Learning Outcomes |
PO |
MME |
The students who succeeded in this course: |
|
|
LO-1 |
Can use basic level analytical thinking skills when faced with problems. |
PO-1 Students who have taken the necessary courses based on their 4-year undergraduate level qualifications in the field of business administration or if they have received undergraduate education in another field can develop and deepen their knowledge in the field of accounting at the level of expertise.
|
Examination |
LO-2 |
Can solve problems using classical decision-making techniques. |
PO-1 Students who have taken the necessary courses based on their 4-year undergraduate level qualifications in the field of business administration or if they have received undergraduate education in another field can develop and deepen their knowledge in the field of accounting at the level of expertise.
|
Examination |
LO-3 |
Can exhibit a professional approach in decision-making processes regarding business processes. |
PO-1 Students who have taken the necessary courses based on their 4-year undergraduate level qualifications in the field of business administration or if they have received undergraduate education in another field can develop and deepen their knowledge in the field of accounting at the level of expertise.
|
Examination |
PO: Programme Outcomes MME:Method of measurement & Evaluation |
Course Contents |
Starting from the concept of data, this course covers the learning of data types, digital and traditional storage possibilities, classification, analysis and reporting.
|
Weekly Course Content |
Week |
Subject |
Learning Activities and Teaching Methods |
1 |
Introduction to Data Science and Its Importance Today |
Lecture, Question and Answer, Problem Solving |
2 |
Historical Development of Data Science and Types of Data |
Lecture, Question and Answer, Problem Solving |
3 |
Data Collection and Sampling Methods
|
Lecture, Question and Answer, Problem Solving |
4 |
Descriptive Statistics: Frequency Distribution, Central Tendency and Variability Measures |
Lecture, Question and Answer, Problem Solving |
5 |
Statistical Estimation and Hypothesis Testing |
Lecture, Question and Answer, Problem Solving |
6 |
Simple and Partial Correlation, Parametric Hypothesis Tests: T-Test and Z-Test |
Lecture, Question and Answer, Problem Solving |
7 |
Nonparametric Hypothesis Tests: Chi-Square, Mann Whitney U |
Lecture, Question and Answer, Problem Solving |
8 |
mid-term exam |
|
9 |
SPPS and Python Applications |
Lecture, Question and Answer, Problem Solving |
10 |
Analysis of Variance (ANOVA) and Kruskal-Wallis Tests |
Lecture, Question and Answer, Problem Solving |
11 |
Simple/Multiple Linear Regression and Correlation Analysis |
Lecture, Question and Answer, Problem Solving |
12 |
Factor and Cluster Analysis |
Lecture, Question and Answer, Problem Solving |
13 |
Introduction to Machine Learning: MATLAB and Python Application |
Lecture, Question and Answer, Problem Solving |
14 |
Introduction to Machine Learning: MATLAB and Python Application |
Lecture, Question and Answer, Problem Solving |
15 |
Data Visualization |
Lecture, Question and Answer, Problem Solving |
16 |
final exam |
|
Recommend Course Book / Supplementary Book/Reading |
1 |
Büyüköztürk, Ş. (2018). Veri Analizi El Kitabı, Pegem Akademi, 24. Baskı, Ankara. |
2 |
Kalaycı, E. (2010). SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri, Asil Yayın D., 5. Baskı, Ankara. |
Required Course instruments and materials |
Textbooks, Notebook, Blackboard |