios. |
|
The students who have succeeded in this course; Will learn how to develop fundamental statistical models using R programming language. Will learn how to analyze big data sets to provide insight regarding the assumptions, value drivers, and risks. Will use statistical models to explore different ways to think about uncertainty, guide decision-making, and persuasively communicate analytical results. Will learn how to apply basic methods to text mining, building search engines and recommendation systems. |
| Introduction to Big Data and Data Science Statistical programming: Introduction to R and RStudio Data Modeling Basics/Data Collection/Cleansing/Processing Data Visualization and Communication Simple Regression: Introduction, Statistical and Practical Flexible Regression Models: Dummy Variables Flexible Regression Models: Data Transformation Selective Regression Models Fundamentals of Text Mining Building Search Engines Inner workings of Recommendation Engines |
| Week | Subject | Related Preparation | |
| 1) | Introduction to Big Data and Data Science | ||
| 2) | Statistical programming: Introduction to R and RStudio | ||
| 3) | Data Modeling Basics/Data Collection/Cleansing/Processing | ||
| 4) | Data Visualization and Communication | ||
| 5) | Simple Regression: Introduction, Statistical and Practical Significance of Predictors | ||
| 6) | Flexible Regression Models: Dummy Variables | ||
| 7) | Flexible Regression Models: Data Transformation | ||
| 8) | Selective Regression Models | ||
| 9) | Text Mining | ||
| 10) | Building Search Engines | ||
| 11) | Inner workings of Recommendation Engines | ||
| 12) | Project Presentations | ||
| 13) | Project Presentations | ||
| Course Notes: | Jeffrey Stanton. An Introduction to Data Science (2013) edition 3. Wolfgang Jank. Business Analytics for Managers (2011). Roger D. Peng. R Programming for Data Science(2016) |
| References: | Doing Data Science, Rachel Schutt and Cathy O’Neil. 2014. O’Reilly. The Art of Data Science: A Guide for Anyone Who Works with Data, Roger D. Peng and Elizabeth Matsui An Introduction to Statistical Learning with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani R for Beginners, Emmanuel Paradis, 2005 , http://cran.r-project.org/other-docs.html |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | 1 | % 10 |
| Presentation | % 0 | |
| Project | 1 | % 35 |
| Seminar | % 0 | |
| Midterms | 1 | % 15 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 25 | |
| PERCENTAGE OF FINAL WORK | % 75 | |
| Total | % 100 | |
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications. | |
| 1) | To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field. | |
| 1) | Being able to independently carry out a work that requires expertise in the field. | |
| 1) | To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning. | |
| 1) | To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data. | |
| 2) | To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines, | |
| 2) | To be able to develop strategy, policy and implementation plans in the fields related to the field and to evaluate the obtained results within the framework of quality processes. | |
| 2) | To be able to critically examine social relations and the norms that guide these relations, to develop them and take action to change them when necessary. | |
| 2) | To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field. | |
| 2) | To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility. | |
| 2) | To be able to comprehend the interdisciplinary interaction with which the field is related. | |
| 3) | To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies. | |
| 3) | Being able to lead in environments that require the resolution of problems related to the field. | |
| 3) | To be able to solve the problems encountered in the field by using research methods. |