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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!

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README 

        
![Open Source Society logo](http://i.imgur.com/kYYCXtC.png)



Open Source Society University




  :microscope: Path to a free self-taught education in  Bioinformatics!





  

	Open Source Society University - Bioinformatics

  




## 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: `Open Source Society University - Bioinformatics`



**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

- Algorithms



These 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