├── PROJECTS.md
├── README.md
└── extras
├── books.md
├── courses.md
└── specializations.md
/PROJECTS.md:
--------------------------------------------------------------------------------
1 | Open Source Society University
2 |
3 | Knowledge and skills as commons
4 |
5 |
6 | Projects ideas
7 |
8 | >Here, we are providing a list curated by the community of exercices and projects to practice and reinforce the skills we try to master.
9 |
10 | Projects created by OSSU's students for each course of our [**Data Science**](https://github.com/open-source-society/data-science) curriculum.
11 |
12 | ### Linear Algebra
13 | Project Title | Description | Authors | Repository
14 | :-- | :-- | :--: | :--
15 | | | |
16 |
17 | ### Single Variable Calculus
18 | Project Title | Description | Authors | Repository
19 | :-- | :-- | :--: | :--
20 | | slope-field | Slope-field generation written in Haskell | Mahdi Dibaiee | https://github.com/mdibaiee/slope-field
21 |
22 | ### Multivariable Calculus
23 | Project Title | Description | Authors | Repository
24 | :-- | :-- | :--: | :--
25 | | | |
26 |
27 | ### Python
28 | Project Title | Description | Authors | Repository
29 | :-- | :-- | :--: | :--
30 | | | |
31 |
32 | ### Probability and Statistics
33 | Project Title | Description | Authors | Repository
34 | :-- | :-- | :--: | :--
35 | | | |
36 |
37 | ### Introduction to Data Science
38 | Project Title | Description | Authors | Repository
39 | :-- | :-- | :--: | :--
40 | | | |
41 |
42 | ### Machine Learning
43 | Project Title | Description | Authors | Repository
44 | :-- | :-- | :--: | :--
45 | | | |
46 |
47 | ### Convex Optimization
48 | Project Title | Description | Authors | Repository
49 | :-- | :-- | :--: | :--
50 | | | |
51 |
52 |
53 | ### Big Data
54 | Project Title | Description | Authors | Repository
55 | :-- | :-- | :--: | :--
56 | | | |
57 |
58 | ### Database
59 | Project Title | Description | Authors | Repository
60 | :-- | :-- | :--: | :--
61 | | | |
62 |
63 | ### Natural Language Processing
64 | Project Title | Description | Authors | Repository
65 | :-- | :-- | :--: | :--
66 | | | |
67 |
68 | ### Deep Learning
69 | Project Title | Description | Authors | Repository
70 | :-- | :-- | :--: | :--
71 | | | |
72 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | 
2 |
3 | Open Source Society University
4 |
5 | :bar_chart: Path to a free self-taught education in Data Science!
6 |
7 |
8 | 
9 |
10 |
11 | 
12 |
13 |
14 |
15 |
16 | ## Contents
17 |
18 | - [About](#about)
19 | - [Motivation & Preparation](#motivation--preparation)
20 | - [Curriculum](#curriculum)
21 | - [How to use this guide](#how-to-use-this-guide)
22 | - [Prerequisite](#prerequisite)
23 | - [How to collaborate](#how-to-collaborate)
24 | - [Code of conduct](#code-of-conduct)
25 | - [Community](#community)
26 | - [Next Goals](#next-goals)
27 | - [Team](#team)
28 | - [References](#references)
29 |
30 | ## About
31 |
32 | This is a **solid path** for those of you who want to complete a **Data Science** course on your own time, **for free**, with courses from the **best universities** in the World.
33 |
34 | 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.
35 |
36 | ## Motivation & Preparation
37 |
38 | Here are two interesting links that can make **all** the difference in your journey.
39 |
40 | 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**.
41 |
42 | - [MIT Challenge](https://www.scotthyoung.com/blog/myprojects/mit-challenge-2/)
43 |
44 | 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.
