Information Security Technology
Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Information Security Technology
Course Details
Trade and Industry Vocational School
Programme of Information Security Technology
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 08121103 | Data Analysis | 1 | Spring | 2 | 2+2+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Associate (Short Cycle) (TQF-HE: Level 5 / QF-EHEA: Short Cycle / EQF-LLL: Level 5) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Lect. Gizem ÇELİK |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Gizem ÇELİK | C-125 | [email protected] | 7434 | Friday 10:00-12:00 |
Course Content
In today's age of advanced technology, we are fortunate enough to have easy access to an abundance of data through the internet and social media platforms. This data serves as a highly valuable resource for professionals in a multitude of fields, including business, science, and social science. To that end, this comprehensive course is designed to equip students with the skills necessary to effectively analyze complex data using Python programming. Throughout the course, students will learn to utilize powerful tools such as Numpy, Pandas for data analysis and Seaborn, Matplotlib for data visualization, all within the user-friendly Jupyter Notebook. In addition, the course will provide students with invaluable insights on how to collect, clean, prepare, and analyze time-series data. Furthermore, students will gain a deep understanding of how to apply linear regression models to predict unknown and future values, thereby enhancing their ability to make informed decisions based on data-driven insights.
Objectives of the Course
The aim of this course is to teach students how to collect, process, analyze, and interpret data securely, accurately, and meaningfully.
Students will be able to analyze events in the field of information security (such as security breaches, log records, and network traffic) using data analysis tools and methods, and will develop analytical thinking skills to support threat detection and decision-making processes.
Students will be able to analyze events in the field of information security (such as security breaches, log records, and network traffic) using data analysis tools and methods, and will develop analytical thinking skills to support threat detection and decision-making processes.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | X |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | X |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | He/she has basic, current and practical knowledge about his/her profession. | 5 |
| P3 | Follows current developments and practices for his/her profession and uses them effectively. | 4 |
| P4 | Uses professionally relevant information technologies (software, programs, animations, etc.) effectively. | 5 |
| P5 | Has the ability to independently evaluate professional problems and issues with an analytical and critical approach and to propose solutions. | 5 |
| P6 | Can effectively present thoughts through written and verbal communication at the level of knowledge and skills and express them in an understandable manner. | 3 |
| P9 | It has social, scientific, cultural and ethical values in the stages of collecting data related to its field, its application and the announcement of its results. | 5 |
| P11 | Explains and applies data security and encryption methods. | 3 |
| P14 | Performs mathematical calculations. | 4 |
| P20 | To enable students to gain the competence to solve the problems they encounter in their academic and professional lives by using information technologies effectively and efficiently. | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | P.1.3 | ||
| O2 | P.1.6 | ||
| O3 | P.3.2 | ||
| O4 | P.3.3 | ||
| O5 | P.4.3 | ||
| O6 | P.4.5 | ||
| O7 | P.5.1 | ||
| O8 | P.14.1 | ||
| O9 | P.5.3 | ||
| O10 | P.14.5 | ||
| O11 | P.6.3 | ||
| O12 | P.9.2 | ||
| O13 | P.20.1 | ||
| O14 | P.20.5 | ||
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | https://bologna.karatay.edu.tr/tr/ders/ticaret-ve-sanayi-meslek-yuksekokulu/bilgisayar-programciligi/03831194/veri-analizi |
