BÄ°S662 - DATA SCIENCE IN HEALTH
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| DATA SCIENCE IN HEALTH | BÄ°S662 | 2nd Semester | 3 | 0 | 3 | 7 |
| Prequisites | Not having prequisites | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion Project Design/Management | |||||
| Instructor (s) | Assist. Prof. Osman DAÄž(PhD), Professor Erdem KARABULUT (PhD), Lecturer H. YaÄŸmur ZENGÄ°N(PhD) | |||||
| Course objective | To teach the basic concepts of R software, how to read data and data manipulation and also to provide students with the ability to visualize health data by applying advanced data visualization tools and interactive graphs. To teach students how to write their own functions. To teach the students reproducible research tools on R software to ensure reproduction of the research report in case of data change. To model health data with machine learning algorithms and compare the model performances. To teach the students the new developing technologies to develop a web-interface especially for non - R users. | |||||
| Learning outcomes |
| |||||
| Course Content | 1. Data import and basic conceptual expressions 2. Data manipulation 3. Data visualization with advance techniques 4. Interactive graphics 5. Writing R functions 6. Reproducible research 7. Modelling the health data with machine learning algorithms and performance comparison of models. 8. Development of web-based software in R with the new technologies | |||||
| References | 1. Mailund, T. (2017). Beginning Data Science in R, Apress, Berkeley, California. 2. Peng, R.D. (2015). Report Writing for Data Science in R, available at https://books.google.com.tr. 3. Peng, Roger D. (2016). R programming for data science. Leanpub. 4. Robert, C.P., Casella, G. (2010). Introducing Monte Carlo Methods with R, Springer. 5. Caffo, B. (2015). Regression models for data science in R. A companion book for the Coursera Regression Models class. 6. Rizzo, M.L. (2008) Statistical Computing with R, Boca Raton : Chapman & Hall/CRC. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Basic conceptual expressions and data import in R |
| Week 2 | Data manipulation in health: Studying on big data |
| Week 3 | Data visualization with advance techniques |
| Week 4 | Interactive graphics |
| Week 5 | "Built-in" functions in R |
| Week 6 | Writing R functions for different objectives |
| Week 7 | Generalization of the written function for different scenarios |
| Week 8 | Mid-term exam |
| Week 9 | Introduction to R Markdown |
| Week 10 | Reproducible research |
| Week 11 | Modelling the health data with machine learning algorithms and performance comparison of models |
| Week 12 | Shiny Platform |
| Week 13 | Developing web-based software for clinical decision support systems |
| Week 14 | Term project presentation |
| Week 15 | Preparation to final exam |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | ||
| Laboratory | ||
| Application | ||
| Field activities | ||
| Specific practical training | ||
| Assignments | 4 | 15 |
| Presentation | 1 | 5 |
| Project | 1 | 15 |
| Seminar | ||
| Midterms | 1 | 15 |
| Final exam | 1 | 50 |
| Total | ||
| Percentage of semester activities contributing grade succes | 7 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | ||
| Application | 0 | ||
| Specific practical training | 0 | ||
| Field activities | 0 | ||
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 2 | 28 |
| Presentation / Seminar Preparation | 1 | 10 | 10 |
| Project | 1 | 30 | 30 |
| Homework assignment | 4 | 10 | 40 |
| Midterms (Study duration) | 1 | 25 | 25 |
| Final Exam (Study duration) | 1 | 35 | 35 |
| Total Workload | 36 | 115 | 210 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. A specialist with a graduate diploma in biostatistics: Has the knowledge to lead research planning, execution, and finalization, staying updated on literature and current studies. | X | ||||
| 2. Critically evaluates studies and scientific papers in presentations, courses, seminars, and conferences, encouraging a critical perspective. | X | ||||
| 3. Has the sufficient theoretical and practical knowledge of statistics to determine the appropriate statistical analysis and to grasp the results correctly. | X | ||||
| 4. Be proficient in computer use and statistical software, ensuring data suitability and recommending solutions for data management and analysis methods. | X | ||||
| 5. Effectively communicates analysis issues through active participation in discussions, exchanging information with the advisor, and presenting seminars. | X | ||||
| 6. Provides method suggestions in consultancy, does research planning, prepares research reports. | X | ||||
| 7. Maintains scientific accuracy and ethical values, remaining careful against any conscious or unconscious biases throughout the study. | X | ||||
| 8. Supports a counseling service under faculty supervision, may handle independent projects, and participates in conferences, presenting papers or posters with the academic advisor. | X | ||||
| 9. Be ready for multidisciplinary studies, collaborating professionally in group settings and gains the ability to assign individuals in the group. | X | ||||
| 10. Integrates diverse disciplines to analyze and synthesize information, offering solutions. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest