| Course Objectives: |
This course covers fundamental data analysis techniques used to perform analysis on structured and unstructured data. Students are introduced to data preprocessing, exploratory data analysis (EDA), visualization, hypothesis testing, and basic modeling techniques. Data analysis applications are conducted using the Python programming language and SPSS. By the end of the course, students will be able to generate meaningful information and insights from raw data. |
| Course Content: |
This course covers the fundamental data analysis concepts and methods necessary for analyzing structured and unstructured data. The course covers data collection and preprocessing techniques, missing and outlier analysis, and data cleaning. Exploratory Data Analysis (EDA) focuses on summarizing data, calculating statistical metrics, and visualization methods (histograms, box plots, scatter plots, etc.).Furthermore, basic statistical modeling methods such as hypothesis testing, correlation, and regression analysis are covered. Applications are performed using the Python programming language and SPSS software.
By the end of the course, students will have the ability to analyze raw data and transform it into meaningful information and interpretable insights. |
Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:
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| Knowledge
(Described as Theoritical and/or Factual Knowledge.)
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1) Be able to apply basic statistical tests and interpret results.
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| Skills
(Describe as Cognitive and/or Practical Skills.)
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| Competences
(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)
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1) Can analyze data sets obtained from the real world.
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2) It can perform data cleaning, transformation and visualization operations.
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3) It can support decision-making processes with findings obtained from data analysis.
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| Week |
Subject |
Materials Sharing * |
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Related Preparation |
Further Study |
| 1) |
Descriptive Statistics Application |
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| 2) |
Descriptive Statistics Application |
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| 3) |
Descriptive Statistics Application |
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| 4) |
Basic Statistics Application |
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| 5) |
Basic Statistics Application |
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| 6) |
Temel İstatistik Uygulama |
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| 7) |
Hypothesis Tests |
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| 8) |
Midterm exam |
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| 9) |
Parametric Data Analysis |
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| 10) |
Parametric Data Analysis |
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| 11) |
Parametric Data Analysis |
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| 12) |
Non-Parametric Data Analysis |
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| 13) |
Non-Parametric Data Analysis |
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| 14) |
Non-Parametric Data Analysis |
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Programme Learning Outcomes |
Contribution Level (from 1 to 5) |
| 1) |
It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results. |
5 |
| 2) |
It explains the principles of algorithm design and develops software for solving problems using at least one programming language. |
1 |
| 3) |
It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data. |
1 |
| 4) |
Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations. |
1 |
| 5) |
They apply natural language processing techniques to text data and develop basic NLP-based applications. |
1 |
| 6) |
It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools. |
1 |
| 7) |
It creates data-driven decision models using decision support systems. |
1 |
| 8) |
It develops optimization models and produces solutions for industrial and sectoral problems. |
1 |
| 9) |
In professional practice, we operate within the framework of ethical principles, data security, and social responsibility. |
1 |
| 10) |
They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness. |
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| WORKLOAD OF TEACHING & LEARNING ACTIVITIES |
| Teaching & Learning Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Course |
14 |
3 |
42 |
| Laboratory |
0 |
0 |
0 |
| Application |
14 |
3 |
42 |
| Special Course Internship (Work Placement) |
0 |
0 |
0 |
| Field Work |
0 |
0 |
0 |
| Study Hours Out of Class |
0 |
0 |
0 |
| Presentations / Seminar |
0 |
0 |
0 |
| Project |
0 |
0 |
0 |
| Homework Assignments |
1 |
10 |
10 |
| Total Workload of Teaching & Learning Activities |
- |
- |
94 |
| WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES |
| Assesment & Evaluation Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Quizzes |
1 |
2 |
2 |
| Midterms |
1 |
20 |
20 |
| Semester Final Exam |
1 |
30 |
30 |
| Total Workload of Assesment & Evaluation Activities |
- |
- |
52 |
| TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) |
146 |
| ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) |
6 |