├── .github └── FUNDING.yml ├── .gitignore ├── README.md ├── assets ├── README.md └── styles │ └── main.css ├── components ├── Footer.vue ├── Head.vue ├── Logo.vue ├── Menu.vue └── README.md ├── content ├── algorithms-path.md ├── ds-path.md ├── introduction-path.md ├── ml-path.md ├── sql-path.md └── stats-path.md ├── layouts ├── README.md └── default.vue ├── middleware └── README.md ├── nuxt.config.js ├── package-lock.json ├── package.json ├── pages ├── README.md ├── about.vue ├── index.vue └── sections │ └── _slug.vue ├── plugins └── README.md ├── server └── index.js └── static ├── README.md └── favicon.ico /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | 3 | github: yesidays 4 | patreon: yesidays 5 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Created by .ignore support plugin (hsz.mobi) 2 | ### Node template 3 | # Logs 4 | /logs 5 | *.log 6 | npm-debug.log* 7 | yarn-debug.log* 8 | yarn-error.log* 9 | 10 | # Runtime data 11 | pids 12 | *.pid 13 | *.seed 14 | *.pid.lock 15 | 16 | # Directory for instrumented libs generated by jscoverage/JSCover 17 | lib-cov 18 | 19 | # Coverage directory used by tools like istanbul 20 | coverage 21 | 22 | # nyc test coverage 23 | .nyc_output 24 | 25 | # Grunt intermediate storage (http://gruntjs.com/creating-plugins#storing-task-files) 26 | .grunt 27 | 28 | # Bower dependency directory (https://bower.io/) 29 | bower_components 30 | 31 | # node-waf configuration 32 | .lock-wscript 33 | 34 | # Compiled binary addons (https://nodejs.org/api/addons.html) 35 | build/Release 36 | 37 | # Dependency directories 38 | node_modules/ 39 | jspm_packages/ 40 | 41 | # TypeScript v1 declaration files 42 | typings/ 43 | 44 | # Optional npm cache directory 45 | .npm 46 | 47 | # Optional eslint cache 48 | .eslintcache 49 | 50 | # Optional REPL history 51 | .node_repl_history 52 | 53 | # Output of 'npm pack' 54 | *.tgz 55 | 56 | # Yarn Integrity file 57 | .yarn-integrity 58 | 59 | # dotenv environment variables file 60 | .env 61 | 62 | # parcel-bundler cache (https://parceljs.org/) 63 | .cache 64 | 65 | # next.js build output 66 | .next 67 | 68 | # nuxt.js build output 69 | .nuxt 70 | 71 | # Nuxt generate 72 | dist 73 | 74 | # vuepress build output 75 | .vuepress/dist 76 | 77 | # Serverless directories 78 | .serverless 79 | 80 | # IDE / Editor 81 | .idea 82 | 83 | # Service worker 84 | sw.* 85 | 86 | # macOS 87 | .DS_Store 88 | 89 | # Vim swap files 90 | *.swp 91 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ds-learning-path 2 | 3 | Welcome, 4 | 5 | This is a personal project that I am starting, in it you can find a career plan and preparation for a Data Scientist with the demands of a FANG. 6 | 7 | Also, the visual and functionality development I am doing in Nuxt.js, it is the first time that I use it, so feedback is accepted to improve the structure and management of the project. 8 | 9 | If you want to communicate with me you can write to [@silvercorp](https://www.twitter.com/silvercorp) on Twitter. 10 | 11 | Be a [Patreon](https://www.patreon.com/yesidays) 12 | 13 | ## Build Setup 14 | 15 | ```bash 16 | # install dependencies 17 | $ npm install 18 | 19 | # serve with hot reload at localhost:3000 20 | $ npm run dev 21 | 22 | # build for production and launch server 23 | $ npm run build 24 | $ npm run start 25 | 26 | # generate static project 27 | $ npm run generate 28 | ``` 29 | 30 | For detailed explanation on how things work, check out [Nuxt.js docs](https://nuxtjs.org). 31 | -------------------------------------------------------------------------------- /assets/README.md: -------------------------------------------------------------------------------- 1 | # ASSETS 2 | 3 | **This directory is not required, you can delete it if you don't want to use it.** 4 | 5 | This directory contains your un-compiled assets such as LESS, SASS, or JavaScript. 6 | 7 | More information about the usage of this directory in [the documentation](https://nuxtjs.org/guide/assets#webpacked). 8 | -------------------------------------------------------------------------------- /components/Footer.vue: -------------------------------------------------------------------------------- 1 | 113 | 114 | -------------------------------------------------------------------------------- /components/Head.vue: -------------------------------------------------------------------------------- 1 | 57 | 58 | -------------------------------------------------------------------------------- /components/Logo.vue: -------------------------------------------------------------------------------- 1 | 20 | 35 | -------------------------------------------------------------------------------- /components/Menu.vue: -------------------------------------------------------------------------------- 1 | 26 | 27 | -------------------------------------------------------------------------------- /components/README.md: -------------------------------------------------------------------------------- 1 | # COMPONENTS 2 | 3 | **This directory is not required, you can delete it if you don't want to use it.** 4 | 5 | The components directory contains your Vue.js Components. 6 | 7 | _Nuxt.js doesn't supercharge these components._ 8 | -------------------------------------------------------------------------------- /content/algorithms-path.md: -------------------------------------------------------------------------------- 1 | # Algoritmia 2 | 3 | ### Cadenas 4 | 5 | - Substrings 6 | - [Introduction to Substring Search](https://www.coursera.org/lecture/algorithms-part2/introduction-to-substring-search-n3ZpG) 7 | - [Brute-Force Substring Search](https://www.coursera.org/lecture/algorithms-part2/brute-force-substring-search-2Kn5i) 8 | - [Knuth–Morris–Pratt](https://www.coursera.org/lecture/algorithms-part2/knuth-morris-pratt-TAtDr) 9 | - [Boyer–Moore](https://www.coursera.org/lecture/algorithms-part2/boyer-moore-CYxOT) 10 | - [Rabin–Karp](https://www.coursera.org/lecture/algorithms-part2/rabin-karp-3KiqT) 11 | - [Search pattern in string](https://www.youtube.com/watch?v=RgY6IDcwaqM) 12 | - [Hacker Rank - Problemas](https://www.hackerrank.com/domains/algorithms?filters%5Bsubdomains%5D%5B%5D=strings) 13 | 14 | ### Recursión/Backtracking 15 | 16 | - [Introduction to Recursion (Data Structures & Algorithms #6)](https://www.youtube.com/watch?v=B0NtAFf4bvU) 17 | - [How Recursion Works — explained with flowcharts and a video](https://medium.com/free-code-camp/how-recursion-works-explained-with-flowcharts-and-a-video-de61f40cb7f9) 18 | - [Recursion, Recursion, Recursion](https://medium.com/free-code-camp/recursion-recursion-recursion-4db8890a674d) 19 | - [Recursion](https://www.youtube.com/watch?v=mz6tAJMVmfM) 20 | - [Recursion for Beginners: A Beginner's Guide to Recursion](https://www.youtube.com/watch?v=AfBqVVKg4GE) 21 | - [Coding Challenge #77: Recursion](https://www.youtube.com/watch?v=jPsZwrV9ld0) 22 | - [Backtracking (Think Like a Programmer)](https://www.youtube.com/watch?v=gBC_Fd8EE8A) 23 | - [Backtracking explained](https://medium.com/@andreaiacono/backtracking-explained-7450d6ef9e1a) 24 | - [Backtracking: How to Approach Search Programming Interview Questions](https://medium.com/swlh/backtracking-how-to-approach-search-programming-interview-questions-647a742197) 25 | - [Backtracking (Playlist)](https://www.youtube.com/watch?v=keb6rP07Yqg&list=PLTZbNwgO5ebpqWBmBx0lpy9IYFMSQcrL-) 26 | - [Hacker Rank - Problemas](https://www.hackerrank.com/domains/algorithms?filters%5Bsubdomains%5D%5B%5D=recursion) 27 | 28 | ### **Sorting** 29 | 30 | **Teoría** 31 | 32 | - [Introduction to sorting algorithms (Playlist)](https://www.youtube.com/watch?v=pkkFqlG0Hds&list=PL2_aWCzGMAwKedT2KfDMB9YA5DgASZb3U) 33 | - [Stability in sorting algorithms](https://www.geeksforgeeks.org/stability-in-sorting-algorithms/) 34 | - [What is stability in sorting algorithms and why is it important?](https://stackoverflow.com/questions/1517793/what-is-stability-in-sorting-algorithms-and-why-is-it-important) 35 | - [An intro to Algorithms: Searching and Sorting algorithms](https://codeburst.io/algorithms-i-searching-and-sorting-algorithms-56497dbaef20) 36 | - [Sorting - Playlist](https://www.youtube.com/watch?v=P00xJgWzz2c&index=1&list=PL89B61F78B552C1AB) 37 | - [Sorting - GeeksforGeeks (Playlist - Visual only)](https://www.youtube.com/watch?v=MtQL_ll5KhQ&list=PLqM7alHXFySHrGIxeBOo4-mKO4H8j2knW) 38 | - [3 Levels of Sorting Algorithms - FASTEST Comparison Sort!](https://www.youtube.com/watch?v=qk7b4-iyCJ4) 39 | - [15 Sorting Algorithms in 6 Minutes](https://www.youtube.com/watch?v=kPRA0W1kECg) (Visual only) 40 | - [QuickSort](https://www.coursera.org/learn/algorithms-part1/home/week/3) (4 videos) - Sedgewick (Coursera) 41 | - [MergeSort](https://www.coursera.org/learn/algorithms-part1/home/week/3) (5 videos) - Sedgewick (Coursera) 42 | - [CS 61B Lecture 29: Sorting I (video)](https://archive.org/details/ucberkeley_webcast_EiUvYS2DT6I) 43 | - [CS 61B Lecture 30: Sorting II (video)](https://archive.org/details/ucberkeley_webcast_2hTY3t80Qsk) 44 | - [CS 61B Lecture 32: Sorting III (video)](https://archive.org/details/ucberkeley_webcast_Y6LOLpxg6Dc) 45 | - [CS 61B Lecture 33: Sorting V (video)](https://archive.org/details/ucberkeley_webcast_qNMQ4ly43p4) 46 | 47 | **Práctica** 48 | 49 | - [Leetcode - Sorting](https://leetcode.com/problemset/all/?topicSlugs=sort) 50 | - [Hacker Rank - Sorting](https://www.hackerrank.com/domains/algorithms?filters%5Bstatus%5D%5B%5D=unsolved&filters%5Bsubdomains%5D%5B%5D=arrays-and-sorting&badge_type=problem-solving) 51 | 52 | ### Estructuras de datos 53 | 54 | **Arreglos - Teoría** 55 | 56 | - [Definition of Array](https://www.youtube.com/watch?v=55l-aZ7_F24) 57 | - [Array Explained In 1 Minute](https://www.youtube.com/watch?v=6tjYC86iV5E) 58 | - [Basic Arrays (video)](https://archive.org/details/0102WhatYouShouldKnow/02_04-basicArrays.mp4) 59 | - [Arrays](https://www.coursera.org/lecture/data-structures/arrays-OsBSF) 60 | - [Multi-dim (video)](https://archive.org/details/0102WhatYouShouldKnow/02_05-multidimensionalArrays.mp4) 61 | - [Dynamic arrays](https://www.coursera.org/lecture/data-structures/dynamic-arrays-EwbnV) 62 | - [Jagged Arrays (video)](https://www.youtube.com/watch?v=1jtrQqYpt7g) 63 | - [Jagged Arrays (video)](https://archive.org/details/0102WhatYouShouldKnow/02_06-jaggedArrays.mp4) 64 | - [Resizing arrays (video)](https://archive.org/details/0102WhatYouShouldKnow/03_01-resizableArrays.mp4) 65 | - [Arrays of Flexible Size - Processing Tutorial](https://www.youtube.com/watch?v=Hf-AHwRaodA) 66 | 67 | **Arreglos - Práctica** 68 | 69 | - [Hacker Rank - Problem solving](https://www.hackerrank.com/domains/data-structures?filters%5Bsubdomains%5D%5B%5D=arrays) 70 | - [Leetcode - Array](https://leetcode.com/problemset/all/?topicSlugs=array) 71 | - [Python: Array Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/array/) 72 | 73 | **Listas - Teoría** 74 | 75 | - [Introduction to Linked Lists](https://www.youtube.com/watch?v=WwfhLC16bis) 76 | - [Singly-Linked Lists](https://www.coursera.org/lecture/data-structures/singly-linked-lists-kHhgK) 77 | - [Data Structures: Linked Lists](https://www.youtube.com/watch?v=njTh_OwMljA) 78 | - [Data Structures: Singly Linked List in Python 3 (Playlist - 10 videos)](https://www.youtube.com/watch?v=XGSoQ0J5An4&list=PLzjoZGHG3J8vdUH75YPqmO7lbQl_M-xXo) 79 | - [Linked List 1](https://archive.org/details/ucberkeley_webcast_htzJdKoEmO0) 80 | - [Linked list 2](https://archive.org/details/ucberkeley_webcast_-c4I3gFYe3w) 81 | - [Doubly-Linked Lists](https://www.coursera.org/lecture/data-structures/doubly-linked-lists-jpGKD) 82 | - [Data Structures in Python: Doubly Linked Lists (Playlist)](https://www.youtube.com/watch?v=8kptHdreaTA&list=PL5tcWHG-UPH3nDinW5u_oRcNv6hwhY7ET) 83 | - [Bjarne Stroustrup: Why you should avoid Linked Lists](https://www.youtube.com/watch?v=YQs6IC-vgmo) 84 | - [List vs Array — Data Types](https://medium.com/backticks-tildes/list-vs-array-python-data-type-40ac4f294551) 85 | - [Array vs Linked List Data Structures](https://levelup.gitconnected.com/array-vs-linked-list-data-structure-c5c0ff405f16) 86 | 87 | **Listas - Práctica** 88 | 89 | - [Python: Linked List - Exercises, Practice, Solution](https://www.w3resource.com/python-exercises/data-structures-and-algorithms/python-linked-list.php) 90 | - [Hacker Rank: Problem Solving](https://www.hackerrank.com/domains/data-structures?filters%5Bsubdomains%5D%5B%5D=linked-lists) 91 | - [Leetcode - Linked list](https://leetcode.com/problemset/all/?topicSlugs=linked-list) 92 | 93 | **Pilas - Teoría** 94 | 95 | - [Data Structures: Stacks and Queues](https://www.youtube.com/watch?v=wjI1WNcIntg) 96 | - [Data structures: Introduction to stack](https://www.youtube.com/watch?v=F1F2imiOJfk) 97 | - [What is a Stack Data Structure - An Introduction to Stacks](https://www.youtube.com/watch?v=FNZ5o9S9prU) 98 | - [Using Stacks Last-In First-Out (video)](https://archive.org/details/0102WhatYouShouldKnow/05_01-usingStacksForLast-inFirst-out.mp4) 99 | - [A Gentle Introduction to Data Structures: How Stacks Work](https://medium.com/free-code-camp/data-structures-stacks-on-stacks-c25f2633c529) 100 | - [Stacks and Queues in Python](https://levelup.gitconnected.com/stacks-and-queues-in-python-b2e8b4dbd876) 101 | 102 | **Pilas - Práctica** 103 | 104 | - [Hacker Rank - Problem solving](https://www.hackerrank.com/domains/data-structures?filters%5Bsubdomains%5D%5B%5D=stacks) 105 | - [Leetcode Stack](https://leetcode.com/problemset/all/?topicSlugs=stack) 106 | - [Stacks and Queues in Python](https://stackabuse.com/stacks-and-queues-in-python/) 107 | 108 | **Colas - Teoría** 109 | 110 | - [Data structures: Introduction to Queues](https://www.youtube.com/watch?v=XuCbpw6Bj1U) 111 | - [Using Queues First-In First-Out(video)](https://archive.org/details/0102WhatYouShouldKnow/05_03-usingQueuesForFirst-inFirst-out.mp4) 112 | - [Queue (video)](https://www.coursera.org/lecture/data-structures/queues-EShpq) 113 | - [Priority Queues (video)](https://archive.org/details/0102WhatYouShouldKnow/05_04-priorityQueuesAndDeques.mp4) 114 | - [Stacks and Queues in Python](https://levelup.gitconnected.com/stacks-and-queues-in-python-b2e8b4dbd876) 115 | - [Introduction to Priority Queues in Python](https://towardsdatascience.com/introduction-to-priority-queues-in-python-83664d3178c3) 116 | 117 | **Colas - Práctica** 118 | 119 | - [Hacker Rank - Problem solving](https://www.hackerrank.com/domains/data-structures?filters%5Bsubdomains%5D%5B%5D=queues) 120 | - [Leetcode - Queue](https://leetcode.com/problemset/all/?topicSlugs=queue) 121 | 122 | **Hash table** 123 | 124 | - [Understanding “Hash-Table” without any code](https://medium.com/@cakrawalabuka/understanding-hash-table-without-any-code-9a03dbeb028d) 125 | - [Data Structures: Hash Tables](https://www.youtube.com/watch?v=shs0KM3wKv8&t=292s) 126 | - [Hash Tables in Python](https://www.youtube.com/watch?v=zHi5v78W1f0) 127 | - [What is a HashTable Data Structure - Introduction](https://www.youtube.com/watch?v=MfhjkfocRR0) 128 | - [Hash Tables](https://www.youtube.com/watch?v=nvzVHwrrub0) 129 | - [Hashing and Hash table in data structure and algorithm](https://www.youtube.com/watch?v=Ke_tII6Y0GE) 130 | - [Hashing - Playlist](https://www.youtube.com/watch?v=wWgIAphfn2U&list=PLqM7alHXFySGwXaessYMemAnITqlZdZVE) 131 | - [Table Doubling, Karp-Rabin](https://www.youtube.com/watch?v=BRO7mVIFt08&index=9&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb) 132 | - [PyCon 2010: The Mighty Dictionary](https://www.youtube.com/watch?v=C4Kc8xzcA68) 133 | - [Understanding Hash Functions](https://archive.org/details/0102WhatYouShouldKnow/06_02-understandingHashFunctions.mp4) 134 | - [Using Hash Tables](https://archive.org/details/0102WhatYouShouldKnow/06_03-usingHashTables.mp4) 135 | - [Supporting Hashing](https://archive.org/details/0102WhatYouShouldKnow/06_04-supportingHashing.mp4) 136 | 137 | **Hash table - Práctica** 138 | 139 | - [Leet code - Hash table](https://leetcode.com/problemset/all/?topicSlugs=hash-table) 140 | - [Hash Table implementation in Python](http://blog.chapagain.com.np/hash-table-implementation-in-python-data-structures-algorithms/) 141 | - [Hashing](https://runestone.academy/runestone/books/published/pythonds/SortSearch/Hashing.html) 142 | 143 | ### 144 | 145 | **Árboles - Teoría** 146 | 147 | - [Data structures: Introduction to Trees](https://www.youtube.com/watch?v=qH6yxkw0u78) 148 | - [Data Structures: Trees](https://www.youtube.com/watch?v=oSWTXtMglKE) 149 | - [Trees](http://www.openbookproject.net/thinkcs/python/english2e/ch21.html) 150 | - [Trees (Playlist)](https://www.youtube.com/watch?v=IpyCqRmaKW4&list=PLqM7alHXFySHCXD7r1J0ky9Zg_GBB1dbk) 151 | 152 | **Árboles - Práctica** 153 | 154 | - [Making Data Trees in Python](https://medium.com/swlh/making-data-trees-in-python-3a3ceb050cfd) 155 | 156 | **Binary search trees: BTS - Teoría** 157 | 158 | - [BFS and DFS in a Binary Tree](https://www.youtube.com/watch?v=uWL6FJhq5fM) 159 | - [Introduction BFS - Coursera](https://www.coursera.org/lecture/data-structures-optimizing-performance/introduction-Ct2em) 160 | - [Binary Trees and Binary Search (BST)](https://www.youtube.com/watch?v=RBSGKlAvoiM) - 3:03:55 161 | - [Binary Trees in Python: Introduction and Traversal Algorithms](https://www.youtube.com/watch?v=6oL-0TdVy28) 162 | - [Binary Search Tree Review (video)](https://www.youtube.com/watch?v=x6At0nzX92o&index=1&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6) 163 | - [Binary Search Trees, BST Sort - MIT](https://www.youtube.com/watch?v=9Jry5-82I68) 164 | 165 | **Binary search trees: BTS - Práctica** 166 | 167 | - [Leetcode - Examples](https://leetcode.com/problemset/all/?topicSlugs=binary-search-tree) 168 | - [Binary Search Tree - Code](https://www.geeksforgeeks.org/binary-search-tree-set-1-search-and-insertion/) 169 | - [How to Implement a Binary Search Tree in Python](https://medium.com/@stephenagrice/how-to-implement-a-binary-search-tree-in-python-e1cdba29c533) 170 | 171 | **Heap / Priority Queue / Binary Heap - Teoría** 172 | 173 | - [Data Structures: Heaps](https://www.youtube.com/watch?v=t0Cq6tVNRBA) 174 | - [Heap - Build Max Heap](https://www.youtube.com/watch?v=WsNQuCa_-PU) 175 | - [6 Steps to Understanding a Heap with Python](https://towardsdatascience.com/data-structure-heap-23d4c78a6962) 176 | - [Understanding Heap Data Structure](https://medium.com/swlh/understanding-heap-data-structure-54efe7b2fbd7) 177 | - [Heaps and Heap Sort](https://www.youtube.com/watch?v=B7hVxCmfPtM&index=4&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb) 178 | - [Priority Queues](https://archive.org/details/ucberkeley_webcast_yIUFT6AKBGE) 179 | 180 | **Heap / Priority Queue / Binary Heap - Práctica** 181 | 182 | - [Implement A Binary Heap - An Efficient Implementation of The Priority Queue ADT (Abstract Data Type)](https://www.youtube.com/watch?v=g9YK6sftDi0) 183 | - [Heap Practice Problems and Interview Questions](https://medium.com/@codingfreak/heap-practice-problems-and-interview-questions-b678ff3b694c) 184 | - [Hacker Rank - Solving](https://www.hackerrank.com/domains/data-structures?filters%5Bsubdomains%5D%5B%5D=heap) 