Code | Name of the Course Unit | Semester | In-Class Hours (T+P) | Credit | ECTS Credit |
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YBS317 | DATA ANALYTICS I | 5 | 3 | 3 | 8 |
GENERAL INFORMATION |
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Language of Instruction : | Turkish |
Level of the Course Unit : | BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle |
Type of the Course : | Compulsory |
Mode of Delivery of the Course Unit | - |
Coordinator of the Course Unit | Assist.Prof. DİDEM TETİK KÜÇÜKELÇİ |
Instructor(s) of the Course Unit | Assist.Prof. SÜREYYA İMRE BIYIKLI |
Course Prerequisite | No |
OBJECTIVES AND CONTENTS |
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Objectives of the Course Unit: | The aim of this course is to prepare students to collect, define and analyze data and to use advanced statistical tools to make decisions about the field of business. In the scope of the course, data mining algorithms based mainly on prediction will be included and business applications will be made with R language. It is aimed to provide students with problem solving and project development habits with a systematic approach. |
Contents of the Course Unit: | Contents of the course include the subjects such as algorithms and programming concepts, variables, data types, input and output statements, series, lists, dictionaries, functions, file processing. |
KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to) |
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Use different file formats for data input and output. {use} |
Analyze statistical analyses in R, which is an open source platform. {analysis} |
Evaluate the accuracy of the analysed data.{evaluate} |
Develop his/her own analysis technique himself/herself.{develop} |
Assess data extraction and data reduction techniques.{assess} |
WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY |
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Week | Preparatory | Topics(Subjects) | Method |
1 | - | History and development of computers | Lecture & Discussion & Practice |
2 | Literature Review, Assignment | Main components of computer: hardware and software | Lecture & Discussion & Practice |
3 | Literature Review, Assignment | Algorithms and programming concepts | Lecture & Discussion & Practice |
4 | Literature Review, Assignment | Variables, Types of Data, Input and Output Expressions | Lecture & Discussion & Practice |
5 | Literature Review, Assignment | Conditional Expressions | Lecture & Discussion & Practice |
6 | Literature Review, Assignment | Loop structures | Lecture & Discussion & Practice |
7 | Literature Review, Assignment | Arrays: Clusters, Lists, Words | Lecture & Discussion & Practice |
8 | - | MID-TERM EXAM | - |
9 | Literature Review, Assignment | Revision before midterm exam | Lecture & Discussion & Practice |
10 | Literature Review, Assignment | Functions -I | Lecture & Discussion & Practice |
11 | Literature Review, Assignment | Functions -II | Lecture & Discussion & Practice |
12 | Literature Review, Assignment | Strings | Lecture & Discussion & Practice |
13 | Literature Review, Assignment | Files | Lecture & Discussion & Practice |
14 | Literature Review, Assignment | Graph drawing | Lecture & Discussion & Practice |
15 | Literature Review, Assignment | Revision before final exam | Lecture & Discussion & Practice |
16 | - | FINAL EXAM | - |
17 | - | FINAL EXAM | - |
SOURCE MATERIALS & RECOMMENDED READING |
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Karacay, T. (2016). Python 3: Veri Yapilari. Seckin Publications. |
Aksoy, A. (2016). Yeni Baslayanlar Icin Python. Abakus Publications. |
ASSESSMENT |
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Assessment & Grading of In-Term Activities | Number of Activities | Degree of Contribution (%) | Description |
Level of Contribution | |||||
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0 | 1 | 2 | 3 | 4 | 5 |
KNOWLEDGE |
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Theoretical |
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Programme Learning Outcomes | Level of Contribution | ||||||
0 | 1 | 2 | 3 | 4 | 5 | ||
1 |
Define concepts such as management, manager and leader.
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5 | |||||
2 |
Analyze the accuracy, reliability and validity of the new information obtained from the data.
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5 |
KNOWLEDGE |
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Factual |
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Programme Learning Outcomes | Level of Contribution | ||||||
0 | 1 | 2 | 3 | 4 | 5 | ||
1 |
Report the obtained data.
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5 | |||||
2 |
Prepare software and projects related with the field.
