Midwifery
Course Details
KTO KARATAY UNIVERSITY
Faculty of Health Sciences
Programme of Midwifery
Course Details
Faculty of Health Sciences
Programme of Midwifery
Course Details
Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
---|---|---|---|---|---|---|---|
99901055 | Data Analysis and Applications | 1 | Autumn | 1 | 2+0+0 | 3 | 3 |
Course Type | Elective |
Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
Course Language | Turkish |
Methods and Techniques | - |
Mode of Delivery | Face to Face |
Prerequisites | - |
Coordinator | - |
Instructor(s) | Asst. Prof. Ali Osman ÇIBIKDİKEN |
Instructor Assistant(s) | - |
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
Throughout this course, individuals will develop a comprehensive understanding of utilizing Python to analyze a diverse range of data. This will include gaining proficiency in data preparation for analysis, executing basic statistical analysis, creating impactful data visualizations, and forecasting future trends derived from data.
Contribution of the Course to Field Teaching
Basic Vocational Courses | |
Specialization / Field Courses | |
Support Courses | |
Transferable Skills Courses | |
Humanities, Communication and Management Skills Courses |
Weekly Detailed Course Contents
Week | Topics |
---|---|
1 | Data Definitions and Analysis Techniques: What is data, Relevance of data, Types of data, Data formats, Data source, What is data analysis. |
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 |
Textbook or Material
Resources | [2] David Taieb ,"Data Analysis with Python: A Modern Approach "1st Edition, Packt Publishing |
[2] 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 | - | - |
Seminar | - | - |
Quiz | - | - |
Listening | - | - |
Midterms | - | - |
Final Exam | - | - |
Total | 0 (%) |
ECTS / Working Load Table
Quantity | Duration | Total Work Load | |
---|---|---|---|
Course Week Number and Time | 0 | 0 | 0 |
Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 0 | 0 | 0 |
Midterms | 0 | 0 | 0 |
Quiz | 0 | 0 | 0 |
Homework | 0 | 0 | 0 |
Practice | 0 | 0 | 0 |
Laboratory | 0 | 0 | 0 |
Project | 0 | 0 | 0 |
Workshop | 0 | 0 | 0 |
Presentation/Seminar Preparation | 0 | 0 | 0 |
Fieldwork | 0 | 0 | 0 |
Final Exam | 0 | 0 | 0 |
Other | 0 | 0 | 0 |
Total Work Load: | 0 | ||
Total Work Load / 30 | 0 | ||
Course ECTS Credits: | 0 |