| 2 | Basic Analysis Techniques Statistical hypothesis generation and testing, Chi-Square test, t-Test, Analysis of variance, Correlation analysis |
| 3 | Introduction to Data Analysis Process Understanding different types of data, Introduction to Python for data analytics |
| 4 | Data Exploration and Cleaning Data collection and acquisition, Data cleaning techniques, Handling missing values and outliers, Exploratory data analysis |
| 5 | Data Preprocessing Data transformation and normalization, Feature engineering techniques, Data scaling and standardization, Handling categorical data |
| 6 | NumPy: Datatypes, Universal Functions, Indexing, Summary Methods, Sorting, Computations and Broadcasting |
| 7 | Pandas: DataFrame Basics, DataFrame Construction, DataFrame Change and Reorganization, Indexing and Access Techniques, Grouping, Pivoting, and Reshaping |
| 8 | Midterm Exam |
| 9 | Pandas: Data Manipulation, Statistics, Data Methods, Missing Data Tools |
| 10 | Week 10 Understanding Data Visualization Visualization Is Storytelling, Types of Charts, Colors, Common Mistakes, Best Practices, Reproducibility |
| 11 | Matplotlib for Data Visualization Steps for Creating a Data Visualization, Jupyter Notebooks and Matplotlib, Matplotlib Styles, Panda Series Plotting, Panda Dataframe Plotting |
| 12 | Introduction to Statistical Techniques Regression and Prediction, Classification, K-Nearest Neighbors, Clustering |
| 13 | Introduction to Machine Learning Regression and Prediction, Classification, K-Nearest Neighbors Clustering |
| 14 | Applying data analytics techniques to a real-world dataset Designing and executing a data analysis project |
| 15 | Applied Project: Data Analysis, Visualization & Modelling |
| 16 | Final Exam |
Textbook or Material
| Resources | Wes Mckinney "Python for Data Analysis", First edition, Publisher O'Reilly Media. |
| David Taieb ,"Data Analysis with Python: A Modern Approach "1st Edition, Packt Publishing |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | 1 | 30 (%) |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 30 (%) |
| Final Exam | 1 | 40 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 2 | 28 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 1 | 25 | 25 |
| Midterms | 1 | 20 | 20 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 14 | 2 | 28 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 1 | 20 | 20 |
| Final Exam | 1 | 20 | 20 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 141 | ||
| Total Work Load / 30 | 4,70 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P3 | P4 | P5 | P6 | P9 | P14 | P20 |
|---|---|---|---|---|---|---|---|---|---|
| O1 | Temel programlama mantığı ve veri yapıları hakkında bilgiye sahiptir. | 5 | - | - | - | - | - | - | - |
| O2 | Öğrenilen bilgileri uygulamalı örneklerle gösterir. | 5 | - | - | - | - | - | - | - |
| O3 | Güncel yazılım araçlarını uygular. | - | 5 | - | - | - | - | - | - |
| O4 | Karmaşık problemleri ele alır ve yaratıcı çözümler üretir. | - | 4 | - | - | - | - | - | - |
| O5 | Algoritma geliştirmeyi bilir ve algoritmaya uygun veri yapısı oluşturur. | - | - | 4 | - | - | - | - | - |
| O6 | İhtiyaç duyulan bilişim teknolojilerini araştırır, seçer ve uygun şekilde entegre eder. | - | - | 4 | - | - | - | - | - |
| O7 | Bir problemi analiz eder. | - | - | - | 5 | - | - | - | - |
| O8 | Uygun çözüm alternatiflerini üretir. | - | - | - | 4 | - | - | - | - |
| O9 | Bilgisayar ve veri bilimi analizlerini ve sonuçlarını raporlayabilir. | - | - | - | - | 5 | - | - | - |
| O10 | Veri toplama ve analizinde etik standartları takip eder. | - | - | - | - | - | 5 | - | - |
| O11 | Mesleki problemleri çözmek için gerekli olan temel matematiksel işlemleri doğru bir şekilde gerçekleştirir. | - | - | - | - | - | - | 4 | - |
| O12 | Mesleki problemlere yönelik matematiksel modeller oluşturur ve bu modelleri kullanarak çözümler üretir. | - | - | - | - | - | - | 4 | - |
| O13 | Bilgi teknolojileri ve bilgisayar sistemlerinin temel kavramlarını tanımlayabilme ve bu kavramlar arasındaki ilişkileri açıklayabilme. | - | - | - | - | - | - | - | 5 |
| O14 | Bilgisayar programlama ve algoritma mantığını temel seviyede kavrayarak basit programlar yazabilme. | - | - | - | - | - | - | - | 5 |