Code | Name of the Course Unit | Semester | In-Class Hours (T+P) | Credit | ECTS Credit |
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YBS316 | DATA ANALYTICS II | 6 | 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 | |
Course Prerequisite | No |
OBJECTIVES AND CONTENTS |
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Objectives of the Course Unit: | This course aims to enable students to develop basic algorithms in order to solve different types of problems and to teach the basic structures of programming and programming in a computerized environment; thus, it aims to make students think like a data scientist. |
Contents of the Course Unit: | The content of the course is the concepts of algorithms and programming, variables, data types, input and output statements, arrays, lists, dictionaries, functions, file operations and the use of Python as a data analytics tool. |
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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Develop algorithm for solving problems encountered in daily life. {develop} |
Evaluate the functions of the Python programming language.{evaluate} |
Design algorithms with Python programming language. {design} |
Test the usability of algorithms.{testing} |
Apply machine learning design to create solutions that meet the needs identified taking into account global, cultural, social, environmental and economic factors, as well as public health, safety and well-being.{Application} |
WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY |
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Week | Preparatory | Topics(Subjects) | Method |
1 | - | Data, Information, Knowledge Concepts | Lecture & Studying on Sample & Practice |
2 | Studying on Sample, Assignment | Explaining R infrastructure and its installation | Lecture & Studying on Sample & Practice |
3 | Studying on Sample, Assignment | Loop structure and its functions | Lecture & Studying on Sample & Practice |
4 | Studying on Sample, Assignment | Data types (structural, non-structural nominal, ordinal data) | Lecture & Studying on Sample & Practice |
5 | Studying on Sample, Assignment | Basic statistical calculation and visualisation | Lecture & Studying on Sample & Practice |
6 | Studying on Sample, Assignment | Data pre-processing process | Lecture & Studying on Sample & Practice |
7 | Studying on Sample, Assignment | Principal Component Analysis | Lecture & Studying on Sample & Practice |
8 | - | MID-TERM EXAM | - |
9 | Studying on Sample, Assignment | Regression analysis | Lecture & Studying on Sample & Practice |
10 | Studying on Sample, Assignment | Hypothesis tests | Lecture & Studying on Sample & Practice |
11 | Studying on Sample, Assignment | Decision trees | Lecture & Studying on Sample & Practice |
12 | Studying on Sample, Assignment | Choosing Model and its methods | Lecture & Studying on Sample & Practice |
13 | Studying on Sample, Assignment | Logistic Regression | Lecture & Studying on Sample & Practice |
14 | Studying on Sample, Assignment | Practice | Studying on Sample & Practice |
15 | Studying on Sample, Assignment | Practice | Studying on Sample & Practice |
16 | - | FINAL EXAM | - |
17 | - | FINAL EXAM | - |
SOURCE MATERIALS & RECOMMENDED READING |
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Gursakal, N. (2014). Istatistikte R ile Programlama. |
Arslan, I. (2015). R ile Istatistiksel Programlama. Pusula Publications, Istanbul. |
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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4 | |||||
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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5 |
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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5 | |||||
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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4 | |||||
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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3 | |||||
3 |
Share the analyzes and obtained results with colleagues.
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3 | |||||
4 |
Cooperate with colleagues at international level with the help of foreign language competency.
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4 |
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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4 | |||||
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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3 |
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 | 7 | 4 | 28 |
Laboratory | 0 | 0 | 0 |
Reading | 0 | 0 | 0 |
Assignment (Homework) | 0 | 0 | 0 |
Project Work | 0 | 0 | 0 |
Seminar | 0 | 0 | 0 |
Internship | 0 | 0 | 0 |
Technical Visit | 0 | 0 | 0 |
Web Based Learning | 0 | 0 | 0 |
Implementation/Application/Practice | 14 | 2 | 28 |
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 | 0 | 0 | 0 |
Final Exam | 1 | 1 | 1 |
Preparation for the Final Exam | 7 | 5 | 35 |
Mid-Term Exam | 1 | 1 | 1 |
Preparation for the Mid-Term Exam | 7 | 4 | 28 |
Short Exam | 0 | 0 | 0 |
Preparation for the Short Exam | 0 | 0 | 0 |
TOTAL | 64 | 0 | 202 |
Total Workload of the Course Unit | 202 | ||
Workload (h) / 25.5 | 7,9 | ||
ECTS Credits allocated for the Course Unit | 8,0 |