Syllabus ( MBG 430 )
|
|
Basic information
|
|
| Course title: |
Introduction to Biostatistics |
| Course code: |
MBG 430 |
| Lecturer: |
Assist. Prof. Pınar PİR
|
| ECTS credits: |
5 |
| GTU credits: |
3 (3-0-0) |
| Year, Semester: |
4,1, Fall |
| Level of course: |
First Cycle (Undergraduate) |
| Type of course: |
Elective
|
| Language of instruction: |
English
|
| Mode of delivery: |
Face to face
|
| Pre- and co-requisites: |
None |
| Professional practice: |
No |
| Purpose of the course: |
Introduction to Biostatistics course has two main goals: (a) provide the students with the fundamentals of statistics, (b) equip the students with statistical tools that are fundamental to bioinformatics and systems biology |
|
|
|
Learning outcomes
|
|
Upon successful completion of this course, students will be able to:
-
Have a thorough understanding of fundamentals of probability and statistics
Contribution to Program Outcomes
-
To be able to comprehend the history and nature of scientific thinking and to apply them to problems in the field.
-
To be able to work individually, make independent decisions and participate actively in multidisciplinary group studies.
Method of assessment
-
Written exam
-
Oral exam
-
Have an understanding of types of hypothesis testing and applications in life sciences
Contribution to Program Outcomes
-
To be able to comprehend the history and nature of scientific thinking and to apply them to problems in the field.
-
To be able to work individually, make independent decisions and participate actively in multidisciplinary group studies.
Method of assessment
-
Written exam
-
Homework assignment
-
Equipped with biostatistics tools essential to bioinformatics and systems biology.
Contribution to Program Outcomes
-
To be able to comprehend the history and nature of scientific thinking and to apply them to problems in the field.
-
To be able to work individually, make independent decisions and participate actively in multidisciplinary group studies.
-
To be able to drive hypotheses using existing knowledge, designing and conducting experiment for problem solving and make correct interpretation of the results obtained from the experiment.
Method of assessment
-
Written exam
-
Homework assignment
-
Aware of biased uses of biostatistics in the literature
Contribution to Program Outcomes
-
To be able to comprehend the history and nature of scientific thinking and to apply them to problems in the field.
-
To be able to apply biological concepts to individual, social, economic, technologic and environmental issues and to develop sustainable approaches for problem solving.
-
To be able to embrace academic ethical rules and to be able to act with a sense of responsibility.
Method of assessment
-
Written exam
|
|
|
Contents
|
|
| Week 1: |
Descriptive Statistics: Presenting the data from patients |
| Week 2: |
Introduction to Probability: Understanding risks of disease
Homework 1 - Quiz 1 |
| Week 3: |
Discrete Probability Distributions
Homework 2 - Quiz 2 |
| Week 4: |
Continuous Probability Distributions: Design of clinical trials
Homework 3 - Quiz 3 |
| Week 5: |
Estimation Methods
Homework 4 - Quiz 4 |
| Week 6: |
Midterm Exam 1
Hypothesis Testing: One-Sample Inference - Identifying the Differentially Transcribed Genes
Homework 5 - Quiz 5 |
| Week 7: |
Hypothesis Testing: Two-Sample Inference - Identifying the Differentially Transcribed Genes with Paired Data
Homework 6 - Quiz 6 |
| Week 8: |
Nonparametric Methods: Analysis of Omics Data With Unknown Distribution
Homework 7 - Quiz 7 |
| Week 9: |
Hypothesis Testing: Categorical Data
Homework 8 - Quiz 8 |
| Week 10: |
Regression and Correlation Methods: Analysing the correlation between protein and gene expression levels
Homework 9 - Quiz 9 |
| Week 11: |
Multisample Inference: ANOVA
Homework 10 - Quiz 10 |
| Week 12: |
Midterm Exam 2
Experimental Design and Analysis Techniques
Homework 11 - Quiz 11 |
| Week 13: |
Hypothesis Testing: Time-Dependant Data
Homework 12 - Quiz 12 |
| Week 14: |
Case Study 1: Biased use of biostatistics in literature - How not to use p-value |
| Week 15*: |
Case Study 2: Biased use of biostatistics in journals/magazines |
| Week 16*: |
Final Exam |
| Textbooks and materials: |
B. Rosner, "Fundamentals of Biostatistics", Brooks/Cole, Boston, 2010 |
| Recommended readings: |
RE. Walpole, RH. Myers, SL. Myers, K. Ye, "Probability and Statistics for Engineers and Scientists", Prentice Hall, Boston, 2012. |
|
|
* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
|
|
|
|
|
Assessment
|
|
|
| Method of assessment |
Week number |
Weight (%) |
|
| Mid-terms: |
6 and 12 |
30 |
| Other in-term studies: |
|
0 |
| Project: |
|
0 |
| Homework: |
2-13 |
20 |
| Quiz: |
2-13 |
20 |
| Final exam: |
16 |
30 |
| |
Total weight: |
(%) |
|
|
|
|
Workload
|
|
|
| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
|
| Courses (Face-to-face teaching): |
3 |
15 |
|
| Own studies outside class: |
1 |
16 |
|
| Practice, Recitation: |
0 |
0 |
|
| Homework: |
1 |
12 |
|
| Term project: |
0 |
0 |
|
| Term project presentation: |
0 |
0 |
|
| Quiz: |
0.5 |
12 |
|
| Own study for mid-term exam: |
4 |
2 |
|
| Mid-term: |
2 |
2 |
|
| Personal studies for final exam: |
4 |
1 |
|
| Final exam: |
3 |
1 |
|
| |
|
Total workload: |
|
| |
|
Total ECTS credits: |
* |
|
|
* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
|
|
|
-->