45 |
46 | - [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn)
47 |
48 | **Are you ready to get started?**
49 |
50 | ## Curriculum
51 |
52 | - [Linear Algebra](#linear-algebra)
53 | - [Single Variable Calculus](#single-variable-calculus)
54 | - [Multivariable Calculus](#multivariable-calculus)
55 | - [Python](#python)
56 | - [Probability and Statistics](#probability-and-statistics)
57 | - [Introduction to Data Science](#introduction-to-data-science)
58 | - [Machine Learning](#machine-learning)
59 | - [Project](#project)
60 | - [Convex Optimization](#convex-optimization)
61 | - [Data Wrangling](#data-wrangling)
62 | - [Big Data](#big-data)
63 | - [Database](#database)
64 | - [Deep Learning](#deep-learning)
65 | - [Natural Language Processing](#natural-language-processing)
66 | - [Capstone Project](#capstone-project)
67 | - [Specializations](#specializations)
68 |
69 |
70 | ---
71 |
72 | ### Linear Algebra
73 |
74 | Courses | Duration | Effort
75 | :-- | :--: | :--:
76 | [Linear Algebra - Foundations to Frontiers](https://www.edx.org/course/linear-algebra-foundations-frontiers-utaustinx-ut-5-04x#!)| 15 weeks | 8 hours/week
77 | [Applications of Linear Algebra Part 1](https://www.edx.org/course/applications-linear-algebra-part-1-davidsonx-d003x-1)| 5 weeks | 4 hours/week
78 | [Applications of Linear Algebra Part 2](https://www.edx.org/course/applications-linear-algebra-part-2-davidsonx-d003x-2)| 4 weeks | 5 hours/week
79 |
80 | ### Single Variable Calculus
81 | Courses | Duration | Effort
82 | :-- | :--: | :--:
83 | [Calculus 1A: Differentiation](https://www.edx.org/course/calculus-1a-differentiation-mitx-18-01-1x)| 13 weeks | 6-10 hours/week
84 | [Calculus 1B: Integration](https://www.edx.org/course/calculus-1b-integration-mitx-18-01-2x)| 13 weeks | 5-10 hours/week
85 | [Calculus 1C: Coordinate Systems & Infinite Series](https://www.edx.org/course/calculus-1c-coordinate-systems-infinite-mitx-18-01-3x)| 13 weeks | 6-10 hours/week
86 |
87 | ### Multivariable Calculus
88 | Courses | Duration | Effort
89 | :-- | :--: | :--:
90 | [MIT OCW Multivariable Calculus](http://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm)| 15 weeks | 8 hours/week
91 |
92 | ### Python
93 | Courses | Duration | Effort
94 | :-- | :--: | :--:
95 | [Introduction to Computer Science and Programming Using Python](https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-7)| 9 weeks | 15 hours/week
96 | [Introduction to Computational Thinking and Data Science](https://www.edx.org/course/introduction-computational-thinking-data-mitx-6-00-2x-3)| 10 weeks | 15 hours/week
97 | [Introduction to Python for Data Science](https://prod-edx-mktg-edit.edx.org/course/introduction-python-data-science-microsoft-dat208x-1)| 6 weeks | 2-4 hours/week
98 | [Programming with Python for Data Science](https://www.edx.org/course/programming-python-data-science-microsoft-dat210x)| 6 weeks | 3-4 hours/week
99 |
100 | ### Probability and Statistics
101 | Courses | Duration | Effort
102 | :-- | :--: | :--:
103 | [Introduction to Probability](https://www.edx.org/course/introduction-probability-science-mitx-6-041x-1#.U3yb762SzIo)| 16 weeks | 12 hours/week
104 | [Statistical Reasoning](https://lagunita.stanford.edu/courses/OLI/StatReasoning/Open/about)| - weeks | - hours/week
105 | [Introduction to Statistics: Descriptive Statistics](https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x)| 5 weeks | - hours/week
106 | [Introduction to Statistics: Probability](https://www.edx.org/course/introduction-statistics-probability-uc-berkeleyx-stat2-2x)| 5 weeks | - hours/week
107 | [Introduction to Statistics: Inference](https://www.edx.org/course/introduction-statistics-inference-uc-berkeleyx-stat2-3x)| 5 weeks | - hours/week
108 |
109 | ### Introduction to Data Science
110 | Courses | Duration | Effort
111 | :-- | :--: | :--:
112 | [Introduction to Data Science](https://www.coursera.org/course/datasci)| 8 weeks | 10-12 hours/week
113 | [Data Science - CS109 from Harvard](http://cs109.github.io/2015/)| 12 weeks | 5-6 hours/week
114 | [The Analytics Edge](https://www.edx.org/course/analytics-edge-mitx-15-071x-2)| 12 weeks | 10-15 hours/week
115 |
116 | ### Machine Learning
117 | Courses | Duration | Effort
118 | :-- | :--: | :--:
