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| YZLM5130 | Data Science and Analytics | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of SOFTWARE ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Doç. Dr. Özcan ÖZYURT | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course aims to teach students basic and advanced concepts in data science and analytics. It aims to provide students with the skills to collect, process, analyze and interpret data using the Python programming language. By working on real-world data, students will develop practical application and analytical thinking skills that will support data-driven decision-making processes. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Understands the processes of collecting, processing, analyzing and interpreting data | 1,4 | 1,3, | | PO - 2 : | Knows how to perform data cleaning, preprocessing and numerical operations | 1,4 | 1,3, | | PO - 3 : | Can visualize data | 1,4 | 1,3, | | PO - 4 : | Understands data sets and can perform statistical and exploratory data analysis | 1,4 | 1,3, | | PO - 5 : | It can perform analyzes in areas such as machine learning, deep learning, time series analysis, text mining and natural language processing. | 1,4 | 1,3, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Introduction to Data Science and Analytics, Python Programming Fundamentals, Data Cleaning and Preprocessing with Pandas, Numerical Operations with Numpy, Data Visualization with Matplotlib and Seaborn, Understanding Data Sets, Statistical Analysis, Introduction to Machine Learning with Scikit-learn, Classification, Regression, Clustering , Basic Deep Learning with Tensorflow and Keras, Time Series Analysis with Pandas and Statsmodels, Text Preprocessing with Python, Topic Modeling, Sentiment Analysis, Big Data Processing and Analysis with PySpark, Good Practices for Data Science Projects, Real World Data Science Projects |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction and Python Programming Fundamentals | | | Week 2 | Data Cleansing and Preprocessing with Pandas | | | Week 3 | Numerical Operations with Numpy | | | Week 4 | Data Visualization with Matplotlib and Seaborn | | | Week 5 | Exploratory Data Analysis | | | Week 6 | Introduction to Machine Learning with Scikit-learn | | | Week 7 | Classification Algorithms | | | Week 8 | Regression Algorithms | | | Week 9 | Midterm exam | | | Week 10 | Clustering Techniques | | | Week 11 | Basic Deep Learning with Tensorflow and Keras | | | Week 12 | Time Series Analysis | | | Week 13 | Text Preprocessing and NLP with Python | | | Week 14 | Big Data Processing and Analysis with PySpark | | | Week 15 | Good Practices and Real World Applications for Data Science Projects (Assignment presentations) | | | Week 16 | Final exam | | | |
| 1 | McKinney, W. 2022, Python for Data Analysis, 3E, O'Reilly, USA.
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| 1 | Geron, A. 2019, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, O'Relly, USA. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | | 30 | | Homework/Assignment/Term-paper | 15 | | | 20 | | End-of-term exam | 16 | | | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 14 | 3 | 42 | | Sınıf dışı çalışma | 4 | 14 | 56 | | Arasınav için hazırlık | 12 | 1 | 12 | | Arasınav | 2 | 1 | 2 | | Ödev | 5 | 14 | 70 | | Dönem sonu sınavı için hazırlık | 15 | 1 | 15 | | Dönem sonu sınavı | 3 | 1 | 3 | | Total work load | | | 200 |
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