課程名稱 |
生物數據分析的計算技能 Computational Skills for Biological Data Analysis |
開課學期 |
111-2 |
授課對象 |
生物資源暨農學院 農藝學系 |
授課教師 |
吳泓熹 |
課號 |
Agron5106 |
課程識別碼 |
621EU7070 |
班次 |
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學分 |
3.0 |
全/半年 |
半年 |
必/選修 |
選修 |
上課時間 |
星期二6,7,8(13:20~16:20) |
上課地點 |
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備註 |
本課程以英語授課。地點:生物產業自動化中心(鄭江樓505室) 總人數上限:45人 |
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課程簡介影片 |
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核心能力關聯 |
本課程尚未建立核心能力關連 |
課程大綱
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課程概述 |
Please refer to the English version for the latest information: Syllabus
In the era of big data, proficiency in several fundamental computational skills is required to conduct high-quality analysis and reproducible research in multiple disciplines. Within the field of biology and agriculture, large-scale datasets are easily accessible due to the advancement in technology. The amount of data will continue to increase at a dramatic rate over the following decades. Students will be required to have the ability to process and analyse large amounts of data efficiently in the “-omic” and even "post-omic" era.
This course will introduce a few fundamental and transferable computational skills for students who work with biological data. These skills include but are not limited to command line interface, working with computer servers, software version control (Git and GitHub) for collaboration, software testing for reproducible analysis, working with the relational database (MySQL), data cleaning and manipulation.
Although many of these skill sets are transferable to fields outside of biology, this course will focus on their application to biological data.
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課程目標 |
Please refer to the English version for the latest information: Syllabus
At the completion of this course, students will be able to:
- Work with the command line interface, navigate the filesystem, perform file manipulation, execute commands to solve simple tasks and connect to remote servers.
- Utilise software version control systems (Git and GitHub) for reproducible work, collaborate with others, and perform software testing and validation.
- Understand the principles and concepts of the relational database (MySQL), and be able to perform daily tasks, such as storing and retrieving data.
- Work collaboratively in groups to solve computational challenges.
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課程要求 |
- This course will be taught in English. All materials are available in English only.
- Cheating and plagiarism in assignments, exams or any other assessments are serious academic misconduct. All instances will be handled according to the university policy.
- Students are required to have basic statistics knowledge. Any Statistics101 course that covers descriptive statistics, simple linear regression and ANOVA will be sufficient.
- Students are required to have exposure to at least one programming language. It is recommended that students are familiar with the following basic concepts: declare variables, basic arithmetic operation, basic data type (R: vector, list, data.frame. Python: list, dictionary), declare functions.
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預期每週課後學習時數 |
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Office Hours |
另約時間 |
指定閱讀 |
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參考書目 |
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評量方式 (僅供參考) |
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週次 |
日期 |
單元主題 |
Week 1 |
Feb/21 |
Introduction |
Week 2 |
Feb/28 |
Holiday |
Week 3 |
Mar/07 |
Basic programming (R or python) - Part 1 |
Week 4 |
Mar/14 |
Basic programming (R or python) - Part 2 |
Week 5 |
Mar/21 |
Command line interface - Part 1 |
Week 6 |
Mar/28 |
Software version control with Git and GitHub |
Week 7 |
Apr/04 |
Holiday |
Week 8 |
Apr/11 |
Introduction to the relational database |
Week 9 |
Apr/18 |
MySQL database - part 1 |
Week 10 |
Apr/25 |
MySQL database - part 2 |
Week 11 |
May/02 |
MySQL database - part 3 |
Week 12 |
May/09 |
Collaboration on GitHub |
Week 13 |
May/16 |
Software testing |
Week 14 |
May/23 |
Database with R/Python |
Week 15 |
May/30 |
Command line interface - Part 2 |
Week 16 |
Jun/06 |
Final projcet presentation |
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