185 | - [Leetcode - Problems](https://leetcode.com/problemset/all/?topicSlugs=heap) 186 | 187 | **Graphs - Teoría** 188 | 189 | - [Algorithms Course - Graph Theory Tutorial from a Google Engineer](https://www.youtube.com/watch?v=09_LlHjoEiY) 190 | - [Graph Data Structure Intro (inc. adjacency list, adjacency matrix, incidence matrix)](https://www.youtube.com/watch?v=DBRW8nwZV-g) 191 | - [Graph Data Structure 1. Terminology and Representation (algorithms)](https://www.youtube.com/watch?v=c8P9kB1eun4) 192 | - [Data structures: Introduction to graphs](https://www.youtube.com/watch?v=gXgEDyodOJU) 193 | - [Lecture: Graph Data Structures (Playlist)](https://www.youtube.com/watch?v=OiXxhDrFruw&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=11) 194 | - [Single-Source Shortest Paths Problem](https://www.youtube.com/watch?v=Aa2sqUhIn-E&index=15&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb) 195 | - [Coursera: Algorithms on Graphs - full course](https://www.coursera.org/learn/algorithms-on-graphs) 196 | - [Dijkstra](https://www.youtube.com/watch?v=2E7MmKv0Y24&index=16&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb) 197 | - [Bellman-Ford (video)](https://www.youtube.com/watch?v=ozsuci5pIso&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=17) 198 | - [Speeding Up Dijkstra (video)](https://www.youtube.com/watch?v=CHvQ3q_gJ7E&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=18) 199 | - [Aduni: Graph Algorithms I - Topological Sorting, Minimum Spanning Trees, Prim's Algorithm - Lecture 6 (video)](https://www.youtube.com/watch?v=i_AQT_XfvD8&index=6&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm) 200 | - [Aduni: Graph Algorithms II - DFS, BFS, Kruskal's Algorithm, Union Find Data Structure - Lecture 7 (video)](https://www.youtube.com/watch?v=ufj5_bppBsA&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=7) 201 | - [Aduni: Graph Algorithms III: Shortest Path - Lecture 8 (video)](https://www.youtube.com/watch?v=DiedsPsMKXc&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=8) 202 | - [Aduni: Graph Alg. IV: Intro to geometric algorithms - Lecture 9 (video)](https://www.youtube.com/watch?v=XIAQRlNkJAw&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=9) 203 | - [CS 61B 2014: Weighted graphs (video)](https://archive.org/details/ucberkeley_webcast_zFbq8vOZ_0k) 204 | - [Greedy Algorithms: Minimum Spanning Tree (video)](https://www.youtube.com/watch?v=tKwnms5iRBU&index=16&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp) 205 | - [Strongly Connected Components Kosaraju's Algorithm Graph Algorithm (video)](https://www.youtube.com/watch?v=RpgcYiky7uw) 206 | 207 | **Graphs - Práctica** 208 | 209 | - [Hacker Rank - Examples](https://www.hackerrank.com/domains/algorithms?filters%5Bstatus%5D%5B%5D=unsolved&filters%5Bsubdomains%5D%5B%5D=graph-theory&badge_type=problem-solving) 210 | - [Leetcode - Graph](https://leetcode.com/problemset/all/?topicSlugs=graph) 211 | 212 | **Dynamic programming - Teoría** 213 | 214 | - [A Gentle Introduction to Data Structures: How Graphs Work](https://medium.com/free-code-camp/a-gentle-introduction-to-data-structures-how-graphs-work-a223d9ef8837) 215 | - [What Is Dynamic Programming and How To Use It](https://www.youtube.com/watch?v=vYquumk4nWw) 216 | - [An intro to Algorithms: Dynamic Programming](https://medium.com/free-code-camp/an-intro-to-algorithms-dynamic-programming-dd00873362bb) 217 | - [Greedy Algorithm and Dynamic Programming](https://medium.com/cracking-the-data-science-interview/greedy-algorithm-and-dynamic-programming-a8c019928405) 218 | - [Dynamic Programming vs Divide-and-Conquer](https://itnext.io/dynamic-programming-vs-divide-and-conquer-2fea680becbe) 219 | - [A graphical introduction to dynamic programming](https://medium.com/@avik.das/a-graphical-introduction-to-dynamic-programming-2e981fa7ca2) 220 | - [Less Repetition, More Dynamic Programming](https://medium.com/basecs/less-repetition-more-dynamic-programming-43d29830a630) 221 | - [Exploring Data Structures: Graphs and its traversal algorithms](https://medium.com/hackernoon/graphs-in-cs-and-its-traversal-algorithms-cfee5533f74e) 222 | - [Dynamic Programming: An induction approach](https://medium.com/@tiagot/dynamic-programming-an-induction-approach-b5c5e73c4a19) 223 | - [CSE373 2012 - Lecture 19 - Introduction to Dynamic Programming (video)](https://youtu.be/Qc2ieXRgR0k?list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&t=1718) 224 | - [Skiena: CSE373 2012 - Lecture 21 - Dynamic Programming Examples (video)](https://youtu.be/o0V9eYF4UI8?list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&t=406) 225 | 226 | **Dynamic programming - Práctica** 227 | 228 | - [Top 50 Dynamic Programming Practice Problems](https://blog.usejournal.com/top-50-dynamic-programming-practice-problems-4208fed71aa3) 229 | - [How to solve the Knapsack Problem with dynamic programming](https://medium.com/@fabianterh/how-to-solve-the-knapsack-problem-with-dynamic-programming-eb88c706d3cf) 230 | - [Hacker Rank - Dynamic programming](https://www.hackerrank.com/domains/algorithms?filters%5Bstatus%5D%5B%5D=unsolved&filters%5Bsubdomains%5D%5B%5D=dynamic-programming&badge_type=problem-solving) 231 | - [Leet code - Dynamic programming](https://leetcode.com/problemset/all/?topicSlugs=dynamic-programming) 232 | 233 | ### Complejidad computacional 234 | 235 | - [Computational Complexity](https://www.youtube.com/watch?v=YoZPTyGL2IQ) 236 | - [Introduction to Big O Notation and Time Complexity](https://www.youtube.com/watch?v=D6xkbGLQesk&t=25s) 237 | - [The Ultimate Big O Notation Tutorial (Time & Space Complexity For Algorithms)](https://www.youtube.com/watch?v=waPQP2TDOGE) 238 | - [Deeply Understanding Logarithms In Time Complexities & Their Role In Computer Science](https://www.youtube.com/watch?v=M4ubFru2O80) 239 | - [Big O Notation](https://www.youtube.com/watch?v=v4cd1O4zkGw) 240 | - [What Is Big O? (Comparing Algorithms)](https://www.youtube.com/watch?v=MyeV2_tGqvw) 241 | - [What is Big O? (Comparing Algorithms)](https://www.youtube.com/watch?v=MyeV2_tGqvw) 242 | - [Big-O notation in 5 minutes — The basics](https://www.youtube.com/watch?v=__vX2sjlpXU) 243 | - [Illustrating "Big O" (video)](https://www.coursera.org/lecture/algorithmic-thinking-1/illustrating-big-o-YVqzv) 244 | - [Complete Beginner's Guide to Big O Notation](https://www.youtube.com/watch?v=kS_gr2_-ws8) 245 | - [P vs. NP and the Computational Complexity Zoo](https://www.youtube.com/watch?v=YX40hbAHx3s) 246 | - [Lecture MIT: Computational Complexity](https://www.youtube.com/watch?v=moPtwq_cVH8&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=23) 247 | 248 | ### Adicionales 249 | 250 | - Coursera: [Algorithmic Thinking](https://www.coursera.org/learn/algorithmic-thinking-1/home/welcome) 251 | - [Pramp: Mock interviews](https://www.pramp.com/invt/7Gm1JLVnzVfv4v9Q0B2W) 252 | - [Cheat sheet](http://bigocheatsheet.com/) - Know Thy Complexities! 253 | - [Nick White - LeetCode Solutions (187 Videos)](https://www.youtube.com/playlist?list=PLU_sdQYzUj2keVENTP0a5rdykRSgg9Wp-) 254 | - [IDeserve (88 videos)](https://www.youtube.com/watch?v=NBcqBddFbZw&list=PLamzFoFxwoNjPfxzaWqs7cZGsPYy0x_gI) 255 | - [Fisher coder - Leetcode (82 videos)](https://www.youtube.com/playlist?list=PLK0ZC7fyo01Jr4CwyEGPB_YYVYqoJS7LR) 256 | - [Tushar Roy - Coding Made Simple (5 playlist)](https://www.youtube.com/user/tusharroy2525/playlists?shelf_id=2&view=50&sort=dd) 257 | - [Interactive Coding Interview Challenges in Python](https://github.com/donnemartin/interactive-coding-challenges) 258 | - [Introduction to Big O Notation and Time Complexity](https://www.youtube.com/watch?v=D6xkbGLQesk) 259 | - [What is Big O? (Comparing Algorithms)](https://www.youtube.com/watch?v=MyeV2_tGqvw) 260 | - [The Ultimate Big O Notation Tutorial (Time & Space Complexity For Algorithms)](https://www.youtube.com/watch?v=waPQP2TDOGE) 261 | - [How to Find a Solution](https://www.topcoder.com/community/competitive-programming/tutorials/how-to-find-a-solution/) 262 | -------------------------------------------------------------------------------- /content/ds-path.md: -------------------------------------------------------------------------------- 1 | # Ciencia de Datos 2 | 3 | ### Conceptos 4 | 5 | - [¿Qué es la Ciencia de datos?](https://www.youtube.com/watch?v=X3paOmcrTjQ) 6 | - [¿Qué es un Científico de Datos?](https://www.youtube.com/watch?v=xC-c7E5PK0Y) 7 | 8 | ### Caja de herramientas para el científico de datos 9 | 10 | - Lenguaje de programación 11 | - Python 12 | - [Learn Python - Full course for beginners (4hrs)](https://www.youtube.com/watch?v=rfscVS0vtbw) 13 | - [Python Course](https://www.python-course.eu/python3_course.php) (Web) 14 | - [Google's Python Class](https://developers.google.com/edu/python/) 15 | - [Python for you and me](https://pymbook.readthedocs.io/en/latest/index.html) 16 | - [Learnpython.org](https://www.learnpython.org/) 17 | - [Python on Kaggle](https://www.kaggle.com/learn/python) 18 | - Paquetes fundamentales 19 | - Pandas 20 | - [10 minutes to pandas](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html) 21 | - [Learn Pandas](https://www.kaggle.com/learn/pandas) 22 | - [Exercises](https://www.learnpython.org/es/Pandas%20Basics) 23 | - Numpy 24 | - [Numpy for beginners](https://www.youtube.com/watch?v=QUT1VHiLmmI) (Video) 25 | - [Basics of Numpy](https://www.youtube.com/watch?v=xECXZ3tyONo) (Video) 26 | - [Exercises](https://www.learnpython.org/en/Numpy_Arrays) 27 | - SciPy 28 | - [Conceptos](https://www.guru99.com/scipy-tutorial.html) 29 | - Ambientes recomendados 30 | - [Jupyter](https://jupyter.org/) 31 | - [Google Colab](https://colab.research.google.com/) 32 | - IDE 33 | 34 | ### Colectar datos 35 | 36 | - [Lectura](https://realpython.com/pandas-read-write-files/) 37 | - CSV 38 | - Excel 39 | - JSON 40 | - Base de datos 41 | - API 42 | 43 | ### Preparación y exploración de los datos 44 | 45 | - [Selección de datos](https://medium.com/dunder-data/selecting-subsets-of-data-in-pandas-6fcd0170be9c) 46 | - [Subconjuntos](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html) 47 | - [Ordenamiento](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html) 48 | - [Agrupación](https://realpython.com/pandas-groupby/) 49 | - [Datos faltantes](https://www.geeksforgeeks.org/working-with-missing-data-in-pandas/) 50 | - [Filtrado](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.filter.html) 51 | - [Unión de los datos](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html) 52 | - [Reducción de dimensionalidad](https://towardsdatascience.com/dimension-reduction-techniques-with-python-f36ca7009e5c) 53 | 54 | En este punto te aconsejo que primero comprendas el concepto de reducción de la dimensionalidad y después te enfoques al código. 