├── GitHub - Springboard banner 2@2x.png
├── PROJECTS.md
├── README.md
└── extras
├── books.md
├── courses.md
└── specializations.md
/GitHub - Springboard banner 2@2x.png:
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/PROJECTS.md:
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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 | - [Becoming an OSS student](#becoming-an-oss-student)
20 | - [Motivation & Preparation](#motivation--preparation)
21 | - [Curriculum](#curriculum)
22 | - [How to use this guide](#how-to-use-this-guide)
23 | - [Prerequisite](#prerequisite)
24 | - [How to collaborate](#how-to-collaborate)
25 | - [Code of conduct](#code-of-conduct)
26 | - [Community](#community)
27 | - [Next Goals](#next-goals)
28 | - [Team](#team)
29 | - [References](#references)
30 |
31 | ## About
32 |
33 | 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.
34 |
35 | 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.
36 |
37 | ## Becoming an OSS student
38 |
39 | To officially register for this course you must create a profile in our [web app](https://ossu.firebaseapp.com).
40 |
41 | **ps**: Currently, the web app is for tracking the progress of the [Computer Science](https://github.com/open-source-society/computer-science) path, but we are working to extend this functionality for all of our courses. Thanks for the comprehension.
42 |
43 | > **"How can I do this?"**
44 |
45 | Just create an account on GitHub and log in with this account in our web app.
46 |
47 | The intention of this app is to offer 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.
48 |
49 | In the "My Progress" tab, you are able to edit the status of the courses that you are taking, and also add the link of your final project for each one.
50 |
51 | ## Motivation & Preparation
52 |
53 | Here are two interesting links that can make **all** the difference in your journey.
54 |
55 | 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**.
56 |
57 | - [MIT Challenge](https://www.scotthyoung.com/blog/myprojects/mit-challenge-2/)
58 |
59 | 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.
60 |
61 | - [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn)
62 |
63 | **Are you ready to get started?**
64 |
65 | ## Curriculum
66 |
67 | - [Linear Algebra](#linear-algebra)
68 | - [Single Variable Calculus](#single-variable-calculus)
69 | - [Multivariable Calculus](#multivariable-calculus)
70 | - [Python](#python)
71 | - [Probability and Statistics](#probability-and-statistics)
72 | - [Introduction to Data Science](#introduction-to-data-science)
73 | - [Machine Learning](#machine-learning)
74 | - [Project](#project)
75 | - [Convex Optimization](#convex-optimization)
76 | - [Data Wrangling](#data-wrangling)
77 | - [Big Data](#big-data)
78 | - [Database](#database)
79 | - [Deep Learning](#deep-learning)
80 | - [Natural Language Processing](#natural-language-processing)
81 | - [Capstone Project](#capstone-project)
82 | - [Specializations](#specializations)
83 |
84 |
85 | ---
86 |
87 | ### Linear Algebra
88 |
89 | Courses | Duration | Effort
90 | :-- | :--: | :--:
91 | [Linear Algebra - Foundations to Frontiers](https://www.edx.org/course/linear-algebra-foundations-frontiers-utaustinx-ut-5-04x#!)| 15 weeks | 8 hours/week
92 | [Applications of Linear Algebra Part 1](https://www.edx.org/course/applications-linear-algebra-part-1-davidsonx-d003x-1)| 5 weeks | 4 hours/week
93 | [Applications of Linear Algebra Part 2](https://www.edx.org/course/applications-linear-algebra-part-2-davidsonx-d003x-2)| 4 weeks | 5 hours/week
94 |
95 | ### Single Variable Calculus
96 | Courses | Duration | Effort
97 | :-- | :--: | :--:
98 | [Calculus 1A: Differentiation](https://www.edx.org/course/calculus-1a-differentiation-mitx-18-01-1x)| 13 weeks | 6-10 hours/week
99 | [Calculus 1B: Integration](https://www.edx.org/course/calculus-1b-integration-mitx-18-01-2x)| 13 weeks | 5-10 hours/week
100 | [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
101 |
102 | ### Multivariable Calculus
103 | Courses | Duration | Effort
104 | :-- | :--: | :--:
105 | [MIT OCW Multivariable Calculus](http://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm)| 15 weeks | 8 hours/week
