Information Package / Course Catalogue
| Business Administration | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Faculty of Engineering | ||||
| Course Code | IE 332 | ||||
| Course Title in English | Exploratory Data Analytics | ||||
| Course Title in Turkish | Keşifsel Veri Analizi | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom,Lecture | ||||
| Level of Course | Advanced | ||||
| Semester | Spring | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 152 hours per semester | ||||
| Number of Credits | 6 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | Basic principles of computational methods and introductory level probability/statistics | ||||
| Co-requisites | None | ||||
| Registration Restrictions | none | ||||
| Overall Educational Objective | To learn the basics of 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 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) summarize data using statistical methods; 2) draw conclusions and/or make predictions using data analytics; 3) apply linear regression models; 4) understand recent analytical trends such as text mining, recommendation systems; 5) communicate data analysis results in a clear and concise manner in written and oral form. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 1) Has a broad foundation and intellectual awareness with exposure to mathematics, history, economics, and social sciences | |||||
| 2) Demonstrates knowledge and skills in different functional areas of business (accounting, finance, operations, marketing, strategy, and organization) and an understanding of their interactions within various industry sectors | |||||
| 3) Applies theoretical knowledge as well as creative, analytical, and critical thinking to manage complex technical or professional activities or projects | |||||
| 4) Exhibits an understanding of global, environmental, economic, legal, and regulatory contexts for business sustainability | |||||
| 5) Demonstrates individual and professional ethical behavior and social responsibility | |||||
| 6) Demonstrates responsiveness to ethnic, cultural, and gender diversity values and issues | |||||
| 7) Uses written and spoken English effectively (at least CEFR B2 level) to communicate information, ideas, problems, and solutions | |||||
| 8) Demonstrates skills in data and information acquisition, analysis, interpretation, and reporting | |||||
| 9) Displays computer proficiency to support problem solving and decision-making | |||||
| 10) Demonstrates teamwork, leadership, and entrepreneurial skills | |||||
| 11) Displays learning skills necessary for further study with a high degree of autonomy |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Has a broad foundation and intellectual awareness with exposure to mathematics, history, economics, and social sciences | H | Exam |
| 2) | Demonstrates knowledge and skills in different functional areas of business (accounting, finance, operations, marketing, strategy, and organization) and an understanding of their interactions within various industry sectors | H | Exam |
| 3) | Applies theoretical knowledge as well as creative, analytical, and critical thinking to manage complex technical or professional activities or projects | S | Project |
| 4) | Exhibits an understanding of global, environmental, economic, legal, and regulatory contexts for business sustainability | N | |
| 5) | Demonstrates individual and professional ethical behavior and social responsibility | N | |
| 6) | Demonstrates responsiveness to ethnic, cultural, and gender diversity values and issues | H | Exam |
| 7) | Uses written and spoken English effectively (at least CEFR B2 level) to communicate information, ideas, problems, and solutions | H | Exam |
| 8) | Demonstrates skills in data and information acquisition, analysis, interpretation, and reporting | N | |
| 9) | Displays computer proficiency to support problem solving and decision-making | N | |
| 10) | Demonstrates teamwork, leadership, and entrepreneurial skills | N | |
| 11) | Displays learning skills necessary for further study with a high degree of autonomy | N |
| Prepared by and Date | ÖZGÜR ÖZLÜK , September 2019 |
| Course Coordinator | SEMRA AĞRALI |
| Semester | Spring |
| Name of Instructor | Öğr. Gör. HANDE OZOGUZ |
Course Contents
| Week | Subject |
| 1) | Introduction to Data Science |
| 2) | Basics of R |
| 3) | Basics of R |
| 4) | Data Analysis (one variable) |
| 5) | Data Analysis (two variable) |
| 6) | Data Analysis (multi variable) |
| 7) | Linear Regression |
| 8) | Linear Regression |
| 9) | Linear Regression |
| 10) | Linear Regression |
| 11) | Image Processing Basics |
| 12) | Text Mining Basics |
| 13) | Search Engines Basics |
| 14) | Recommendation Engines Basics |
| 15) | Final Exam/Project/Presentation period |
| 16) | Final Exam/Project/Presentation period |
| Required/Recommended Readings | none | |||||||||||||||
| Teaching Methods | Lectures/contact hours using “flipped classroom” as an active learning technique | |||||||||||||||
| 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 |
ozluko@mef.edu.tr none Instructor’s office and phone number: 5th Floor office hours: Thursdays 16:20-17:20 email address: ozluko@mef.edu.tr Rules for attendance: none Rules for late submission of assignments: It will be discounted 20/100 by each delayed day. Rules for missing a midterm: Provided that proper justification evidence is presented, each missed midterm exam will be given the grade of the final exam. There will be no make-up exams Missing a final: Faculty regulations A reminder of proper classroom behavior, code of student conduct: YÖK Regulations Statement on plagiarism: YÖK Regulations |
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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 | 1 | 3 | 2 | 84 | ||
| Project | 1 | 10 | 10 | 20 | |||
| Quiz(zes) | 3 | 3 | 1 | 12 | |||
| Midterm(s) | 1 | 12 | 2 | 14 | |||
| Final Examination | 1 | 20 | 2 | 22 | |||
| Total Workload | 152 | ||||||
| Total Workload/25 | 6.1 | ||||||
| ECTS | 6 | ||||||