BIO405 Biological Data Analysis with Python
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
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Genetics and Bioengineering
Muhamed Adilović
Course Lecturer
Course Objectives
This course aims to teach students how to look for, process, analyze, and represent biological data using Python programming language.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Python Programming for Biology: Bioinformatics and Beyond; Tim J. Stevens, Wayne Boucher; Cambridge University Press; 1st edition
Additional Literature
Molecular Biology of the Cell; W. W. Norton & Company; Seventh edition // Python for Biologists: A complete programming course for beginners; CreateSpace Independent Publishing Platform; 1st edition // Advanced Python for Biologists; CreateSpace Independent Publishing Platform; 1st editionTeaching Methods
Lecture presentations
Coding demonstrations
Problem solving
And class discussions.
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction | Ch. 1 |
| 2 | Python and Biology Basics | Ch. 2 |
| 3 | More Python, GENCODE, RefSeq | Ch. 3 |
| 4 | Data Formats, Functional Programming, Genome Browsers; Quiz I | Ch. 4-5 |
| 5 | Biopython, More Functional Programming, UniProt | Ch. 5 |
| 6 | Sequence Alignment and Error Handling | Ch. 11-13 |
| 7 | Pfam, Regex, NumPy | Ch. 9 |
| 8 | Midterm | |
| 9 | Pandas | Ch 6 |
| 10 | Visualization, Gene Expression | Ch 16 |
| 11 | ClinVar, OOP, Advanced Pandas | Ch 7-8 |
| 12 | Statistics; Quiz II | Ch 22 |
| 13 | Statistics Continued | Ch 23 |
| 14 | Human Genetic Variation, Modules, Multivariate Analysis | Ch 14 |
| 15 | Review for the Final Exam |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| BIO405.1 | Course | Monday 09:00 - 11:50 | A F1.18 - Computer Lab | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Tuesday | 08:00 - 11:00 | A F1.33 | |
| Wednesday | 08:00 - 11:00 | A F1.33 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Midterm exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Quizzes
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture hours
45 hours ⏳ (15 week × 3 h)
Home study
45 hours ⏳ (15 week × 3 h)
Quizzzes
20 hours ⏳ (4 week × 5 h)
Midterm exam study
20 hours ⏳ (1 week × 20 h)
Final exam study
20 hours ⏳ (1 week × 20 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [BIO405] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Feb 02, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| BIO405 | Biological Data Analysis with Python | 1 | 2 | 6 | Monday 9:00-11:50 | |||||
| Prerequisite | ENS213 / CS103 / ENS213 / CS103 | It is a prerequisite to | - | |||||||
| Lecturer | Muhamed Adilović | Office Hours / Room / Phone | Tuesday: 8:00-11:00 Wednesday: 8:00-11:00 |
|||||||
| madilovic@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | This course aims to teach students how to look for, process, analyze, and represent biological data using Python programming language. | |||||||||
| Textbook | Python Programming for Biology: Bioinformatics and Beyond; Tim J. Stevens, Wayne Boucher; Cambridge University Press; 1st edition | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Lecture presentations, coding demonstrations, problem solving, and class discussions. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction | Ch. 1 | ||||||||
| Week 2 | Python and Biology Basics | Ch. 2 | ||||||||
| Week 3 | More Python, GENCODE, RefSeq | Ch. 3 | ||||||||
| Week 4 | Data Formats, Functional Programming, Genome Browsers; Quiz I | Ch. 4-5 | ||||||||
| Week 5 | Biopython, More Functional Programming, UniProt | Ch. 5 | ||||||||
| Week 6 | Sequence Alignment and Error Handling | Ch. 11-13 | ||||||||
| Week 7 | Pfam, Regex, NumPy | Ch. 9 | ||||||||
| Week 8 | Midterm | |||||||||
| Week 9 | Pandas | Ch 6 | ||||||||
| Week 10 | Visualization, Gene Expression | Ch 16 | ||||||||
| Week 11 | ClinVar, OOP, Advanced Pandas | Ch 7-8 | ||||||||
| Week 12 | Statistics; Quiz II | Ch 22 | ||||||||
| Week 13 | Statistics Continued | Ch 23 | ||||||||
| Week 14 | Human Genetic Variation, Modules, Multivariate Analysis | Ch 14 | ||||||||
| Week 15 | Review for the Final Exam | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,4,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm exam | 1 | 30 | 1,2,3,4,5 | Not Allowed | |
| Quizzes | 2 | 30 | 1,2,3,4,5 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture hours | 3 | 15 | 45 | Home study | 3 | 15 | 45 | |||
| Quizzzes | 5 | 4 | 20 | Midterm exam study | 20 | 1 | 20 | |||
| Final exam study | 20 | 1 | 20 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 23/02/2026 | |||||||||