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https://github.com/ossu/bioinformatics
:microscope: Path to a free self-taught education in Bioinformatics!
https://github.com/ossu/bioinformatics
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:microscope: Path to a free self-taught education in Bioinformatics!
- Host: GitHub
- URL: https://github.com/ossu/bioinformatics
- Owner: ossu
- Archived: true
- Created: 2016-06-21T23:11:54.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-04-08T00:22:09.000Z (10 months ago)
- Last Synced: 2024-09-27T03:42:27.958Z (4 months ago)
- Size: 61.5 KB
- Stars: 5,393
- Watchers: 364
- Forks: 900
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome - bioinformatics - 🔬 Path to a free self-taught education in Bioinformatics! (Learning resources / Security)
- awesome-machine-learning-resources - **[Tutorial
- awesome-genetics - Path to a free self-taught education in Bioinformatics
README
Open Source Society University
:microscope: Path to a free self-taught education in Bioinformatics!
Archived
Note: this curriculum is not under active development and may be out of date. Read more [here](./ARCHIVED.md).
## Contents
- [About](#about)
- [Motivation & Preparation](#motivation--preparation)
- [Curriculum](#curriculum)
- [How to use this guide](#how-to-use-this-guide)
- [Prerequisite](#prerequisite)
- [How to collaborate](#how-to-collaborate)
- [Code of Conduct](#code-of-conduct)
- [Community](#community)
- [Team](#team)
- [References](#references)## About
This is a **solid path** for those of you who want to complete a [Bioinformatics](https://en.wikipedia.org/wiki/Bioinformatics) course on your own time, **for free**, with courses from the **best universities** in the World.
In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind.
To become a bioinformatician, you have to learn quite a lot of science, so be ready for subjects like; Biology, Chemistry, etc...
## Motivation & Preparation
Here are two interesting links that can make **all** the difference in your journey.
The first one is a motivational video that shows a guy that went through the "MIT Challenge", which consists of learning the entire **4-year** MIT curriculum for Computer Science in **1 year**.
- [MIT Challenge](https://www.scotthyoung.com/blog/myprojects/mit-challenge-2/)
The second link is a MOOC that will teach you learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. These are **fundamental abilities** to succeed in our journey.
- [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn)
**Are you ready to get started?**
## Curriculum
### 1st Year
Code | Course | Duration | Effort
:-- | :--: | :--: | :--:
BIO 1311 | [Fundamentals of Biology](https://ocw.mit.edu/courses/7-01sc-fundamentals-of-biology-fall-2011/) | 12 weeks | 7-14 Hours/Week
CHEM 1311 | [Principles of Chemical Science](https://ocw.mit.edu/courses/5-111sc-principles-of-chemical-science-fall-2014/) | 15 Weeks | 4-6 Hours/Week
Py4E | [Python for Everybody](https://www.py4e.com/lessons) | 10 weeks | 10 hours/week
6.00.1x | [Introduction to Computer Science and Programming using Python](https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/) ([alt](https://www.edx.org/course/introduction-to-computer-science-and-programming-7)) | 9 weeks | 15 hours/week
MATH 1311 | [College Algebra and Problem Solving](https://www.edx.org/course/college-algebra-problem-solving-asux-mat117x) | 4 Weeks | 6 Hours/Week
MATH 1312 | [Pre-calculus](https://www.edx.org/course/precalculus-asux-mat170x) | 4 Weeks | 6 Hours/Week
18.01.1x | [Calculus 1A: Differentiation](https://openlearninglibrary.mit.edu/courses/course-v1:MITx+18.01.1x+2T2019/about) | 13 weeks | 6-10 hours/week
18.01.2x | [Calculus 1B: Integration](https://openlearninglibrary.mit.edu/courses/course-v1:MITx+18.01.2x+3T2019/about) | 13 weeks | 5-10 hours/week
