| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 4 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Define data, data analysis, data science and its basic concepts and answer questions about these subject.
- can distinguish between programming languages and demonstrate proficiency in using AI-extensible IDEs like Positron to manage multi-language workflows,
- Have knowledge about basic R and Python coding and can use them in basic data analysis.
- Perform statistical methods and make visuals of data using R packages.
- can describe ML and DL techniques, distinguish their applications, and utilize AI-assisted coding practices for debugging and optimizing biological research scripts.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | No |
|---|
| Course Description | In this course, students learn the foundational principles of data science, including core terminology and the end-to-end analytical process. They gain hands-on experience with data analysis tools. Students will gain proficiency in data applications using Positron, a next-generation AI-extensible IDE. Furthermore, the course integrates AI-assisted coding practices to help students optimize their workflows in biological data analysis. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
What is Data Science? Introduction, Basic Concepts & Process |
|
2nd Week |
Data, Data Analysis, Steps, and Concepts |
|
3rd Week |
Exploring Data Analysis Tools (Spreadsheets - Functions and Database-SQL) |
|
4th Week |
Programming Languages |
|
5th Week |
Introduction to R and RStudio |
|
6th Week |
R Packages and Basic R codes |
|
7th Week |
Statistics with R (Case Applications) |
|
8th Week |
Statistics with R (Case Applications) |
|
9th Week |
Visualization and R Markdown |
|
10th Week |
Visualization and R Markdown |
|
11th Week |
Machine Learning (ML) and Deep Learning (DL): Examining a simple ML model in R |
|
12th Week |
Introduction to Python and Positron (as an AI-extensible IDE) |
|
13th Week |
Basic Python Syntax and AI-assisted coding practices |
|
14th Week |
Python Coding Applications and Automated Biological Data Pipelines with AI |
|
| Recommended or Required Reading | Biostatistics with R: An Introduction to Statistics Through Biological Data, 2012th Edition, Babak Shahbaba, Springer, 2011.
Python Programming for Biology, First Edition, Tim j. Stevens, Wayne Boucher, 2014.
An Introduction to Statistics with Python: With Applications in the Life Sciences, First Edition, Thomas Haslwanter, Springer, 2016. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Discussion
- Questions/Answers
- Practice
- Presentation
- Problem Solving
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
4 |
30 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
|
|
|
| Preparation for Final exam |
1 |
20 |
20 |
| Course hours |
14 |
2 |
28 |
| Preparation for Midterm exam |
1 |
20 |
20 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
4 |
5 |
20 |
| Performance Practice |
14 |
2 |
28 |
| Total Work Load | 120 |
| Total Work Load / 30 (h) | 4 |
| ECTS Credit of the Course | 4 |
|
| Language of Instruction | English |
|---|
| Work Placement(s) | No |
|---|