119 | [Learning From Data (Introductory Machine Learning)](https://www.edx.org/course/learning-data-introductory-machine-caltechx-cs1156x) [[caltech]](http://work.caltech.edu/lectures.html) | 10 weeks | 10-20 hours/week
120 | [Statistical Learning](https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about)| - weeks | 3 hours/week
121 | [Stanford's Machine Learning Course](https://www.coursera.org/learn/machine-learning)| - weeks | 8-12 hours/week
122 |
123 | ### Project
124 | Complete Kaggle's Getting Started and Playground Competitions
125 |
126 |
127 | ### Convex Optimization
128 | Courses | Duration | Effort
129 | :-- | :--: | :--:
130 | [Convex Optimization](https://lagunita.stanford.edu/courses/Engineering/CVX101/Winter2014/about)| 9 weeks | 10 hours/week
131 |
132 | ### Data Wrangling
133 | Courses | Duration | Effort
134 | :-- | :--: | :--:
135 | [Data Wrangling with MongoDB](https://www.udacity.com/course/data-wrangling-with-mongodb--ud032)| 8 weeks | 10 hours/week
136 |
137 | ### Big Data
138 | Courses | Duration | Effort
139 | :-- | :--: | :--:
140 | [Intro to Hadoop and MapReduce](https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617)| 4 weeks | 6 hours/week
141 | [Deploying a Hadoop Cluster](https://www.udacity.com/course/deploying-a-hadoop-cluster--ud1000)| 3 weeks | 6 hours/week
142 |
143 | ### Database
144 | Courses | Duration | Effort
145 | :-- | :--: | :--:
146 | [Stanford's Database course](https://lagunita.stanford.edu/courses/DB/2014/SelfPaced/about)| - weeks | 8-12 hours/week
147 |
148 | ### Natural Language Processing
149 | Courses | Duration | Effort
150 | :-- | :--: | :--:
151 | [Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/)| - weeks | - hours/week
152 |
153 | ### Deep Learning
154 | Courses | Duration | Effort
155 | :-- | :--: | :--:
156 | [Deep Learning](https://www.udacity.com/course/deep-learning--ud730)| 12 weeks | 8-12 hours/week
157 |
158 | ### Capstone Project
159 | - Participate in Kaggle competition
160 | - List down other ideas
161 |
162 |
163 | ### Specializations
164 |
165 | After finishing the courses above, start your specializations on the topics that you have more interest.
166 | You can view a list of available specializations [here](https://github.com/open-source-society/data-science/blob/master/extras/specializations.md).
167 |
168 | 
169 |
170 | ## How to use this guide
171 |
172 | ### Order of the classes
173 |
174 | This guide was developed to be consumed in a linear approach. What does this mean? That you should complete one course at a time.
175 |
176 | The courses are **already** in the order that you should complete them. Just start in the [Linear Algebra](#linear-algebra) section and after finishing the first course, start the next one.
177 |
178 | **If the course isn't open, do it anyway with the resources from the previous class.**
179 |
180 | ### Should I take all courses?
181 |
182 | **Yes!** The intention is to conclude **all** the courses listed here!
183 |
184 | ### Duration of the project
185 |
186 | It may take longer to complete all of the classes compared to a regular Data Science course, but I can **guarantee** you that your **reward** will be proportional to **your motivation/dedication**!
187 |
188 | 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.
189 |
190 | > See more about "Commit to a process, not a goal" [here](http://jamesclear.com/goals-systems).
191 |
192 | ### Project Based
193 |
194 | Here in **OSS University**, you do **not** need to take exams, because we are focused on **real projects**!
195 |
196 | In order to show for everyone that you **successfully** finished a course, you should create a **real project**.
197 |
198 | > "What does it mean?"
199 |
200 | 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.
201 |
202 | The projects of all students will be listed in [this](https://github.com/open-source-society/data-science/blob/master/PROJECTS.md) file. Submit your project's information in that file after you conclude it.
203 |
204 | **You can create this project alone or with other students!**
205 |
206 | #### Project Suggestions
207 |
208 |
209 |
210 | And you should also...
211 |
212 | ### Be creative!
213 |
214 | This is a **crucial** part of your journey through all those courses.
215 |
216 | 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**!
217 |
218 | In order to show that you **really** learned those things, you need to be **creative**!
