Syllabus ( BSB 615 )
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Basic information
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| Course title: |
Biostatistics |
| Course code: |
BSB 615 |
| Lecturer: |
Assist. Prof. Pınar PİR
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| ECTS credits: |
7.5 |
| GTU credits: |
3 (3+0+0) |
| Year, Semester: |
3, Fall |
| Level of course: |
Second Cycle (Master's) |
| Type of course: |
Compulsory
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| Language of instruction: |
English
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| Mode of delivery: |
Face to face
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| Pre- and co-requisites: |
None |
| Professional practice: |
No |
| Purpose of the course: |
The 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 |
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Learning outcomes
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Upon successful completion of this course, students will be able to:
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Have a thorough understanding of fundamentals probability and statistics
Contribution to Program Outcomes
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Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology
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Grasp the importance of bioinformatics and systems biology based viewpoint in the analysis and interpretation of working principles of the cell.
Method of assessment
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Written exam
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Homework assignment
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Have an understanding of types of hypothesis testing and applications in life sciences
Contribution to Program Outcomes
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Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology
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Link the concepts belonging to the different disciplines and interpret & analyze scientific research in these disciplines.
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Acquire scientific knowledge and work independently
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Grasp the importance of bioinformatics and systems biology based viewpoint in the analysis and interpretation of working principles of the cell.
Method of assessment
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Written exam
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Homework assignment
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Equipped with biostatistics tools essential to bioinformatics and systems biology.
Contribution to Program Outcomes
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Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology
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Process and analyze genome-scale biological data using statistical methods and data mining methods.
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Link the concepts belonging to the different disciplines and interpret & analyze scientific research in these disciplines.
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Acquire scientific knowledge and work independently
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Find out new methods to improve his/her knowledge.
Method of assessment
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Written exam
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Homework assignment
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Aware of biased uses of biostatistics in the literature
Contribution to Program Outcomes
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Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology
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Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results.
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Acquire scientific knowledge and work independently
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Find out new methods to improve his/her knowledge.
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Demonstrating professional and ethical responsibility.
Method of assessment
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Written exam
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Contents
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| Week 1: |
Descriptive Statistics: Presenting the data from patients |
| Week 2: |
Introduction to Probability: Understanding risks of disease
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| Week 3: |
Introduction to Probability: Conditional probability
Classwork1 |
| Week 4: |
Discrete Probability Distributions - binomial, Poisson and negative binomial distributions |
| Week 5: |
Discrete Probability Distributions - hypergeometric distribution and other distributions
Classwork2 |
| Week 6: |
Continuous Probability Distributions: Design of clinical trials
Classwork3 |
| Week 7: |
Estimation Methods - Continuous distributions
Midterm 1 |
| Week 8: |
Estimation Methods - Discrete distributions
Classwork4 |
| Week 9: |
Hypothesis Testing: One-Sample Inference - z-test, t - test, identifying the Differentially Transcribed Genes |
| Week 10: |
Hypothesis Testing: One-Sample Inference - chi-square test, tests on discrete variables
Classwork5 |
| Week 11: |
Hypothesis Testing: Two-Sample Inference - t-tests
Midterm Exam 2 |
| Week 12: |
Hypothesis Testing: Two-Sample Inference - chi-square and F tests
Classwork6 |
| Week 13: |
Nonparametric Methods: Analysis of Omics Data With Unknown Distribution
Classwork7 |
| Week 14: |
Biased use of biostatistics in literature - How not to use p-value |
| Week 15*: |
. |
| 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. |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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Assessment
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| Method of assessment |
Week number |
Weight (%) |
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| Mid-terms: |
6 and 12 |
30 |
| Other in-term studies: |
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0 |
| Project: |
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0 |
| Homework: |
2-13 |
20 |
| Quiz: |
2-13 |
20 |
| Final exam: |
16 |
30 |
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Total weight: |
(%) |
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Workload
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| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
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| Courses (Face-to-face teaching): |
3 |
15 |
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| Own studies outside class: |
4 |
16 |
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| Practice, Recitation: |
0 |
0 |
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| Homework: |
3 |
12 |
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| Term project: |
0 |
0 |
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| Term project presentation: |
0 |
0 |
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| Quiz: |
0.5 |
12 |
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| Own study for mid-term exam: |
10 |
2 |
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| Mid-term: |
2 |
2 |
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| Personal studies for final exam: |
10 |
1 |
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| Final exam: |
3 |
1 |
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Total workload: |
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Total ECTS credits: |
* |
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* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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