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Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Computer Programming
Course Details
Course Code Course Name Year Period Semester T+A+L Credit ECTS
03831194 Data Analysis 2 Autumn 3 2+1+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 Lect. Özlem AKARÇAY PERVİN
Instructor(s) Lect. Özlem AKARÇAY PERVİN
Instructor Assistant(s) -
Course Instructor(s)
Name and Surname Room E-Mail Address Internal Meeting Hours
Lect. Özlem AKARÇAY PERVİN TSMYO-T213 [email protected] 7916
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 X
Specialization / Field Courses X
Support Courses X
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
P3 He/She follows current developments and practices in his profession and uses them effectively. 5
P9 It has social, scientific, cultural and ethical values in the stages of collecting data related to its field, applying it and announcing the results. 4
Course Learning Outcomes
Upon the successful completion of this course, students will be able to:
No Learning Outcomes Outcome Relationship Measurement Method **
O1 Knows current techniques for data analysis. P.3.1 1
O2 Must know and use current software development platforms. P.3.2 1
O3 Follows ethical standards in data collection and analysis P.9.2 1
** 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 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 1 40 (%)
Final Exam 1 60 (%)
Total 100 (%)
ECTS / Working Load Table
Quantity Duration Total Work Load
Course Week Number and Time 14 3 42
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 1 14
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 P3 P9
O1 Knows current techniques for data analysis. 5 -
O2 Must know and use current software development platforms. 5 -
O3 Follows ethical standards in data collection and analysis - 5