219 |
220 | Here are some tips about how you can do that:
221 |
222 | - **Articles**: create blog posts to synthesize/summarize what you learned.
223 | - **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.
224 |
225 | ### Cooperative work
226 |
227 | **We love cooperative work**! Use our [channels](#community) to communicate with other fellows to combine and create new projects!
228 |
229 | ### Which programming languages should I use?
230 |
231 | Python and R are heavily used in Data Science community and our courses teach you both, but...
232 |
233 | 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.
234 |
235 | ### Content Policy
236 |
237 | 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.
238 |
239 | [Be creative](#be-creative) in order to show your progress! :smile:
240 |
241 | ### Stay tuned
242 |
243 | [Watch](https://help.github.com/articles/watching-repositories/) this repository for futures improvements and general information.
244 |
245 | ## Prerequisite
246 |
247 | The **only things** that you need to know are how to use **Git** and **GitHub**. Here are some resources to learn about them:
248 |
249 | **Note**: Just pick one of the courses below to learn the basics. You will learn a lot more once you get started!
250 |
251 | - [Try Git](https://try.github.io/levels/1/challenges/1)
252 | - [Git - the simple guide](http://rogerdudler.github.io/git-guide/)
253 | - [GitHub Training & Guides](https://www.youtube.com/playlist?list=PLg7s6cbtAD15G8lNyoaYDuKZSKyJrgwB-)
254 | - [GitHub Hello World](https://guides.github.com/activities/hello-world/)
255 | - [Git Immersion](http://gitimmersion.com/index.html)
256 | - [How to Use Git and GitHub](https://www.udacity.com/course/how-to-use-git-and-github--ud775)
257 |
258 | ## Change Log
259 |
260 |
261 |
262 | ## How to collaborate
263 |
264 | 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.
265 |
266 | 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.
267 |
268 | TODO:
269 | If you want to suggest a new resource, send a pull request adding such resource to the [extras](https://github.com/open-source-society/data-science/tree/master/extras) section.
270 |
271 | 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*.
272 |
273 | **Let's do it together! =)**
274 |
275 | ## Code of Conduct
276 | [OSSU's code of conduct](https://github.com/ossu/code-of-conduct).
277 |
278 | ## Community
279 |
280 | Subscribe to our [newsletter](https://tinyletter.com/ossu).
281 |
282 | Use our [forum](https://github.com/ossu/forum) if you need some help.
283 |
284 | You can also interact through [GitHub issues](https://github.com/open-source-society/data-science/issues).
285 |
286 | We also have a chat room! [](https://gitter.im/open-source-society/data-science)
287 |
288 | Add **Open Source Society University** to your [Linkedin](https://www.linkedin.com/school/11272443/) and [Facebook](https://www.facebook.com/ossuniversity) profile!
289 |
290 | > **ps**: A forum is an ideal way to interact with other students as we do not lose important discussions, which usually occur in communication via chat apps. **Please use our forum for important discussions**.
291 |
292 | ## Next Goals
293 |
294 | - [Add our University page at Linkedin](https://help.linkedin.com/app/answers/detail/a_id/40128/~/adding-a-new-university-page), so in that way we will be able to add **OSS University** in our Linkedin profile.