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5 |
SKILLS |
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Cognitive |
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Programme Learning Outcomes | Level of Contribution | ||||||
0 | 1 | 2 | 3 | 4 | 5 | ||
1 |
Use the appropriate resources for data analysis related with the field.
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5 | |||||
2 |
Analyze the work processes.
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4 |
SKILLS |
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Practical |
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Programme Learning Outcomes | Level of Contribution | ||||||
0 | 1 | 2 | 3 | 4 | 5 | ||
1 |
Manage projects as part of a team.
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4 | |||||
2 |
Apply the material, techniques and analyzes in relation with the subject for project and work flows.
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5 |
OCCUPATIONAL |
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Autonomy & Responsibility |
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Programme Learning Outcomes | Level of Contribution | ||||||
0 | 1 | 2 | 3 | 4 | 5 | ||
1 |
Fulfill responsibility with a focus on result in individual and team studies.
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5 |
OCCUPATIONAL |
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Learning to Learn |
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Programme Learning Outcomes | Level of Contribution | ||||||
0 | 1 | 2 | 3 | 4 | 5 | ||
1 |
Recognizes what he/she knows about his/her field or not.
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5 | |||||
2 |
Act the theoretical knowledge in real life with learning to learn approach.
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5 | |||||
3 |
Apply different methods and techniques with an innovative approach in his/her research.
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5 |
OCCUPATIONAL |
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Communication & Social |
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Programme Learning Outcomes | Level of Contribution | ||||||
0 | 1 | 2 | 3 | 4 | 5 | ||
1 |
Apply the results obtained in accordance with voluntarism and social responsibility projects.
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5 | |||||
2 |
Establish a healthy contact with colleagues
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4 | |||||
3 |
Share the analyzes and obtained results with colleagues.
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4 | |||||
4 |
Cooperate with colleagues at international level with the help of foreign language competency.
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3 |
OCCUPATIONAL |
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Occupational and/or Vocational |
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Programme Learning Outcomes | Level of Contribution | ||||||
0 | 1 | 2 | 3 | 4 | 5 | ||
1 |
Behave in accordance with ethical values regarding the collection, analysis and reporting of data.
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5 | |||||
2 |
Participate the design of work processes and systems with full quality.
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5 | |||||
3 |
Cooperate with other employees for the continuation of sustainability in the profession.
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4 |
WORKLOAD & ECTS CREDITS OF THE COURSE UNIT |
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Workload for Learning & Teaching Activities |
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Type of the Learning Activites | Learning Activities (# of week) | Duration (hours, h) | Workload (h) |
Lecture & In-Class Activities | 14 | 3 | 42 |
Preliminary & Further Study | 13 | 3 | 39 |
Land Surveying | 0 | 0 | 0 |
Group Work | 13 | 2 | 26 |
Laboratory | 0 | 0 | 0 |
Reading | 14 | 1 | 14 |
Assignment (Homework) | 13 | 2 | 26 |
Project Work | 1 | 23 | 23 |
Seminar | 0 | 0 | 0 |
Internship | 0 | 0 | 0 |
Technical Visit | 0 | 0 | 0 |
Web Based Learning | 0 | 0 | 0 |
Implementation/Application/Practice | 0 | 0 | 0 |
Practice at a workplace | 0 | 0 | 0 |
Occupational Activity | 0 | 0 | 0 |
Social Activity | 0 | 0 | 0 |
Thesis Work | 0 | 0 | 0 |
Field Study | 0 | 0 | 0 |
Report Writing | 1 | 10 | 10 |
Final Exam | 1 | 1 | 1 |
Preparation for the Final Exam | 1 | 14 | 14 |
Mid-Term Exam | 1 | 1 | 1 |
Preparation for the Mid-Term Exam | 1 | 7 | 7 |
Short Exam | 0 | 0 | 0 |
Preparation for the Short Exam | 0 | 0 | 0 |
TOTAL | 73 | 0 | 203 |
Total Workload of the Course Unit | 203 | ||
Workload (h) / 25.5 | 8 | ||
ECTS Credits allocated for the Course Unit | 8,0 |