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 547 | ||||
| Course Title in English | Web Analytics | ||||
| Course Title in Turkish | Web Analitiği | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom | ||||
| Level of Course | Intermediate | ||||
| Semester | Spring | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 179 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 | This course will provide insight into the basics of analytics and optimization to explore of user experience and focuses on understanding customers’ behavior by using data gathering from websites or mobile applications. Also the course contains optimizing end-customer behavior by using testing/optimization tools or investigate the bottlenecks of customers when using websites or mobile applications. | ||||
| Course Description in Turkish | Bu ders analitik & optimizasyon temelleri ve araçları ile kullanıcı davranışlarının analizini inceleyen bir ders olarak tasarlanmıştır. Temel olarak kullanıcı davranış ve tecrübelerine odaklanarak website veya mobil uygulamaların optimizasyonunu temel alır. Ayrıca kullanıcı testleri (AB Test) ile website ve/veya mobil uygulamaları daha iyi hale getirmeye ya da kullanıcıların website ve/veya mobil uygulamalarda yaşadığı zorlukları çözmeye odaklanır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Implementation analytics tools (technical) 2) Understand user journey based on analytics data 3) Analyze metrics, events, dimensions of analytics data 4) AB Testing basics and improve user experience based on website/mobile application goals by using AB Tests |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 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. | H | |
| 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. | H | |
| 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. | H | |
| 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. | N | |
| 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 | Spring |
| Name of Instructor | Öğr. Gör. SERCAN AKKAŞ |
Course Contents
| Week | Subject |
| 1) | Introduction to Web Analytics (Analytics 101) |
| 2) | Introduction to Web Analytics (Analytics 101) |
| 3) | Analytics Implementation & Tools & Basics |
| 4) | Analytics Implementation & Tools & Basics |
| 5) | Segmentation & Funnels |
| 6) | Segmentation & Funnels |
| 7) | Analytics Reports & 3rd Party Tools |
| 8) | Analytics Reports & 3rd Party Tools |
| 9) | Analytics Case Studies & Analysis |
| 10) | Analytics Case Studies & Analysis |
| 11) | AB Testing Basics & Test Prioritization & Test Results Analysis |
| 12) | AB Testing Basics & Test Prioritization & Test Results Analysis |
| 13) | AB Testing Cases |
| 14) | AB Testing Cases |
| 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 Analytics & AB Testing Tools at the laboratory hours | ||||||||||||||||||
| Computer Use | Students will apply the methods they learned using Analytics & AB Testing Tools at the laboratory hours | ||||||||||||||||||
| Other Activities | None | ||||||||||||||||||
| Assessment Methods |
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| Course Administration |
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 | 3 | 3 | 84 | |||
| Application | 4 | 4 | 16 | ||||
| Project | 1 | 40 | 40 | ||||
| Homework Assignments | 6 | 4 | 24 | ||||
| Quiz(zes) | 5 | 3 | 0.5 | 17.5 | |||
| Total Workload | 181.5 | ||||||
| Total Workload/25 | 7.3 | ||||||
| ECTS | 7.5 | ||||||