55 | 56 | ### Modelado 57 | 58 | - Cross-validation 59 | - [Web](https://towardsdatascience.com/train-test-split-and-cross-validation-in-python-80b61beca4b6) 60 | - Regresión 61 | - [Web](https://realpython.com/linear-regression-in-python/) 62 | - Árboles de decisión 63 | - [Web](https://towardsdatascience.com/decision-tree-in-python-b433ae57fb93) 64 | - Clustering 65 | - [Web](https://machinelearningmastery.com/clustering-algorithms-with-python/) 66 | 67 | ### Evaluación 68 | 69 | - Matriz de confusión 70 | - [Simple guide to confusion matrix terminology](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) 71 | - [Understanding Confusion Matrix](https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62) 72 | - [The confusion matrix (Video)](https://www.youtube.com/watch?v=Kdsp6soqA7o) 73 | - Exactitud (accuracy) 74 | - [Accuracy, Recall and Precision (Video)](https://www.youtube.com/watch?v=VPZiJGNX4_s) 75 | - [Metrics to evaluate your machine learning algorithm](https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234#:~:text=Classification%20Accuracy,-Classification%20Accuracy%20is&text=It%20is%20the%20ratio%20of,B%20in%20our%20training%20set.) 76 | - [Google: Classification Accuracy](https://developers.google.com/machine-learning/crash-course/classification/accuracy) 77 | - Recall 78 | - [Accuracy, Recall and Precision (Video)](https://www.youtube.com/watch?v=VPZiJGNX4_s) 79 | - [Google: Precision and Recall](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall) 80 | - Precisión 81 | - [Accuracy, Recall and Precision (Video)](https://www.youtube.com/watch?v=VPZiJGNX4_s) 82 | - [Google: Precision and Recall](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall) 83 | - F1 Score 84 | - [Introduction to precision, recall and F1](https://www.youtube.com/watch?v=jJ7ff7Gcq34) (Video) 85 | - Curve ROC 86 | - [ROC curve and AUC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc) 87 | - Evaluación para problemas de agrupación (clustering) 88 | - Similitud de coseno 89 | - [Cosine Similarity – Understanding the math and how it works (with python codes)](https://www.machinelearningplus.com/nlp/cosine-similarity/) 90 | - Distancia de Manhattan 91 | - [Euclidean Distance and Manhattan Distance](https://www.youtube.com/watch?v=p3HbBlcXDTE) 92 | - [Different Types of Distance Metrics used in Machine Learning](https://medium.com/@kunal_gohrani/different-types-of-distance-metrics-used-in-machine-learning-e9928c5e26c7) 93 | - Distancia Euclídea 94 | - [Euclidean Distance and Manhattan Distance](https://www.youtube.com/watch?v=p3HbBlcXDTE) 95 | - [Different Types of Distance Metrics used in Machine Learning](https://medium.com/@kunal_gohrani/different-types-of-distance-metrics-used-in-machine-learning-e9928c5e26c7) 96 | - Coeficiente de silueta 97 | - [Selecting the number of clusters with silhouette analysis on KMeans clustering](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html) 98 | - [Silhoutte Score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html) 99 | 100 | ### Deployment 101 | 102 | - [Con Flask](https://www.youtube.com/watch?v=-UYyyeYJAoQ) 103 | - [Microservicio](https://towardsdatascience.com/machine-learning-models-as-micro-services-in-docker-a798e1f068a5) 104 | - Apache Airflow 105 | - [Tutorial (Playlist)](https://www.youtube.com/watch?v=AHMm1wfGuHE&list=PLYizQ5FvN6pvIOcOd6dFZu3lQqc6zBGp2) 106 | 107 | ### A/B testing 108 | 109 | - [What is A/B Testing? | Data Science in Minutes](https://www.youtube.com/watch?v=zFMgpxG-chM) 110 | - [A/B Test Like a Pro #1: Preparing for A/B Testing](https://www.youtube.com/watch?v=ph-gNsKX2oA) 111 | - [A/B Testing](https://www.youtube.com/watch?v=8H6QmMQWPEI) 112 | - [What is A/B Testing & How to Use It](https://www.youtube.com/watch?v=NCIEe1me9oE) 113 | - [A/B Testing & Statistical Significance - 4 Steps to Know How to Call a Winning Test](https://www.youtube.com/watch?v=c5YaKCMraO8) 114 | - [A/B Testing - You’re doing it wrong](https://medium.com/hackernoon/a-b-testing-youre-doing-it-wrong-77d628ac9518) 115 | - [Beyond A/B Testing: Multi-armed Bandit Experiments](https://towardsdatascience.com/beyond-a-b-testing-multi-armed-bandit-experiments-1493f709f804) 116 | 117 | ### Servicios en la nube 118 | 119 | - AWS Sagemaker 120 | - [Why do we need AWS Sagemaker?](https://towardsdatascience.com/why-do-we-need-aws-sagemaker-79bce465f19f) 121 | - [Introduction to AWS SageMaker (Playlist)](https://www.youtube.com/watch?v=8Vj7OaR4DcA) 122 | - [Amazon documentación oficial](https://aws.amazon.com/es/sagemaker/) 123 | - Azure Machine Learning Studio 124 | - [Documentación oficial](https://azure.microsoft.com/es-mx/services/machine-learning/) 125 | - [A simple hands-on tutorial of Azure Machine Learning Studio](https://medium.com/data-science-reporter/a-simple-hands-on-tutorial-of-azure-machine-learning-studio-b6f05595dd73) 126 | - [Azure ML Studio (Playlist)](https://www.youtube.com/watch?v=uBYu_yrz_ZQ&list=PL8eNk_zTBST_WSR_KUBex8TDnQ21GetSG) 127 | - Cloud AI 128 | - [Documentación oficial](https://cloud.google.com/products/ai?hl=es) 129 | - [AI Adventures (Playlist)](https://www.youtube.com/watch?v=HcqpanDadyQ&list=PLIivdWyY5sqJxnwJhe3etaK7utrBiPBQ2) 130 | - IBM Watson 131 | - [Getting started with watson assistant](https://cloud.ibm.com/docs/assistant?topic=assistant-getting-started) 132 | - [IBM Watson for beginners tutorials](https://www.youtube.com/watch?v=qcqhHzelIQs&list=PLTgRMOcmRb3MbVexWhbPpFzMiFQVuvVlR) (Playlist) -------------------------------------------------------------------------------- /content/introduction-path.md: -------------------------------------------------------------------------------- 1 | # Introducción 2 | 3 | **Este documento tiene como propósito presentar una recomendación de guía de estudio y preparación para un Científico de Datos que cubre desde los aspectos básicos hasta niveles avanzados.** 4 | 5 | Los Científicos de Datos de acuerdo a diversas definiciones es una persona que tiene conocimientos de estadística, programación y análisis de datos. De acuerdo con Wikipedia tiene las siguientes habilidades: 6 | 7 | - Recopilar, procesar y extraer valor de las diversas y extensas bases de datos. 8 | - Imaginación para comprender, visualizar y comunicar sus conclusiones a los no científicos de datos. 9 | - Capacidad para crear soluciones basadas en datos que aumentan los beneficios, reducen los costos. 10 | - Los científicos de datos trabajan en todas las industrias y hacen frente a los grandes proyectos de datos en todos los niveles. 11 | 12 | [Leer más](https://es.wikipedia.org/wiki/Ciencia_de_datos#Cient%C3%ADfico_de_datos) 13 | 14 | "Científico de datos (n): Persona que sabe más de estadística que cualquier programador y que a la vez sabe más de programación que cualquier estadístico", Josh Willis. 15 | 16 | ## **Plan diario** 17 | 18 | Si aún no sabes cómo empezar te comparto mi plan diario para seguir preparándome como CD. 19 | 20 | - Dedicarle media hora de lectura de temas relacionados a CD en Medium o sitios similares. 21 | - Lectura de alguno de los libros listados (el tiempo que tu dispongas). 22 | - Codificar al menos una hora al día. Pueden ser ejercicios de algoritmia, probabilidad y estadística o realizar algún proyecto donde apliques modelos de ML. 23 | - Tomar notas que me permite revisarlas después en dado caso de tener alguna duda. Puedes usar Notion o una libreta. 24 | - Practicar, practicar y practicar. 25 | - Bonus: Crear un blog y documentar los avances. 26 | 27 | Lo anterior es solo una idea sin embargo dependerá del tiempo y responsabilidades de cada persona, lo importante es que inicies y tengas constancia en tu aprendizaje. 28 | 29 | ## **Sitios útiles** 30 | 31 | Si requieres practicar y poner a prueba tus habilidades puedes utilizar los siguientes sitios. 