MATH 1315 | [Introduction to Probability and Data (with R)](https://www.coursera.org/learn/probability-intro) | 5 Weeks | 6 Hours/Week### 2nd Year
Code | Course | Duration | Effort
:-- | :--: | :--: | :--:
BIO 2311 | [Biochemistry](https://www.edx.org/course/principles-of-biochemistry) | 15 Weeks | 4-6 Hours/Week
CHEM 2311 | [Organic Chemistry](http://ocw.mit.edu/courses/chemistry/5-12-organic-chemistry-i-spring-2005/) | 15 Weeks | 4-6 Hours/Week
COMP 2311 | [CS 2 - Object Oriented Java](https://www.coursera.org/learn/object-oriented-java) | 6 Weeks | 4-6 Hours/Week
18.01.3x | [Calculus 1C: Coordinate Systems & Infinite Series](https://openlearninglibrary.mit.edu/courses/course-v1:MITx+18.01.3x+1T2020/about) | 6 weeks | 5-10 hours/week
6.042J | [Mathematics for Computer Science](https://openlearninglibrary.mit.edu/courses/course-v1:OCW+6.042J+2T2019/about) ([Solutions](https://github.com/spamegg1/Math-for-CS-solutions)) | 13 weeks | 5 hours/week
COMP 2312 | [Databases](https://online.stanford.edu/courses/soe-ydatabases-databases) | 10 Weeks | 8-12 Hours/Week
18.06 | [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/) and [Essence of Linear Algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) | 14 weeks | 12 hours/week
COMP 2313 | [Introduction to Linux](https://www.edx.org/course/introduction-linux-linuxfoundationx-lfs101x-0) | 8 Weeks | 5-7 Hours/Week
MATH 2314 | [Inferential Statistics (with R)](https://www.coursera.org/learn/inferential-statistics-intro) | 5 Weeks | 6 Hours/Week### 3rd Year
Code | Course | Duration | Effort
:-- | :--: | :--: | :--:
BIO 3311 | [Proteins' Biology](https://www.edx.org/course/proteins-biologys-workforce) | 5 Weeks | 4-6 Hours/Week
COMP 3311a | [Algorithmic Thinking 1 ](https://www.coursera.org/learn/algorithmic-thinking-1) | 4 Weeks | 6 Hours/Week
COMP 3311b | [Algorithmic Thinking 2 ](https://www.coursera.org/learn/algorithmic-thinking-2) | 4 Weeks | 6 Hours/Week
MATH 3311 | [Linear Regression and Modeling (with R)](https://www.coursera.org/learn/linear-regression-model)| 4 Weeks | 6 Hours/Week
MATH 3312 | [Bayesian Statistics (with R)](https://www.coursera.org/learn/bayesian) | 5 Weeks | 6 Hours/Week
BIO 3312 | [Cell Biology ](http://ocw.mit.edu/courses/biology/7-06-cell-biology-spring-2007/) | - Weeks | - Hours/Week
MATH 3313 | [Differential Equations](https://ocw.mit.edu/courses/mathematics/18-03sc-differential-equations-fall-2011/) | 7 Weeks | 8-10 Hours/Week
BIO 3313a | [Biostatistics 1](https://www.coursera.org/learn/biostatistics) | 4 Weeks | 3-5 Hours/Week
BIO 3313b | [Biostatistics 2](https://www.coursera.org/learn/biostatistics-2) | 4 Weeks | 3-5 Hours/Week### 4th Year
Code | Course | Duration | Effort
:-- | :--: | :--: | :--:
BIO 4311 | [DNA: Biology's Genetic Code](https://www.edx.org/course/dna-biologys-genetic-code) | 6 Weeks | 4-6 Hours/Week
COMP 4311 | [Data Science ](http://cs109.github.io/2015/) | 13 Week | 10 Hours/Week
BIO 4312a | [Molecular Biology](https://ocw.mit.edu/courses/biology/7-28-molecular-biology-spring-2005/) | 16 Weeks | 4-8 Hours/Week
BIO 4312d | [Bioinformatics 1](https://www.coursera.org/learn/dna-analysis) | 4 Weeks | 4-10 Hours/Week
COMP 4312a | [Bioinformatics 2](https://www.coursera.org/learn/genome-sequencing) | 4 Week | 6 Hours/Week
COMP 4312b | [Bioinformatics 3](https://www.coursera.org/learn/comparing-genomes) | 4 Week | 6 Hours/Week
COMP 4312c | [Bioinformatics 4](https://www.coursera.org/learn/molecular-evolution) | 4 Week | 6 Hours/Week
COMP 4312d | [Bioinformatics 5](https://www.coursera.org/learn/genomic-data) | 4 Week | 6 Hours/Week
COMP 4312e | [Bioinformatics 6](https://www.coursera.org/learn/dna-mutations) | 4 Week | 6 Hours/Week
COMP 4312f | [Bioinformatics 7 (Capstone)](https://www.coursera.org/learn/bioinformatics-project) | 3 Week | 3-4 Hours/Week
