Applications of Data Science
gonay@gsu.edu.tr
Content
Objective
The aim of this course is to introduce mathematical tools and applications that can be used to generate knowledge from data. The aim of this course is to examine the basic statistical concepts that will be used to define the data through case studies.
Course Content
Data Science: Technologies, mathematical tools and technologies. Basic statistical concepts to define the data. Sampling and measurement. Calculations for the sample based on the sample. Inferential statistics. Supervised learning. Regression analysis. Applications from business life.
Course Learning Outcomes
To understand the basic concepts and methods of data science and applications to gain the ability to see the solution. To gain the ability to understand the basic methods and applications of data science.
Teaching and Learning Methods
Lesson, case studies, data applications and exercises
References
Foundations of Data Science: Avrim Blum, John Hopcroft, and Ravindran Kannan
An Introduction to Statical Learning with Applications: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Weekly Contents
| Week | Weekly Contents |
|---|---|
| 1 | Introduction to Data Science Define data with tables and graphs |
| 2 | Introduction to Statistical Methodology |
| 3 | Sampling and Measurement. |
| 4 | Artificial Intelligence and Machine Learning Applications, Case analysis I-Betting Sites |
| 5 | Data center, variability, position measurement |
| 6 | Statistical Inference: Estimation and Correlation Analysis |
| 7 | Midterm exam |
| 8 | Introduction to Data Science Computer Technologies, Case study II-Medicine and Biology |
| 9 | Case study III-Artificial Intelligence Solutions in Banking |
| 10 | Regression Methods |
| 11 | Case study IV-Database Formation Process in Banking |
| 12 | Case study V: Evaluation of banking sector based data models |
| 13 | Case study VI: Data analysis in the field of insurance: how to prepare a motor insurance / house insurance tariff and SAS applications |
| 14 | Case study VII: Artificial Intelligence in 50 questions |
Assessment System
| Activities | Number | Contribution |
|---|---|---|
| Contribution of in-term studies to overall grade | 2 | 60 |
| Contribution of final exam to overall grade | 1 | 40 |
| Total | 3 | 100 |
| Activities | Number | Contribution |
|---|---|---|
| Presentation | 1 | 20 |
| Midterm Examinations (including preparation) | 1 | 40 |
| Total | 2 | 60 |
ECTS
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 2 | 28 | 56 |
| Working Hours out of Class | 1 | 5 | 5 |
| Presentation | 1 | 2 | 2 |
| Midterm Examinations (including preparation) | 1 | 4 | 4 |
| Final Examinations (including preparation) | 1 | 7 | 7 |
| Seminar | 7 | 2 | 14 |
| Total Workload | 88 | ||
| Total Workload / 25 | 3.52 | ||
| Credits ECTS | 4 | ||