32 | 33 | - [Leetcode](https://www.leetcode.com/) 34 | - [Kaggle](https://www.kaggle.com/) 35 | - [Hacker Rank](https://hackerrank.com/) 36 | - [AlgoExpert](https://algoexpert.io/) (código de descuento aiwif-18) 37 | - [Codewars](http://www.codewars.com/) 38 | - [Codility](https://codility.com/programmers/) 39 | - [HackerEarth](https://www.hackerearth.com/) 40 | - [Sphere Online Judge](http://www.spoj.com/) 41 | - [Codechef](https://www.codechef.com/) 42 | - [InterviewCake](https://www.interviewcake.com/) 43 | - [Geeks for Geeks](http://www.geeksforgeeks.org/) 44 | - [InterviewBit](https://www.interviewbit.com/invite/icjf) 45 | 46 | Si tienes sitios que consideras le pueden servir a la comunidad te invito a enviarme un tuit a [@silvercorp](https://www.twitter.com/silvercorp) 47 | 48 | ## **Videos y lecturas generales** 49 | 50 | - [Coursera: Learning How to Learn: Powerful mental tools to help you master tough subjects](https://www.coursera.org/learn/learning-how-to-learn/home/welcome) 51 | - [ABC: Always Be Coding](https://medium.com/always-be-coding/abc-always-be-coding-d5f8051afce2#.4heg8zvm4) 52 | 53 | ## **Agradecimientos** 54 | 55 | Gracias a todos los que colaboran compartiendo este material y en especial a [Latincoder](https://twitter.com/jorge_vgut) y [Lira Tron](https://twitter.com/Lira_tron) por las enseñanzas en temas de algoritmia, y a [Rodo Ferro](https://twitter.com/FerroRodolfo) y [Zally](https://twitter.com/zallyhg) por su retroalimentación. -------------------------------------------------------------------------------- /content/ml-path.md: -------------------------------------------------------------------------------- 1 | # Aprendizaje automático 2 | 3 | ### Conceptos básicos 4 | 5 | - ¿Qué es el aprendizaje automático? 6 | - [Machine Learning Basics | What Is Machine Learning?](https://www.youtube.com/watch?v=ukzFI9rgwfU) 7 | - [What is Machine Learning?](https://www.youtube.com/watch?v=f_uwKZIAeM0) 8 | - [Google: What is Machine Learning?](https://www.youtube.com/watch?v=HcqpanDadyQ&t=2s) 9 | - Aprendizaje supervisado 10 | - [Supervised Learning: Crash Course AI #2](https://www.youtube.com/watch?v=4qVRBYAdLAo) 11 | - [Supervised vs Unsupervised vs Reinforcement Learning](https://www.youtube.com/watch?v=xtOg44r6dsE) 12 | - [Supervised and Unsupervised](https://www.youtube.com/watch?v=kE5QZ8G_78c) 13 | - Aprendizaje no supervisado 14 | - [Unsupervised Learning: Crash Course AI #6](https://www.youtube.com/watch?v=JnnaDNNb380) 15 | - [Unsupervised Learning](https://www.youtube.com/watch?v=8dqdDEyzkFA&t=16s) 16 | - [Supervised vs Unsupervised vs Reinforcement Learning](https://www.youtube.com/watch?v=xtOg44r6dsE) 17 | - [Supervised and Unsupervised](https://www.youtube.com/watch?v=kE5QZ8G_78c) 18 | - [Supervised vs Unsupervised Learning: Key Differences](https://www.guru99.com/supervised-vs-unsupervised-learning.html) 19 | - Aprendizaje reforzado 20 | - [Supervised vs Unsupervised vs Reinforcement Learning](https://www.youtube.com/watch?v=xtOg44r6dsE) 21 | - [Reinforcement Learning Basics](https://www.youtube.com/watch?v=2xATEwcRpy8) 22 | - [An introduction to Reinforcement Learning](https://www.youtube.com/watch?v=JgvyzIkgxF0) 23 | 24 | ### Principales desafíos 25 | 26 | - Types of data 27 | - [Types of Data: Categorical vs Numerical Data](https://www.youtube.com/watch?v=DUcXZ08IdMo) 28 | - [Transforming Categorical to Numerical](https://www.youtube.com/watch?v=du8YTUgOCJ0) 29 | - [Machine Learning #43 Categorical Attributes | Decision Trees](https://www.youtube.com/watch?v=oqrYZ0feGOc) 30 | - Datos faltantes 31 | - [How to Handle Missing Data](https://towardsdatascience.com/how-to-handle-missing-data-8646b18db0d4) 32 | - [The Trouble with Missing Data - Computerphile](https://www.youtube.com/watch?v=oCQbC818KKU) 33 | - [How To Handle Missing Values in Categorical Features](https://www.youtube.com/watch?v=q-DyjA8ZmYM) 34 | - [Working with Missing Data in Machine Learning](https://towardsdatascience.com/working-with-missing-data-in-machine-learning-9c0a430df4ce) 35 | - [Handling Missing Data for a Beginner](https://towardsdatascience.com/handling-missing-data-for-a-beginner-6d6f5ea53436) 36 | - [How do I handle missing values in pandas?](https://www.youtube.com/watch?v=fCMrO_VzeL8) 37 | - Outliers 38 | - [A Brief Overview of Outlier Detection Techniques](https://towardsdatascience.com/a-brief-overview-of-outlier-detection-techniques-1e0b2c19e561) 39 | - [How to Identify Outliers in your Data](https://machinelearningmastery.com/how-to-identify-outliers-in-your-data/) 40 | - [3 methods to deal with outliers](https://www.kdnuggets.com/2017/01/3-methods-deal-outliers.html) 41 | - [A Comprehensive Guide to Data Exploration](https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/) 42 | - [Outlier Detection and Removal using Pandas Python](https://www.youtube.com/watch?v=2Qrost474lQ) 43 | - [Finding an outlier in a dataset using Python](https://www.youtube.com/watch?v=rzR_cKnkD18) 44 | - [Outlier Analysis/Detection with Univariate Methods Using Tukey boxplots in Python (Playlist)](https://www.youtube.com/watch?v=nbNiD76yE8o&list=PL8Bgdi87Y1lWJtBDuStNuEGoXKVFmMrF3) 45 | - Feature selection 46 | - [How do I select features for Machine Learning?](https://www.youtube.com/watch?v=YaKMeAlHgqQ) 47 | - [The 5 Feature Selection Algorithms every Data Scientist should know](https://towardsdatascience.com/the-5-feature-selection-algorithms-every-data-scientist-need-to-know-3a6b566efd2) 48 | - [Why, How and When to apply Feature Selection](https://towardsdatascience.com/why-how-and-when-to-apply-feature-selection-e9c69adfabf2) 49 | - [Feature Selection Techniques Easily Explained | Machine Learning](https://www.youtube.com/watch?v=EqLBAmtKMnQ) 50 | - [Feature Selection - Georgia Tech - Machine Learning](https://www.youtube.com/watch?v=8CpRLplmdqE) 51 | - [Feature Selection: Beyond feature importance?](https://medium.com/fiverr-engineering/feature-selection-beyond-feature-importance-9b97e5a842f) 52 | - [Learning Feature Selection for Building and Improving your Machine Learning Model](https://medium.com/analytics-vidhya/feature-selection-for-building-and-improving-your-machine-learning-model-e3a81b79487) 53 | - [Feature Selection Techniques in Machine Learning with Python](https://towardsdatascience.com/feature-selection-techniques-in-machine-learning-with-python-f24e7da3f36e) 54 | - Sobreajuste (overfitting) y Subajuste (underfitting) 55 | - [Overfitting vs. Underfitting: A Conceptual Explanation](https://towardsdatascience.com/overfitting-vs-underfitting-a-conceptual-explanation-d94ee20ca7f9) 56 | - [Memorizing is not learning! — 6 tricks to prevent overfitting in machine learning.](https://medium.com/hackernoon/memorizing-is-not-learning-6-tricks-to-prevent-overfitting-in-machine-learning-820b091dc42) 57 | - [What is underfitting and overfitting in machine learning and how to deal with it.](https://medium.com/greyatom/what-is-underfitting-and-overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76) 58 | - [The Problem Of Overfitting And How To Resolve It](https://medium.com/fintechexplained/the-problem-of-overfitting-and-how-to-resolve-it-1eb9456b1dfd) 59 | - [Overfitting](https://twitter.com/home) 60 | - [Underfitting and Overfitting in machine learning and how to deal with it](https://towardsdatascience.com/underfitting-and-overfitting-in-machine-learning-and-how-to-deal-with-it-6fe4a8a49dbf) 61 | - [But What Is Overfitting in Machine Learning?](https://www.youtube.com/watch?v=Anq4PgdASsc) 62 | - [Overfitting vs. Underfitting: A Complete Example](https://towardsdatascience.com/overfitting-vs-underfitting-a-complete-example-d05dd7e19765) 63 | - Conjunto de entrenamiento y prueba 64 | - [Training and testing](https://www.youtube.com/watch?v=P2NqrFp8usY) 65 | - [How (dis)similar are my train and test data?](https://towardsdatascience.com/how-dis-similar-are-my-train-and-test-data-56af3923de9b) 66 | - [Machine Learning Fundamentals: Cross Validation](https://www.youtube.com/watch?v=fSytzGwwBVw) 67 | - [K-Fold Cross Validation - Intro to Machine Learning](https://www.youtube.com/watch?v=TIgfjmp-4BA) 68 | - [How to split your dataset to train and test datasets using SciKit Learn](https://medium.com/@contactsunny/how-to-split-your-dataset-to-train-and-test-datasets-using-scikit-learn-e7cf6eb5e0d) 69 | - [Train/Test Split and Cross Validation in Python](https://towardsdatascience.com/train-test-split-and-cross-validation-in-python-80b61beca4b6) 70 | - [Train and Test Set in Python Machine Learning — How to Split](https://medium.com/@rinu.gour123/train-and-test-set-in-python-machine-learning-how-to-split-58029a0d657b) 71 | - [Machine Learning with Python | Part 4 | Creating Train & Test Dataset & evaluating model with MSE](https://www.youtube.com/watch?v=6GtdvA4Q-zw) 72 | - Reducción de dimensiones 73 | - [What Is Dimension Reduction In Data Science?](https://medium.com/fintechexplained/what-is-dimension-reduction-in-data-science-2aa5547f4d29) 74 | - [Machine Learning - Dimensionality Reduction - Feature Extraction & Selection](https://www.youtube.com/watch?v=AU_hBML2H1c) 75 | - [Dimension Reduction Techniques with Python](https://towardsdatascience.com/dimension-reduction-techniques-with-python-f36ca7009e5c) 76 | - [A beginner’s guide to dimensionality reduction in Machine Learning](https://towardsdatascience.com/dimensionality-reduction-for-machine-learning-80a46c2ebb7e) 77 | - [Dimensionality Reduction](https://www.youtube.com/watch?v=3uxOyk-SczU) 78 | - [Understanding PCA (Principal Component Analysis) with Python](https://towardsdatascience.com/dive-into-pca-principal-component-analysis-with-python-43ded13ead21) 79 | - [Dimensionality Reduction - The Math of Intelligence #5](https://www.youtube.com/watch?v=jPmV3j1dAv4) 80 | - [StatQuest: Principal Component Analysis (PCA), Step-by-Step](https://www.youtube.com/watch?v=FgakZw6K1QQ) 81 | - [Machine Learning — Visualization, Dimension Reduction & Anomaly detection](https://medium.com/@jonathan_hui/machine-learning-visualization-dimension-reduction-anomaly-detection-e5762b9093fd) 82 | 83 | ### Regresión 84 | 85 | - Regresión lineal 86 | - [Introduction