Information Package / Course Catalogue
| Information Technologies (English) (Thesis) | |||||
| Master | Length of the Programme: 2 | Number of Credits: 120 | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF: Level 7 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Graduate School | ||||
| Course Code | BDA 503 | ||||
| Course Title in English | Data Analytics Essentials | ||||
| Course Title in Turkish | Veri Analitiğinin Temelleri | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom | ||||
| Level of Course | Introductory | ||||
| Semester | Fall | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 174 hours per semester | ||||
| Number of Credits | 7.5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | Basic probability knowledge | ||||
| Co-requisites | None | ||||
| Registration Restrictions | Only Graduate Students | ||||
| Overall Educational Objective | To learn the basic data analytics process with on hands applications using modern tools to explore data by summarizing, slicing/dicing and analyzing data via graphical and quantitative tools. | ||||
| Course Description | The aim of the course is to give the fundamentals of exploratory data analytics. Exploratory data analytics focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, to consider how that data set came into existence, and to decide how it can be investigated with more formal statistical methods. | ||||
| Course Description in Turkish | Bu ders veri analitiğinin temellerini inceleyen bir ders olarak tasarlanmıştır. Araştırma amaçlı veri analitiği verinin altında yatan yapılanmayı anlamaya, veri seti hakkında sezgi geliştirmeye, verinin nasıl ortaya çıkıp, nasıl hazırlandığını düşünmeye ve formal istatistiki metotlarla nasıl derinlemesine incelenebileceğine odaklanır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Pose a question to a data set 2) Wrangle a data set into a usable format and fix any problems with it 3) Explore the data, find patterns in it, and build intuition about it 4) Draw conclusions and/or make predictions 5) Communicate your findings using computer based statistical and analytical tools |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 1) An ability to develop and deepen their knowledge in the field of Information Technologies at the level of expertise based on their undergraduate level qualifications. | |||||
| 2) An ability to apply scientific and practical knowledge in statistics, computing and computer science. | |||||
| 3) A Comprehensive knowledge of analysis and modeling methods and their limitations. | |||||
| 4) An ability to design and apply analytical, modeling and experimental H 2 based researches, analyzes and interprets complex situations encountered in this process. | |||||
| 5) An ability to transmit the process and results of the work of information systems systematically and clearly in written and oral form in national and international environments. | |||||
| 6) An understanding of data collection, processing, use, interpretation and social, scientific and ethical values in all professional and professional activities. | |||||
| 7) An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take responsibility. | |||||
| 8) An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. | |||||
| 9) An ability to communicate verbally and in writing in English at least at the level of B2 of CEFR. | |||||
| 10) An understanding the social and environmental aspects of IT applications. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to develop and deepen their knowledge in the field of Information Technologies at the level of expertise based on their undergraduate level qualifications. | N | |
| 2) | An ability to apply scientific and practical knowledge in statistics, computing and computer science. | N | |
| 3) | A Comprehensive knowledge of analysis and modeling methods and their limitations. | N | |
| 4) | An ability to design and apply analytical, modeling and experimental H 2 based researches, analyzes and interprets complex situations encountered in this process. | N | |
| 5) | An ability to transmit the process and results of the work of information systems systematically and clearly in written and oral form in national and international environments. | N | |
| 6) | An understanding of data collection, processing, use, interpretation and social, scientific and ethical values in all professional and professional activities. | N | |
| 7) | An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take responsibility. | N | |
| 8) | An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. | N | |
| 9) | An ability to communicate verbally and in writing in English at least at the level of B2 of CEFR. | N | |
| 10) | An understanding the social and environmental aspects of IT applications. | N |
| Prepared by and Date | , |
| Course Coordinator | ÖZGÜR ÖZLÜK |
| Semester | Fall |
| Name of Instructor | Prof. Dr. ÖZGÜR ÖZLÜK |
Course Contents
| Week | Subject |
| 1) | Introduction to Data Science |
| 1) | Introduction to Data Science |
| 2) | Basics of R |
| 3) | Basics of R |
| 4) | Data Wrangling |
| 5) | Data Wrangling |
| 6) | Data Wrangling |
| 7) | Data Analysis (one variable) |
| 8) | Data Analysis (two variable) |
| 9) | Data Analysis (multi variable) |
| 10) | Data Analysis (non parametric) |
| 11) | Data Visualization |
| 12) | Data Visualization |
| 13) | Data Visualization |
| 14) | Final Review |
| 15) | Final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | None | ||||||||||||||||||
| Teaching Methods | Flipped classroom/Exercise/Laboratory/Active learning | ||||||||||||||||||
| Homework and Projects | Students are required to complete a portfolio to be able to enter the final exam | ||||||||||||||||||
| Laboratory Work | Students will apply the methods they learned using R at the laboratory hours | ||||||||||||||||||
| Computer Use | Students will apply the methods they learned using R at the laboratory hours | ||||||||||||||||||
| Other Activities | None | ||||||||||||||||||
| Assessment Methods |
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| Course Administration |
orbayb@mef.edu.tr 02123953600 |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 2 | 1.5 | 49 | |||
| Laboratory | 14 | 2 | 1.5 | 49 | |||
| Project | 9 | 2 | 1 | 27 | |||
| Midterm(s) | 1 | 30 | 30 | ||||
| Final Examination | 1 | 30 | 3 | 33 | |||
| Total Workload | 188 | ||||||
| Total Workload/25 | 7.5 | ||||||
| ECTS | 7.5 | ||||||