295 |
296 | ## Team
297 |
298 | * **Curriculum Founder**: [Shouvik Roy](https://github.com/royshouvik)
299 | * **Curriculum Maintainer**: [Shouvik Roy](https://github.com/royshouvik)
300 | * **Contributors**: [contributors](https://github.com/open-source-society/data-science/graphs/contributors)
301 |
302 | ## References
303 |
--------------------------------------------------------------------------------
/extras/books.md:
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1 | # Data Science - Extra Resources
2 |
3 | ## Books
4 |
5 | - [Python](#python)
6 | - [Data Analysis](#data-analysis)
7 | - [Data Visualization](#data-visualization)
8 | - [Web Scraping](#web-scraping)
9 | - [Databases and SQL](#databases-and-sql)
10 | - [Statistics](#statistics)
11 | - [Linear Algebra](#linear-algebra)
12 | - [Machine Learning](#machine-learning)
13 | - [Data Science](#data-science)
14 | - [Big Data](#big-data)
15 |
16 |
17 | ---
18 |
19 |
20 |
21 | ### Python
22 |
23 | Name | Author | ISBN
24 | :-- | :--: | :--:
25 |
26 |
27 | ### Data Analysis
28 |
29 | Name | Author | ISBN
30 | :-- | :--: | :--:
31 |
32 |
33 | ### Data Visualization
34 |
35 | Name | Author | ISBN
36 | :-- | :--: | :--:
37 |
38 |
39 | ### Web Scraping
40 |
41 | Name | Author | ISBN
42 | :-- | :--: | :--:
43 |
44 |
45 | ### Databases and SQL
46 |
47 | Name | Author | ISBN
48 | :-- | :--: | :--:
49 |
50 | ### Statistics
51 |
52 |
53 | Name | Author | ISBN
54 | :-- | :--: | :--:
55 |
56 |
57 | ### Linear Algebra
58 |
59 | Name | Author | ISBN
60 | :-- | :--: | :--:
61 |
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/extras/courses.md:
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1 | # Data Science - Extra Resources
2 |
3 | ## Courses
4 |
5 | - [Statistics](#statistics)
6 |
7 | ---
8 |
9 | ### Statistics
10 |
11 | Courses | Duration | Effort
12 | :-- | :--: | :--:
13 | [Intro to Statistics](https://www.udacity.com/course/intro-to-statistics--st101)| 8 weeks | 6 hours/week
14 | [Basic Statistics](https://www.coursera.org/learn/basic-statistics)| 8 weeks | 3 hours/week
15 | [Bayesian Statistics](https://www.coursera.org/learn/bayesian)| 5 weeks | 5-7 hours/week
16 |
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/extras/specializations.md:
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1 | # Data Science - Specializations
2 |
3 | ## Specializations
4 |
5 | * [Udacity](#udacity)
6 | * [Machine Learning Nanodegree by Google](#machine-learning-nanodegree-by-google)
7 | * [Data Scientist Nanodegree](#data-scientist-nanodegree)
8 | * [edX](#edx)
9 | * [Data Science and Engineering with Apache Spark](#data-science-and-engineering-with-apache-spark)
10 | * [Coursera](#coursera)
11 | * [Data Mining Specialization](#data-mining-specialization)
12 | * [Machine Learning Specialization](#machine-learning-specialization)
13 | * [Data Science Specialization](#data-science-specialization)
14 | * [FutureLearn](#futurelearn)
15 |
16 | ---
17 |
18 | ### Udacity
19 |
20 | #### Machine Learning Nanodegree by Google
21 | Course | Duration | Effort
22 | :-- | :--: | :--:
23 | [Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009)| - weeks | 10 hours/week
24 |
25 | #### Data Scientist Nanodegree
26 | Course | Duration | Effort
27 | :-- | :--: | :--:
28 | [Data Analyst Nanodegree](https://www.udacity.com/course/data-analyst-nanodegree--nd002)| - weeks | 10 hours/week
29 |
30 | ### edX
31 |
32 | #### Data Science and Engineering with Apache Spark
33 | Course | Duration | Effort
34 | :-- | :--: | :--:
35 | [Data Science and Engineering with Apache Spark XSeries](https://www.edx.org/xseries/data-science-engineering-apache-spark)| - weeks | 10 hours/week
36 |
37 | ### Coursera
38 |
39 | #### Data Mining Specialization
40 | Course | Duration | Effort
41 | :-- | :--: | :--:
42 | [Data Mining](https://www.coursera.org/specializations/data-mining)| - weeks | 8-12 hours/week
43 |
44 | #### Machine Learning Specialization
45 | Course | Duration | Effort
46 | :-- | :--: | :--:
47 | [Machine Learning](https://www.coursera.org/specializations/machine-learning)| - weeks | 8-12 hours/week
48 |
49 | #### Data Science Specialization
50 | Course | Duration | Effort
51 | :-- | :--: | :--:
52 | [Statistics with R](https://www.coursera.org/specializations/statistics)| - weeks | - hours/week
53 | [Data Science at Scale](https://www.coursera.org/specializations/data-science)| 17 weeks | 6-8 hours/week
54 | [Data Science](https://www.coursera.org/specializations/jhu-data-science) | - weeks | 4-9 hours/week
55 |
56 | ### FutureLearn
57 |
58 | #### Big Data
59 | Course | Duration | Effort
60 | :-- | :--: | :--:
61 | [Big Data Analytics](https://www.futurelearn.com/programs/big-data-analytics)| 8 weeks | - hours/week
62 |
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