to Machine Learning Algorithms: Linear Regression](https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression-14c4e325882a) 87 | - [Statistics 101: Linear Regression, The Very Basics](https://www.youtube.com/watch?v=ZkjP5RJLQF4) 88 | - [Video 1: Introduction to Simple Linear Regression](https://www.youtube.com/watch?v=owI7zxCqNY0) 89 | - [Linear Regression In Real Life](https://towardsdatascience.com/linear-regression-in-real-life-4a78d7159f16) 90 | - [What is Regression? | SSE, SSR, SST | R-squared | Errors (ε vs. e)](https://www.youtube.com/watch?v=aq8VU5KLmkY) 91 | - [StatQuest: Linear Models Pt.1 - Linear Regression](https://www.youtube.com/watch?v=nk2CQITm_eo) 92 | - [A beginner’s guide to Linear Regression in Python with Scikit-Learn](https://towardsdatascience.com/a-beginners-guide-to-linear-regression-in-python-with-scikit-learn-83a8f7ae2b4f) 93 | - [Simple and Multiple Linear Regression in Python](https://towardsdatascience.com/simple-and-multiple-linear-regression-in-python-c928425168f9) 94 | - [Linear Regression using Gradient Descent](https://towardsdatascience.com/linear-regression-using-gradient-descent-97a6c8700931) 95 | - [Linear Regression using Python](https://towardsdatascience.com/linear-regression-using-python-b136c91bf0a2) 96 | - [8 ways to perform simple linear regression and measure their speed using Python](https://medium.com/free-code-camp/data-science-with-python-8-ways-to-do-linear-regression-and-measure-their-speed-b5577d75f8b) 97 | - Logística 98 | - [Introduction to Logistic Regression](https://towardsdatascience.com/introduction-to-logistic-regression-66248243c148) 99 | - [StatQuest: Logistic Regression](https://www.youtube.com/watch?v=yIYKR4sgzI8) 100 | - [Building A Logistic Regression in Python, Step by Step](https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8) 101 | - [Logistic Regression. Simplified.](https://medium.com/data-science-group-iitr/logistic-regression-simplified-9b4efe801389) 102 | - [Logistic Regression — Detailed Overview](https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc) 103 | - [5 Reasons “Logistic Regression” should be the first thing you learn when becoming a Data Scientist](https://towardsdatascience.com/5-reasons-logistic-regression-should-be-the-first-thing-you-learn-when-become-a-data-scientist-fcaae46605c4) 104 | - [Understanding Logistic Regression](https://towardsdatascience.com/understanding-logistic-regression-9b02c2aec102) 105 | - [Building a Logistic Regression in Python](https://towardsdatascience.com/building-a-logistic-regression-in-python-301d27367c24) 106 | - [Logistic Regression Details Pt1: Coefficients (Playlist)](https://www.youtube.com/watch?v=vN5cNN2-HWE) 107 | - [Logistic Regression in Python | Logistic Regression Example](https://www.youtube.com/watch?v=VCJdg7YBbAQ) 108 | 109 | ### Árboles de decisión 110 | 111 | - Introduction 112 | - [Decision Tree Classifier — Theory](https://medium.com/machine-learning-101/chapter-3-decision-trees-theory-e7398adac567) 113 | - [StatQuest: Decision Trees](https://www.youtube.com/watch?v=7VeUPuFGJHk) 114 | - [Lecture: Machine learning - Decision trees](https://www.youtube.com/watch?v=-dCtJjlEEgM) 115 | - [Decision Tree Algorithm - Edureka](https://www.youtube.com/watch?v=qDcl-FRnwSU) 116 | - [Decision Trees in Machine Learning](https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052) 117 | - [Decision Trees Algorithms](https://medium.com/deep-math-machine-learning-ai/chapter-4-decision-trees-algorithms-b93975f7a1f1) 118 | - [Decision Trees — An Intuitive Introduction](https://medium.com/x8-the-ai-community/decision-trees-an-intuitive-introduction-86c2b39c1a6c) 119 | - [Let’s Write a Decision Tree Classifier from Scratch](https://www.youtube.com/watch?v=LDRbO9a6XPU) 120 | - Tipos de árboles 121 | - ID3 122 | - [ID3 Algorithm (concept and numerical)](https://www.youtube.com/watch?v=UdTKxGQvYdc) 123 | - [ID3 - Georgia Tech - Machine Learning](https://www.youtube.com/watch?v=IX0iGf2wYM0) 124 | - [An Introduction to Decision Tree Learning: ID3 Algorithm](https://medium.com/machine-learning-guy/an-introduction-to-decision-tree-learning-id3-algorithm-54c74eb2ad55) 125 | - [Decision Trees: ID3 Algorithm Explained](https://towardsdatascience.com/decision-trees-for-classification-id3-algorithm-explained-89df76e72df1) 126 | - [Decision tree Learning example: ID3](https://www.youtube.com/watch?v=UzpwBb3qAbs) 127 | - C4.5 128 | - [C4.5 algorithm](https://en.wikipedia.org/wiki/C4.5_algorithm) 129 | - [What is the C4.5 algorithm and how does it work?](https://towardsdatascience.com/what-is-the-c4-5-algorithm-and-how-does-it-work-2b971a9e7db0) 130 | - [Classification And Regression Trees for Machine Learning](https://machinelearningmastery.com/classification-and-regression-trees-for-machine-learning/) 131 | - [A Step By Step C4.5 Decision Tree Example](https://sefiks.com/2018/05/13/a-step-by-step-c4-5-decision-tree-example/) 132 | - CART 133 | - [Decision Tree (CART) - Machine Learning Fun and Easy](https://www.youtube.com/watch?v=DCZ3tsQIoGU) 134 | - [Introduction to Classification & Regression Trees (CART)](https://www.datasciencecentral.com/profiles/blogs/introduction-to-classification-regression-trees-cart) 135 | - Random forest 136 | - [StatQuest: Random Forests Part 1 - Building, Using and Evaluating](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ) 137 | - [Random Forest Algorithm - Random Forest Explained](https://www.youtube.com/watch?v=eM4uJ6XGnSM) 138 | - [Machine learning - Random forests](https://www.youtube.com/watch?v=3kYujfDgmNk) 139 | - [How to Visualize a Decision Tree from a Random Forest in Python using Scikit-Learn](https://towardsdatascience.com/how-to-visualize-a-decision-tree-from-a-random-forest-in-python-using-scikit-learn-38ad2d75f21c) 140 | - Ensembles 141 | - [Ensemble Learning in Machine Learning | Getting Started](https://towardsdatascience.com/ensemble-learning-in-machine-learning-getting-started-4ed85eb38e00) 142 | - [Basic Ensemble Learning (Random Forest, AdaBoost, Gradient Boosting)- Step by Step Explained](https://towardsdatascience.com/basic-ensemble-learning-random-forest-adaboost-gradient-boosting-step-by-step-explained-95d49d1e2725) 143 | - [Decision Tree Ensembles- Bagging and Boosting](https://towardsdatascience.com/decision-tree-ensembles-bagging-and-boosting-266a8ba60fd9) 144 | - [Ensemble methods: bagging, boosting and stacking](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) 145 | - [Ensemble learners](https://www.youtube.com/watch?v=Un9zObFjBH0) 146 | - [Ensemble Learning, Bootstrap Aggregating (Bagging) and Boosting](https://www.youtube.com/watch?v=m-S9Hojj1as) 147 | - [Ensemble Methods in Machine Learning: What are They and Why Use Them?](https://towardsdatascience.com/ensemble-methods-in-machine-learning-what-are-they-and-why-use-them-68ec3f9fef5f) 148 | - [Ensemble Learning to Improve Machine Learning Results](https://blog.statsbot.co/ensemble-learning-d1dcd548e936) 149 | - Pruning (podando el árbol) 150 | - [Decision Tree Pruning](https://www.youtube.com/watch?v=t56Nid85Thg) 151 | - [Machine Learning: Pruning Decision Trees](https://www.displayr.com/machine-learning-pruning-decision-trees/) 152 | - [Decision Trees — Pruning](https://towardsdatascience.com/decision-trees-pruning-4241cc266fef) 153 | 154 | ### Redes Bayesianas 155 | 156 | - [Bayes Theorem: A Primer](https://lavanya.ai/2019/05/16/bayes-theorem/) 157 | - [A Gentle Introduction to Bayesian Belief Networks](https://machinelearningmastery.com/introduction-to-bayesian-belief-networks/) 158 | - [Bayesian networks](https://ermongroup.github.io/cs228-notes/representation/directed/) 159 | - [Bayesian Networks - Video](https://www.youtube.com/watch?v=TuGDMj43ehw) 160 | - [Introduction to Bayesian Networks - Edureka](https://www.youtube.com/watch?v=SkC8S3wuIfg) 161 | - [Bayesian networks - an introduction](https://www.bayesserver.com/docs/introduction/bayesian-networks) 162 | - [How To Implement Bayesian Networks In Python? – Bayesian Networks Explained With Examples](https://www.edureka.co/blog/bayesian-networks/) 163 | - [Bayesian Networks](https://pomegranate.readthedocs.io/en/latest/BayesianNetwork.html) 164 | - [Introduction to Bayesian Belief Networks](https://towardsdatascience.com/introduction-to-bayesian-belief-networks-c012e3f59f1b) 165 | - [Basics of Bayesian Network](https://towardsdatascience.com/basics-of-bayesian-network-79435e11ae7b) 166 | - Naive bayes 167 | - [Naïve Bayes Classifier - Fun and Easy Machine Learning](https://www.youtube.com/watch?v=CPqOCI0ahss) 168 | - [Naive Bayes, Clearly Explained!!!](https://www.youtube.com/watch?v=O2L2Uv9pdDA) 169 | - [Naive Bayes Classifier - With examples](https://www.youtube.com/watch?v=l3dZ6ZNFjo0) 170 | - [Naive Bayes Classifier](https://towardsdatascience.com/naive-bayes-classifier-81d512f50a7c) 171 | - [Introduction to Naive Bayes Classification](https://towardsdatascience.com/introduction-to-naive-bayes-classification-4cffabb1ae54) 172 | - [All about Naive Bayes](https://towardsdatascience.com/all-about-naive-bayes-8e13cef044cf) 173 | 174 | ### Clustering 175 | 176 | - Introduction 177 | - [Clustering - Google](https://developers.google.com/machine-learning/clustering) 178 | - [How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms](https://medium.com/free-code-camp/how-machines-make-sense-of-big-data-an-introduction-to-clustering-algorithms-4bd97d4fbaba) 179 | - [Introduction to Clustering](https://www.youtube.com/watch?v=4cxVDUybHrI) 180 | - [Clustering in Machine Learning](https://www.geeksforgeeks.org/clustering-in-machine-learning/) 