Information Package / Course Catalogue
| Big Data Analytics (English) (Non-Thesis) | |||||
| Master | Length of the Programme: 1.5 | Number of Credits: 90 | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF: Level 7 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Gradutate School of Science and Engineering | ||||
| Course Code | BDA 557 | ||||
| Course Title in English | Case Studies in Analytics | ||||
| Course Title in Turkish | Veri Analitiğinde Vaka Çalışmaları | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom | ||||
| Level of Course | Intermediate | ||||
| Semester | Summer School | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 173 hours per semester | ||||
| Number of Credits | 7.5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | None | ||||
| Co-requisites | None | ||||
| Registration Restrictions | Only Graduate Students | ||||
| Overall Educational Objective | |||||
| Course Description | This course examines various case studies arising from different application areas. With the aid of experienced academicians and practitioners, each week the students will go through the main steps of tackling analytics problems. With each case study, the data manipulation tools shall be revisited. Attention will be given to feature reduction and model selection. Each case study will be completed by a complete analysis and interpretation of the results | ||||
| Course Description in Turkish | Bu ders farklı uygulama alanlarından kaynaklanan çeşitli vaka çalışmalarını incelemektedir. Deneyimli akademisyenlerin ve uygulayıcıların yardımıyla öğrenciler her hafta analitik problemlerini çözmenin temel adımlarını atacaklar. Her vaka çalışmasında veri işleme araçları yeniden gözden geçirilecektir. Özellik azaltma ve model seçimine dikkat edilecektir. Her vaka çalışması, sonuçların eksiksiz bir analizi ve yorumlanmasıyla tamamlanacaktır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Examine various case studies arising from different application areas 2) Go through the main steps of tackling analytics problems 3) Revisiting data manipulation tools and machine learning models |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 |
|---|---|---|---|
| 1) Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. | |||
| 2) Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | |||
| 3) Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | |||
| 4) Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. | |||
| 5) Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. | |||
| 6) Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. | |||
| 7) Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. | |||
| 8) Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. | |||
| 9) Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. | |||
| 10) Understanding of social and environmental aspects of machine learning applications. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. | S | |
| 2) | Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | S | |
| 3) | Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | S | |
| 4) | Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. | S | |
| 5) | Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. | S | |
| 6) | Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. | S | |
| 7) | Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. | H | |
| 8) | Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. | S | |
| 9) | Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. | S | |
| 10) | Understanding of social and environmental aspects of machine learning applications. | N |
| Prepared by and Date | , |
| Course Coordinator | ÖZGÜR ÖZLÜK |
| Semester | Summer School |
| Name of Instructor | Prof. Dr. ÖZGÜR ÖZLÜK |
Course Contents
| Week | Subject |
| 1) | Basics of Recommendation Engines using R |
| 2) | Using MS Excel for Data Analysis |
| 3) | Optical character recognition in Turkish documents using R |
| 4) | Erasure correction coding |
| 5) | Text processing using ANNs |
| 6) | An HR Application of Classification Trees |
| 7) | Introduction to Applied Neuroscience Models |
| Required/Recommended Readings | None | ||||||||||||
| Teaching Methods | Flipped classroom/Exercise/Laboratory/Active learning | ||||||||||||
| Homework and Projects | Homework ve Project | ||||||||||||
| Laboratory Work | None | ||||||||||||
| Computer Use | Required | ||||||||||||
| Other Activities | None | ||||||||||||
| Assessment Methods |
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| Course Administration |
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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 | 3 | 5 | 2 | 140 | ||
| Homework Assignments | 1 | 30 | 2 | 1 | 33 | ||
| Total Workload | 173 | ||||||
| Total Workload/25 | 6.9 | ||||||
| ECTS | 7.5 | ||||||