BIO 4313 | [Evolution](https://www.coursera.org/learn/genetics-evolution) | 11 Weeks | 4-6 Hours/Week### Extra Year
Code | Course | Duration | Effort
:-- | :--: | :--: | :--:
COMP 5311 | [Introduction to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120) | 10 Weeks | 6 Hours/Week
COMP 5312 | [Deep Learning](https://www.udacity.com/course/deep-learning--ud730) | 8 Weeks | 6 Hours/Week
Extension | [Genomic Data Science Specialization](https://www.coursera.org/specializations/genomic-data-science) | 32 Week | 6 Hours/Week> Will continue with Master's in Bioinformatics
---
![keep learning](http://i.imgur.com/REQK0VU.jpg)
## How to use this guide
### Order of the classes
This guide was developed to be consumed in a linear approach. What does this mean? That you should complete one course at a time.
The courses are **already** in the order that you should complete them. Just start the first course, [Introduction to Biology](https://www.edx.org/course/introduction-biology-secret-life-mitx-7-00x-2), when you done with it, start the next one.
**If the course is not open, do it with the archived resources or wait until next class is open.**
### How to track and show your progress
1. Create an account in [Trello](https://trello.com/).
1. Copy [this](https://trello.com/b/yax8Kgnh) board to your personal account. See how to copy a board [here](http://blog.trello.com/you-can-copy-boards-now-finally/).Now that you have a copy of our official board, you just need to pass the cards to the `Doing` column or `Done` column as you progress in your study.
We also have **labels** to help you have more control through the process. The meaning of each of these labels is:
- `Main Curriculum`: cards with that label represent courses that are listed in our curriculum.
- `Extra Courses`: cards with that label represent courses that was added by the student.
- `Doing`: cards with that label represent courses the student is current doing.
- `Done`: cards with that label represent courses finished by the student. Those cards should also have the link for at least one project/article built with the knowledge acquired in such course.
- `Section`: cards with that label represent the section that we have in our curriculum. Those cards with the `Section` label are only to help the organization of the Done column. You should put the *Course's cards* below its respective *Section's card*.
- `Extra Sections`: cards with that label represent sections that was added by the student.The intention of this board is to provide for our students a way to track their progress, and also the ability to show their progress through a public page for friends, family, employers, etc. You can change the status of your board to be **public** or **private**.
### Should I take all courses?
**Yes!** The intention is to conclude **all** the courses listed here! Also we highly encourage you to complete more by reading papers and attending research projects after your coursework is done.
### Duration of the course
It may take longer to complete all of the classes compared to a regular Bioinformatics course, but we can **guarantee** you that your **reward** will be proportional to **your motivation/dedication**!
You must focus on your **habit**, and **forget** about goals. Try to invest 1 ~ 2 hours **every day** studying this curriculum. If you do this, **inevitably** you'll finish this curriculum.
> See more about "Commit to a process, not a goal" [here](http://jamesclear.com/goals-systems).
### Project Based
Here in **OSS University**, you do **not** need to take exams, because we are focused on **real projects**!
In order to show for everyone that you **successfully** finished a course, you should create a **real project** or write **papers and publish them** about your focus with Bioinformatics.