106 |
107 | ### Python
108 | Courses | Duration | Effort
109 | :-- | :--: | :--:
110 | [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
111 | [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
112 | [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
113 | [Programming with Python for Data Science](https://www.edx.org/course/programming-python-data-science-microsoft-dat210x)| 6 weeks | 3-4 hours/week
114 |
115 | ### Probability and Statistics
116 | Courses | Duration | Effort
117 | :-- | :--: | :--:
118 | [Introduction to Probability](https://www.edx.org/course/introduction-probability-science-mitx-6-041x-1#.U3yb762SzIo)| 16 weeks | 12 hours/week
119 | [Statistical Reasoning](https://lagunita.stanford.edu/courses/OLI/StatReasoning/Open/about)| - weeks | - hours/week
120 | [Introduction to Statistics: Descriptive Statistics](https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x)| 5 weeks | - hours/week
121 | [Introduction to Statistics: Probability](https://www.edx.org/course/introduction-statistics-probability-uc-berkeleyx-stat2-2x)| 5 weeks | - hours/week
122 | [Introduction to Statistics: Inference](https://www.edx.org/course/introduction-statistics-inference-uc-berkeleyx-stat2-3x)| 5 weeks | - hours/week
123 |
124 | ### Introduction to Data Science
125 | Courses | Duration | Effort
126 | :-- | :--: | :--:
127 | [Introduction to Data Science](https://www.coursera.org/course/datasci)| 8 weeks | 10-12 hours/week
128 | [Data Science - CS109 from Harvard](http://cs109.github.io/2015/)| 12 weeks | 5-6 hours/week
129 | [The Analytics Edge](https://www.edx.org/course/analytics-edge-mitx-15-071x-2)| 12 weeks | 10-15 hours/week
130 |
131 | ### Machine Learning
132 | Courses | Duration | Effort
133 | :-- | :--: | :--:
134 | [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
135 | [Statistical Learning](https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about)| - weeks | 3 hours/week
136 | [Stanford's Machine Learning Course](https://www.coursera.org/learn/machine-learning)| - weeks | 8-12 hours/week
137 |
138 | ### Project
139 | Complete Kaggle's Getting Started and Playground Competitions
140 |
141 |
142 | ### Convex Optimization
143 | Courses | Duration | Effort
144 | :-- | :--: | :--:
145 | [Convex Optimization](https://lagunita.stanford.edu/courses/Engineering/CVX101/Winter2014/about)| 9 weeks | 10 hours/week
146 |
147 | ### Data Wrangling
148 | Courses | Duration | Effort
149 | :-- | :--: | :--:
150 | [Data Wrangling with MongoDB](https://www.udacity.com/course/data-wrangling-with-mongodb--ud032)| 8 weeks | 10 hours/week
151 |
152 | ### Big Data
153 | Courses | Duration | Effort
154 | :-- | :--: | :--:
155 | [Intro to Hadoop and MapReduce](https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617)| 4 weeks | 6 hours/week
156 | [Deploying a Hadoop Cluster](https://www.udacity.com/course/deploying-a-hadoop-cluster--ud1000)| 3 weeks | 6 hours/week
157 |
158 | ### Database
159 | Courses | Duration | Effort
160 | :-- | :--: | :--:
161 | [Stanford's Database course](https://lagunita.stanford.edu/courses/DB/2014/SelfPaced/about)| - weeks | 8-12 hours/week
162 |
163 | ### Natural Language Processing
164 | Courses | Duration | Effort
165 | :-- | :--: | :--:
166 | [Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/)| - weeks | - hours/week
167 |
168 | ### Deep Learning
169 | Courses | Duration | Effort
170 | :-- | :--: | :--:
171 | [Deep Learning](https://www.udacity.com/course/deep-learning--ud730)| 12 weeks | 8-12 hours/week
172 |
173 | ### Capstone Project
174 | - Participate in Kaggle competition
175 | - List down other ideas
176 |
177 |
178 | ### Specializations
179 |
180 | After finishing the courses above, start your specializations on the topics that you have more interest.
181 | You can view a list of available specializations [here](https://github.com/open-source-society/data-science/blob/master/extras/specializations.md).
182 |
183 | 
184 |
185 | ## How to use this guide
186 |
187 | ### Order of the classes
188 |
189 | This guide was developed to be consumed in a linear approach. What does this mean? That you should complete one course at a time.
