Information Package / Course Catalogue
Data Analysis and Statistical Methods
Course Code: EMY017
Course Type: Area Elective
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 4
Objectives of the Course

The aim of this course is to enable students to gain basic data analysis and statistical interpretation skills in areas such as real estate markets, rental and sales prices, customer data, and portfolio performance.

Course Content

The course covers data types, data sources, data collection, data cleaning, descriptive statistics, tables and charts, percentage and index calculations, correlation, trend analysis, survey data, real estate sector data applications, digital analysis tools, and AI-supported analysis.

Name of Lecturer(s)
Learning Outcomes
1.The student relates data, statistics, analysis, and decision-making concepts to the real estate sector.
2.The student distinguishes quantitative-qualitative data, primary-secondary data, and real estate data sources.
3.The student gains skills in data organization, cleaning, table creation, and basic chart preparation.
4.The student interprets basic statistics such as mean, median, mode, standard deviation, percentage change, and correlation.
5.The student prepares a short analysis report using real estate price, rental value, customer demand, or portfolio performance data.
Recommended or Required Reading
1.Instructor’s Lecture Notes
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Data Analysis and Statistics: The concepts of data, information, statistics, analysis, decision-making, and the importance of data use in the real estate sector are explained.
Week 2 - Theoretical
Data Types and Data Sources: Quantitative and qualitative data, primary and secondary data, open data, market data, customer data, and real estate portfolio data are introduced.
Week 3 - Theoretical
Data Collection Methods: Surveys, interviews, observation, document review, internet research, listing data, and field data collection methods are discussed.
Week 4 - Theoretical
Data Organization and Cleaning: Missing data, incorrect data, duplicate records, data coding, table creation, and keeping organized records in real estate data are explained.
Week 5 - Theoretical
Descriptive Statistics: Basic measures such as mean, median, mode, minimum, maximum, range, and standard deviation are explained through real estate prices and rental values.
Week 6 - Theoretical
Presenting Data with Tables and Charts: Frequency tables, bar charts, line charts, pie charts, scatter plots, and visualization of real estate market data are discussed.
Week 7 - Practice
Basic Data Analysis Practice: Students are asked to prepare tables, charts, and basic statistics using sample sales price, rental value, or customer data.
Week 8 - Theoretical
Ratio, Percentage, and Index Calculations: Percentage change, increase-decrease rates, rent increases, price indices, and regional market comparisons are explained.
Week 9 - Theoretical
Relationship and Correlation Analysis: The relationship between two variables is examined through examples such as location-price, square meter-price, building age-price, and rent-return relationships.
Week 10 - Theoretical
Basic Forecasting and Trend Analysis: Price changes over time, rental trends, number of portfolios, customer demand, and basic market forecasting methods are explained.
Week 11 - Theoretical
Analysis of Survey Data: Preparing survey questions, coding responses, calculating frequency-percentage distributions, and interpreting customer satisfaction data are discussed.
Week 12 - Theoretical
Data Analysis Applications in the Real Estate Sector: Regional analysis, portfolio performance, customer demand analysis, rent-sale comparisons, and investment potential are evaluated.
Week 13 - Theoretical
Digital Tools and Artificial Intelligence in Data Analysis: Excel, Google Sheets, basic statistical software, data visualization tools, and AI-supported analysis are introduced.
Week 14 - Theoretical
General Review and Practical Data Analysis Project: The semester topics are reviewed; students are asked to prepare a short data analysis report using sample neighborhood, portfolio, or market data.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%5
Quiz1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140456
Assignment1088
Quiz101010
Midterm Examination101010
Final Examination101616
TOTAL WORKLOAD (hours)100
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
OÇ-1
5
2
5
2
1
5
2
4
1
2
1
2
3
OÇ-2
3
5
2
3
2
5
2
2
2
3
2
2
1
OÇ-3
2
2
2
3
2
5
2
3
2
5
2
3
3
OÇ-4
3
2
3
5
2
3
2
5
4
5
1
2
3
OÇ-5
2
3
2
5
2
2
3
2
5
2
3
2
3
Adnan Menderes University - Information Package / Course Catalogue
2026