181 | - [An Introduction to Clustering and different methods of clustering](https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/) 182 | - [Unsupervised Learning | Clustering and Association Algorithms in Machine Learning](https://www.youtube.com/watch?v=UhVn2WrzMnI) 183 | - [4 Basic Types of Cluster Analysis used in Data Analytics](https://www.youtube.com/watch?v=Se28XHI2_xE) 184 | - [An Introduction to Clustering Algorithms in Python](https://towardsdatascience.com/an-introduction-to-clustering-algorithms-in-python-123438574097) 185 | - [Three Popular Clustering Methods and When to Use Each](https://medium.com/predict/three-popular-clustering-methods-and-when-to-use-each-4227c80ba2b6) 186 | - [The 5 Clustering Algorithms Data Scientists Need to Know](https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68) 187 | - [Lecture: Clustering - MIT](https://www.youtube.com/watch?v=esmzYhuFnds) 188 | - Métodos 189 | - [Elbow Method in Supervised Machine Learning(Optimal K Value)](https://medium.com/@moussadoumbia_90919/elbow-method-in-supervised-learning-optimal-k-value-99d425f229e7) 190 | - [Clustering | Choosing The Number Of Clusters](https://www.youtube.com/watch?v=lbR5br5yvrY) 191 | - [K-means: Elbow Method and Silhouette](https://medium.com/@jonathanrmzg/k-means-elbow-method-and-silhouette-e565d7ab87aa) 192 | - [Machine Learning Tutorial Python - 13: K Means Clustering](https://www.youtube.com/watch?v=EItlUEPCIzM) 193 | - K-Means 194 | - [Understanding K-means Clustering in Machine Learning](https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1) 195 | - [K Means Clustering Algorithm](https://www.youtube.com/watch?v=1XqG0kaJVHY) 196 | - [K-Mean Clustering](https://www.youtube.com/watch?v=wt-X61BnUCA) 197 | - [K Means Clustering Algorithm - Simplilearn](https://www.youtube.com/watch?v=Xvwt7y2jf5E) 198 | - [StatQuest: K-means clustering](https://www.youtube.com/watch?v=4b5d3muPQmA) 199 | - Clustering Jerárquico 200 | - [StatQuest: Hierarchical Clustering](https://www.youtube.com/watch?v=7xHsRkOdVwo) 201 | - [Hierarchical Clustering - Fun and Easy Machine Learning](https://www.youtube.com/watch?v=EUQY3hL38cw) 202 | - [Hierarchical Clustering | Simplilearn](https://www.youtube.com/watch?v=9U4h6pZw6f8) 203 | - [Hierarchical Clustering - Dendrograms Using Scipy and Scikit-learn in Python](https://www.youtube.com/watch?v=JcfIeaGzF8A) 204 | 205 | ### Máquinas de soporte vectorial (SVM) 206 | 207 | - [Support Vector Machines, Clearly Explained!!!](https://www.youtube.com/watch?v=efR1C6CvhmE) 208 | - [Support Vector Machine: Complete Theory](https://towardsdatascience.com/understanding-support-vector-machine-part-1-lagrange-multipliers-5c24a52ffc5e) 209 | - [An Introduction to Support Vector Machines (SVM)](https://monkeylearn.com/blog/introduction-to-support-vector-machines-svm/) 210 | - [Support Vector Machines](https://scikit-learn.org/stable/modules/svm.html) 211 | - [Support Vector Machines(SVM) — An Overview](https://towardsdatascience.com/https-medium-com-pupalerushikesh-svm-f4b42800e989) 212 | - [Support Vector Machine (SVM) - Fun and Easy Machine Learning](https://www.youtube.com/watch?v=Y6RRHw9uN9o) 213 | - [Support Vector Machine — Simply Explained](https://towardsdatascience.com/support-vector-machine-simply-explained-fee28eba5496) 214 | - [Support Vector Machine — Introduction to Machine Learning Algorithms](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47) 215 | - [How SVM (Support Vector Machine) algorithm works](https://www.youtube.com/watch?v=1NxnPkZM9bc) 216 | - [Support Vector Machine (SVM) Tutorial](https://blog.statsbot.co/support-vector-machines-tutorial-c1618e635e93) 217 | - [Understanding Support Vector Machine(SVM) algorithm from examples (along with code)](https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/) 218 | - [Support Vector Machine - How Support Vector Machine Works](https://www.youtube.com/watch?v=TtKF996oEl8) 219 | - [Lecture: Support Vector Machines - MIT](https://www.youtube.com/watch?v=_PwhiWxHK8o&t=1s) 220 | - [Support Vector Machines: A Visual Explanation with Sample Python Code](https://www.youtube.com/watch?v=N1vOgolbjSc) 221 | - [Chapter 2 : SVM (Support Vector Machine) — Theory](https://medium.com/machine-learning-101/chapter-2-svm-support-vector-machine-theory-f0812effc72) 222 | 223 | ### Sistemas de recomendación 224 | 225 | - [Introduction to recommender systems](https://towardsdatascience.com/introduction-to-recommender-systems-6c66cf15ada) 226 | - [Recommendation Systems by Google](https://developers.google.com/machine-learning/recommendation) 227 | - [Understanding Content Based Recommender Systems](https://medium.com/analytics-vidhya/understanding-recommender-systems-introduction-3be54e937625) 228 | - [An Introductory Recommender Systems Tutorial](https://medium.com/ai-society/a-concise-recommender-systems-tutorial-fa40d5a9c0fa) 229 | - [Recommender System for Beginners](https://www.youtube.com/watch?v=U-yq3I9QugQ) 230 | - [Introduction to Recommender Systems](https://tryolabs.com/blog/introduction-to-recommender-systems/) 231 | - [Recommendation Systems using Machine Learning](https://www.youtube.com/watch?v=EjOlN6uVBOg) 232 | - [Machine Learning for Recommender systems — Part 1 (algorithms, evaluation and cold start)](https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-1-algorithms-evaluation-and-cold-start-6f696683d0ed) 233 | - [Recommender Systems in Practice](https://towardsdatascience.com/recommender-systems-in-practice-cef9033bb23a) 234 | - [Recommendation systems: Principles, methods and evaluation - So technical](https://www.sciencedirect.com/science/article/pii/S1110866515000341) 235 | - [Building a Movie Recommendation Engine | Machine Learning Projects](https://www.youtube.com/watch?v=XoTwndOgXBM) 236 | - [Recommender Systems | Problem Formulation — [ Machine Learning | Andrew Ng ]](https://www.youtube.com/watch?v=giIXNoiqO_U) 237 | - [How does Netflix recommend movies? Matrix Factorization](https://www.youtube.com/watch?v=ZspR5PZemcs) 238 | - [Movie Recommendation System with Collaborative Filtering](https://www.youtube.com/watch?v=3ecNC-So0r4) 239 | 240 | ### Recursos adicionales 241 | 242 | - Data para su libre uso 243 | - [UCI Datasets](https://archive.ics.uci.edu/ml/index.php) 244 | - [Kaggle](https://www.kaggle.com/) 245 | - [Datos abiertos del gobierno de EUA.](http://data.gov) 246 | - [Datos abiertos de la Unión Europea](http://open-data.europa.eu/en/data/) 247 | - [Listado de 33 recursos de datos abiertos](https://www.forbes.com/sites/bernardmarr/2016/02/12/big-data-35-brilliant-and-free-data-sources-for-2016/#52df8c92b54d) 248 | - Sitios interesantes 249 | - [Analytics Vidhya](https://datahack.analyticsvidhya.com/contest/all/) 250 | - [Data Driven](https://www.drivendata.org/) 251 | - C[rowd AI](https://www.crowdai.org/) 252 | - [Coda Lab](https://competitions.codalab.org/) -------------------------------------------------------------------------------- /content/sql-path.md: -------------------------------------------------------------------------------- 1 | # SQL 2 | 3 | ### Select 4 | 5 | - One and multiple columns 6 | - DISTINCT 7 | - COUNT 8 | - Aliases 9 | - SUM, AVG, ROUND 10 | 11 | ### Where 12 | 13 | - Wildcard characters 14 | - Between 15 | - IN 16 | - NOT IN 17 | - LIKE - NOT LIKE 18 | - SIMILAR 19 | - NULL - IS NOT NULL 20 | - ALL 21 | - SOME 22 | - ANY 23 | - EXISTS 24 | - UNIQUE 25 | - OVERLAPS 26 | - MATCH 27 | - Having 28 | - Limit 29 | 30 | ### Cadenas 31 | 32 | - Concatenación 33 | - Substrings 34 | - Expresiones regulares 35 | - Upper, lower 36 | 37 | ### Números y fechas 38 | 39 | - Extraer fechas 40 | - Funciones Day, month, year 41 | 42 | ### Operadores 43 | 44 | - Lógicos 45 | - AND 46 | - OR 47 | - NOT 48 | - Relacionales 49 | - Union 50 | - Intersect 51 | - Except 52 | - JOINS 53 | - Básico 54 | - Cross 55 | - Natural 56 | - Condition 57 | - LEFT 58 | - Inner 59 | - Outer 60 | - Union 61 | 62 | ### Expresiones avanzadas 63 | 64 | - CASE 65 | - COALESCE 66 | - CAST 67 | - GROUP BY 68 | - HAVING 69 | - ORDER BY 70 | 71 | ### Subqueries 72 | 73 | - ¿Qué es una subquery? 74 | - Ejemplos 75 | 76 | ### Recursividad 77 | 78 | - ¿Qué es una query recursiva? 79 | - Ejemplos 80 | 81 | ### Contenido 82 | 83 | - [SQL Tutorial: How To Write Better Queries](https://towardsdatascience.com/sql-tutorial-how-to-write-better-queries-108ae91d5f4e) 84 | - [MySQL Tutorial for Beginners [Full Course]](https://www.youtube.com/watch?v=7S_tz1z_5bA) 85 | - [SQL Tutorial](https://www.dofactory.com/sql/tutorial) 86 | - [SQL Basics for Beginners | Learn SQL | SQL Tutorial for Beginners | Edureka](https://www.youtube.com/watch?v=zbMHLJ0dY4w) 87 | - [SQL SELECT Statement](https://www.w3schools.com/sql/sql_select.asp) 88 | - [SQL SELECT Tutorial](https://www.youtube.com/watch?v=YufocuHbYZo) - Part 1 89 | - [SQL SELECT Tutorial - Part 2](https://www.youtube.com/watch?v=PkJKzR_sClM) 90 | - [SQL Tutorial - Full Database Course for Beginners](https://www.youtube.com/watch?v=HXV3zeQKqGY) 91 | - [What is SQL? [in 4 minutes for beginners] - Intro](https://www.youtube.com/watch?v=27axs9dO7AE) 92 | - [Learn Basic SQL in 10 Minutes](https://www.youtube.com/watch?v=bEtnYWuo2Bw) 93 | - [SQL COUNT(), AVG() and SUM() Functions](https://www.w3schools.com/sql/sql_count_avg_sum.asp#:~:text=The%20SQL%20COUNT()%2C%20AVG,sum%20of%20a%20numeric%20column.) 