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 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) 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. | H | |
| 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. | S | |
| 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 | Fall |
| Name of Instructor | Öğr. Gör. BERK ORBAY |
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 | ||||||