190 |
191 | 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.
192 |
193 | **If the course isn't open, do it anyway with the resources from the previous class.**
194 |
195 | ### Should I take all courses?
196 |
197 | **Yes!** The intention is to conclude **all** the courses listed here!
198 |
199 | ### Duration of the project
200 |
201 | 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**!
202 |
203 | 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.
204 |
205 | > See more about "Commit to a process, not a goal" [here](http://jamesclear.com/goals-systems).
206 |
207 | ### Project Based
208 |
209 | Here in **OSS University**, you do **not** need to take exams, because we are focused on **real projects**!
210 |
211 | In order to show for everyone that you **successfully** finished a course, you should create a **real project**.
212 |
213 | > "What does it mean?"
214 |
215 | 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.
216 |
217 | 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.
218 |
219 | **You can create this project alone or with other students!**
220 |
221 | #### Project Suggestions
222 |
223 |
224 |
225 | And you should also...
226 |
227 | ### Be creative!
228 |
229 | This is a **crucial** part of your journey through all those courses.
230 |
231 | 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**!
232 |
233 | In order to show that you **really** learned those things, you need to be **creative**!
234 |
235 | Here are some tips about how you can do that:
236 |
237 | - **Articles**: create blog posts to synthesize/summarize what you learned.
238 | - **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.
239 |
240 | ### Cooperative work
241 |
242 | **We love cooperative work**! Use our [channels](#community) to communicate with other fellows to combine and create new projects!
243 |
244 | ### Which programming languages should I use?
245 |
246 | Python and R are heavily used in Data Science community and our courses teach you both, but...
247 |
248 | 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.
249 |
250 | ### Content Policy
251 |
252 | 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.
253 |
254 | [Be creative](#be-creative) in order to show your progress! :smile:
255 |
256 | ### Stay tuned
257 |
258 | [Watch](https://help.github.com/articles/watching-repositories/) this repository for futures improvements and general information.
259 |
260 | ## Prerequisite
261 |
262 | The **only things** that you need to know are how to use **Git** and **GitHub**. Here are some resources to learn about them:
263 |
264 | **Note**: Just pick one of the courses below to learn the basics. You will learn a lot more once you get started!
265 |
266 | - [Try Git](https://try.github.io/levels/1/challenges/1)
267 | - [Git - the simple guide](http://rogerdudler.github.io/git-guide/)
268 | - [GitHub Training & Guides](https://www.youtube.com/playlist?list=PLg7s6cbtAD15G8lNyoaYDuKZSKyJrgwB-)
269 | - [GitHub Hello World](https://guides.github.com/activities/hello-world/)
270 | - [Git Immersion](http://gitimmersion.com/index.html)
271 | - [How to Use Git and GitHub](https://www.udacity.com/course/how-to-use-git-and-github--ud775)
272 |
273 | ## Change Log
274 |
275 |
276 |
277 | ## How to collaborate
278 |
279 | 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.
280 |
281 | 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.
282 |
283 | TODO:
284 | 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.
285 |
286 | 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*.
287 |
288 | **Let's do it together! =)**
289 |
290 | ## Code of Conduct
291 | [OSSU's code of conduct](https://github.com/ossu/code-of-conduct).
292 |
293 | ## Community
294 |
295 | Subscribe to our [newsletter](https://tinyletter.com/ossu).
296 |
297 | Use our [forum](https://github.com/ossu/forum) if you need some help.
298 |
299 | You can also interact through [GitHub issues](https://github.com/open-source-society/data-science/issues).
300 |
301 | We also have a chat room! [](https://gitter.im/open-source-society/data-science)
302 |
303 | Add **Open Source Society University** to your [Linkedin](https://www.linkedin.com/school/11272443/) and [Facebook](https://www.facebook.com/ossuniversity) profile!
304 |
305 | > **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**.
306 |
307 | ## Next Goals
308 |
309 | - [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.
310 |
311 | ## Team
312 |
313 | * **Curriculum Founder**: [Shouvik Roy](https://github.com/royshouvik)
314 | * **Curriculum Maintainer**: [Shouvik Roy](https://github.com/royshouvik)
315 | * **Contributors**: [contributors](https://github.com/open-source-society/data-science/graphs/contributors)
316 |
317 | ## References
318 |
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/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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