> "What does it mean?"
After finish a course, you should think about a **real world problem** that you can solve using the acquired knowledge in the course. You don't need to create a big project, but you must create something to **validate** and **consolidate** your knowledge, and also to show to the world that you are capable to create something useful with the concepts that you learned.
Put the OSSU-Bioinformatics badge in the README of your repository! [![Open Source Society University - Bioinformatics](https://img.shields.io/badge/OSSU-bioinformatics-blue.svg)](https://github.com/open-source-society/bioinformatics)
- Markdown: `[![Open Source Society University - Bioinformatics ](https://img.shields.io/badge/OSSU-bioinformatics-blue.svg)](https://github.com/open-source-society/bioinformatics)`
- HTML: ``**You can create this project alone or with other students!**
### Be creative!
This is a **crucial** part of your journey through all those courses.
You **need** to have in mind that what you are able to **create** with the concepts that you learned will be your certificate **and this is what really matters**!
In order to show that you **really** learned those things, you need to be **creative**!
Here are some tips about how you can do that:
- **Articles**: create blog posts to synthesize/summarize what you learned.
- **GitHub repository**: keep your course's files organized in a GH repository, so in that way other students can use it to study with your annotations.### Cooperative work
**We love cooperative work**! Use our [channels](#community) to communicate with other fellows to combine and create new projects!
### Which programming languages should I use?
List of skills:
- C/C++
- Unix System
- Python/Perl
- R
- AlgorithmsThese skills mentioned above are the very essential tool set that bioinformatician and computational biologist depends on.
The **important** thing for each course is to **internalize** the **core concepts** and to be able to use them with whatever tool (programming language) that you wish.
### Content Policy
You must share **only** files that you are **allowed** to! **Do NOT disrespect the code of conduct** that you signed in the beginning of some courses.
[Be creative](#be-creative) in order to show your progress! :smile:
### Stay tuned
[Watch](https://help.github.com/articles/watching-repositories/) this repository for futures improvements and general information.
## Prerequisite
Students without a strong high school background in Biology will benefit from [Getting up to Speed in Biology](https://openlearninglibrary.mit.edu/courses/course-v1:OCW+Pre-7.01+1T2020/about).
Understanding how to use Git to version your work can be hugely beneficial and is generally not taught in other courses. [Version Control with Git](https://www.udacity.com/course/version-control-with-git--ud123) should get you up to speed.
## How to collaborate
You can [open an issue](https://help.github.com/articles/creating-an-issue/) and give us your suggestions as to how we can improve this guide, or what we can do to improve the learning experience.
You can also [fork this project](https://help.github.com/articles/fork-a-repo/) and send a [pull request](https://help.github.com/articles/using-pull-requests/) to fix any mistakes that you have found.
TODO:
If you want to suggest a new resource, send a pull request adding such resource to the [extras](https://github.com/open-source-society/bioinformatics/tree/master/extras) section.The **extras** section is a place where all of us will be able to submit interesting additional articles, books, courses and specializations, keeping our curriculum *as immutable and concise as possible*.
**Let's do it together! =)**
## Code of conduct
[OSSU's code of conduct](https://github.com/ossu/code-of-conduct).
## Community
We have a Discord server! This should be your first stop to talk with other OSSU students. [Why don't you introduce yourself right now?](https://discord.gg/wuytwK5s9h)
Subscribe to our [newsletter](https://tinyletter.com/OpenSourceSocietyUniversity)
You can also interact through [GitHub issues](https://github.com/open-source-society/bioinformatics/issues).
Add **Open Source Society University** to your [Linkedin](https://www.linkedin.com/school/11272443/) and join our [Facebook](https://www.facebook.com/groups/opensourcesocietyu/) group!
## Team
* **Curriculum Founder**: [Enes Kemal Ergin](https://github.com/eneskemalergin)
* **Curriculum Maintainer**: [Enes Kemal Ergin](https://github.com/eneskemalergin)
* **Contributors**: [contributors](https://github.com/open-source-society/bioinformatics/graphs/contributors)## References