94 | - [SQL SELECT COUNT, SUM, AVG](https://www.dofactory.com/sql/select-count-sum-avg) 95 | - [SQL Tutorial for Beginners: Learn SQL in 7 Days](https://www.guru99.com/sql.html) 96 | 97 | ### Sitios para practicar 98 | 99 | - [SQL Tutorial - SQL Zoo](https://sqlzoo.net/) 100 | - [SQL: Hacker Rank](https://www.hackerrank.com/domains/sql) 101 | - [w3schools](https://www.w3schools.com/sql/trysql.asp?filename=trysql_op_in) -------------------------------------------------------------------------------- /content/stats-path.md: -------------------------------------------------------------------------------- 1 | # Probabilidad y estadística 2 | 3 | ### Estadística descriptiva 4 | 5 | La estadística descriptiva es la rama de la estadística que recolecta, analiza y caracteriza un conjunto de datos con el objetivo de presentar los comportamientos y singularidades del mismo. 6 | 7 | Nota: Si prefieres revisar los temas siguientes en un solo recurso puedes descargar un libro gratuito en este [enlace](https://link.springer.com/book/10.1007%2F978-3-319-46162-5). 8 | 9 | - ¿Qué es la estadística descriptiva? 10 | - [Introducción](https://conjointly.com/kb/descriptive-statistics/) 11 | - [Introduction to Statistics](https://www.youtube.com/watch?v=MXaJ7sa7q-8) 12 | - [Understanding Descriptive Statistics](https://towardsdatascience.com/understanding-descriptive-statistics-c9c2b0641291) 13 | - [Descriptive Statistics (Playlist)](https://www.youtube.com/watch?v=h8EYEJ32oQ8&list=PLU5aQXLWR3_yYS0ZYRA-5g5YSSYLNZ6Mc) 14 | - [Descriptive Statistics (Playlist - 13 videos)](https://www.youtube.com/watch?v=bfQLNyiDPsk&list=PLTNMv857s9WVStKLco6ZBOsfSGXzJ1L0f) 15 | - Variables estadísticas 16 | - [Cualitativa y cuantitativa.](https://www.youtube.com/watch?v=5rUVYWfZOb8) 17 | - Medida de tendencia central. 18 | - Media 19 | - [Finding mean, median and mode](https://www.youtube.com/watch?v=k3aKKasOmIw&list=PLU5aQXLWR3_yYS0ZYRA-5g5YSSYLNZ6Mc&index=2) 20 | - [The mean](https://www.youtube.com/watch?v=bfQLNyiDPsk&list=PLTNMv857s9WVStKLco6ZBOsfSGXzJ1L0f&index=1) 21 | - Mediana 22 | - [Finding mean, median and mode](https://www.youtube.com/watch?v=k3aKKasOmIw&list=PLU5aQXLWR3_yYS0ZYRA-5g5YSSYLNZ6Mc&index=2) 23 | - [The median](https://www.youtube.com/watch?v=rvBqEEGtJY4&list=PLTNMv857s9WVStKLco6ZBOsfSGXzJ1L0f&index=3) 24 | - Moda 25 | - [Finding mean, median and mode](https://www.youtube.com/watch?v=k3aKKasOmIw&list=PLU5aQXLWR3_yYS0ZYRA-5g5YSSYLNZ6Mc&index=2) 26 | - [The mode](https://www.youtube.com/watch?v=HUUyX80PUuI&list=PLTNMv857s9WVStKLco6ZBOsfSGXzJ1L0f&index=4) 27 | - Medida de dispersión. 28 | - [Rango, varianza y desviación estándar](https://www.youtube.com/watch?v=E4HAYd0QnRc) 29 | - [Z-Scores, Standardization, and the Standard Normal Distribution (5.3)](https://www.youtube.com/watch?v=2tuBREK_mgE) 30 | - [Rango intercuartílico](https://www.youtube.com/watch?v=qcTcsEdMgBo) 31 | - [Covarianza](https://www.youtube.com/watch?v=KDw3hC2YNFc) 32 | - Coeficiente de variación de Pearson 33 | - [Pearson's Correlation](https://www.youtube.com/watch?v=2B_UW-RweSE) 34 | - [Pearson Coefficient of Correlation Explained.](https://towardsdatascience.com/pearson-coefficient-of-correlation-explained-369991d93404) 35 | - [How to Calculate and Interpret a Correlation (Pearson's r)](https://www.youtube.com/watch?v=lVOzlHx_15s) 36 | - Coeficiente de correlación de Spearman 37 | - [Spearman correlation](https://www.youtube.com/watch?v=YpG2MlulP_o) 38 | - [How To... Calculate Spearman's Rank Correlation Coefficient (By Hand)](https://www.youtube.com/watch?v=DE58QuNKA-c) 39 | - [Correlation: Pearson vs. Spearman](https://www.youtube.com/watch?v=YjTod0R7cNE) 40 | - Medidas de posición no central 41 | - Cuartiles y percentiles 42 | - [What are Quartiles? Percentiles? Deciles?](https://www.youtube.com/watch?v=Wy7w_5N7vLI) 43 | - [Quartiles, Deciles, & Percentiles With Cumulative Relative Frequency - Data & Statistics](https://www.youtube.com/watch?v=40o82o3uNfk) 44 | - Asimetría y curtosis. 45 | - Asimetría 46 | - Negativa, Simétrica y Asimetría positiva 47 | - [Symmetry and Skewness](https://www.youtube.com/watch?v=pfujiA5Mk_U) 48 | - [Skewness - Right, Left & Symmetric Distribution - Mean, Median, & Mode With Boxplots - Statistics](https://www.youtube.com/watch?v=U0NZu6f5TMI) 49 | - Curtosis 50 | - [What is Kurtosis?](https://www.youtube.com/watch?v=TM033GCU-SY&list=PLTNMv857s9WVStKLco6ZBOsfSGXzJ1L0f&index=10) 51 | - [Kurtosis](https://www.youtube.com/watch?v=Pf7awGwzy4k) 52 | - Frecuencias 53 | - Absoluta 54 | - [Definition](https://www.statisticshowto.com/absolute-frequency-definition-examples/) 55 | - [How To Make a Relative Frequency Distribution Table](https://www.youtube.com/watch?v=gq3FPpm2yvA) 56 | - Absoluta acumulada 57 | - [Cumulative Frequency Distribution Definition](https://www.statisticshowto.com/cumulative-frequency-distribution/) 58 | - Relativa 59 | - [How to make a relative frequency distribution](https://www.youtube.com/watch?v=7jUIt39tUBM) 60 | - Relativa acumulada 61 | - [How To Make a Cumulative Relative Frequency Table](https://www.youtube.com/watch?v=6hJGa4Zp62M) 62 | - Gráficos 63 | - Introducción 64 | - [Understanding Statistical Graphs and when to use them](https://www.youtube.com/watch?v=rllw15xkmUU) 65 | - [Introduction](Bar Charts, Pie Charts, Histograms, Stemplots, Timeplots (1.2)) 66 | - Lineal 67 | - [Line graphs](https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/line-lineaire/5214824-eng.htm#:~:text=A%20line%20graph%20is%20a,see%20the%20Organizing%20data%20chapter.) 68 | - [Line graphs with examples](https://www.onlinemathlearning.com/line-graphs.html) 69 | - Diagrama de barras 70 | - [What are Bar graphs?](https://www.youtube.com/watch?v=FYFYrjMLG7E) 71 | - [Bar graphs](https://www.smartdraw.com/bar-graph/) 72 | - [Reading bar graph examples](https://www.youtube.com/watch?v=OmLl6pkvV-I) 73 | - Diagrama circular (de pastel) 74 | - [What are pie charts?](https://www.youtube.com/watch?v=8-GZRGwcgVs) 75 | - [Pie chart](https://www.mathsisfun.com/data/pie-charts.html) 76 | - [Art of Problem Solving: Bar Charts and Pie Charts](https://www.youtube.com/watch?v=foyPpC3XjhE) 77 | - Polígono de frecuencias 78 | - [How To Make a Frequency Polygon](https://www.youtube.com/watch?v=y7Wb9AjbRjo) 79 | - [What is a Frequency Polygon?](https://www.youtube.com/watch?v=k1ZFZ3hTftg) 80 | - [How To Draw A Frequency Polygon](https://www.youtube.com/watch?v=PZVJ_NUxEUc) 81 | - Histograma 82 | - [What is a Histogram?](https://www.youtube.com/watch?v=YLPDPglvePY) 83 | - [How To Make a Histogram Using a Frequency Distribution Table](https://www.youtube.com/watch?v=AndS0RLdxtk) 84 | - [What is a Histogram in Statistics?](https://www.youtube.com/watch?v=0Ul8SOlOu8c) 85 | - Pareto 86 | - [What is a Pareto Chart? Definition and Examples](https://tulip.co/blog/manufacturing/what-is-a-pareto-chart-definition-and-examples/) 87 | - [Pareto chart](https://www.youtube.com/watch?v=Tsvru8DPxBE) 88 | - [Statistic:Scatter Plot, Dot Plot and Pareto Chart](https://www.youtube.com/watch?v=feXc3KHJCnA) 89 | - Diagrama de caja (BoxPlot) 90 | - [How To Make Box and Whisker Plots](https://www.youtube.com/watch?v=mhaGAaL6Abw) 91 | - [Boxplot with outliers](https://www.youtube.com/watch?v=1HjLY1tc508) 92 | - [Understanding & Comparing Boxplots (Box and Whisker Plots)](https://www.youtube.com/watch?v=Hm6Mra5XJSs) 93 | 94 | ### Estadística inferencial 95 | 96 | Es una rama de la estadística que se encarga de deducir propiedades, generar conclusiones a partir de una muestra del conjunto de datos. 97 | 98 | - ¿Qué es la estadística inferencial? 99 | - [What is Inferential Statistics?](https://www.statisticshowto.com/inferential-statistics/) 100 | - [Inferential Statistics](https://medium.com/datadriveninvestor/inferential-statistics-ce8f8f21b3ab) 101 | - [Understanding Statistical Inference](https://www.youtube.com/watch?v=tFRXsngz4UQ) 102 | - Estimaciones puntuales y de intervalo 103 | - [Introduction to Statistics for Data Science](https://medium.com/diogo-menezes-borges/introduction-to-statistics-for-data-science-16a188a400ca) 104 | - [Estimation and Confidence Intervals](https://www.youtube.com/watch?v=_BiVb6neUP0) 105 | - [Confidence Intervals: Crash Course Statistics](https://www.youtube.com/watch?v=yDEvXB6ApWc) 106 | - [Confidence Intervals - Introduction](https://www.youtube.com/watch?v=MbXThbTSrVI) 107 | - Pruebas de hipótesis 108 | - Introducción 109 | - [Intro to Hypothesis Testing in Statistics](https://www.youtube.com/watch?v=VK-rnA3-41c) 110 | - [Hypothesis testing](https://www.youtube.com/watch?v=8JIe_cz6qGA) 111 | - [Hypothesis Testing - Examples](https://www.youtube.com/watch?v=0XXT3bIY_pw) 112 | - Usando p-values 113 | - [Understanding the p-value - Statistics Help](https://www.youtube.com/watch?v=eyknGvncKLw) 114 | - [StatQuest: P Values, clearly explained](https://www.youtube.com/watch?v=5Z9OIYA8He8) 115 | - [P-value Explained Simply for Data Scientists](https://towardsdatascience.com/p-value-explained-simply-for-data-scientists-4c0cd7044f14) 116 | - [Explaining p-values with puppies](https://medium.com/hackernoon/explaining-p-values-with-puppies-af63d68005d0) 117 | - [P Values, z Scores, Alpha, Critical Values](https://www.youtube.com/watch?v=zTABmVSAtT0) 118 | - [The problem with p-values](https://towardsdatascience.com/a-case-study-of-the-p-value-f0d708861334) 119 | - z-test, t-test, chi-square, f-test 120 | - [Learn about the t-test, the chi square test, the p value and more](https://www.youtube.com/watch?v=I10q6fjPxJ0&t=320s) 121 | - [Z-statistics vs. T-statistics](https://www.youtube.com/watch?v=5ABpqVSx33I) 122 | - [Test Statistics: Crash Course Statistics](https://www.youtube.com/watch?v=QZ7kgmhdIwA) 123 | - [Hypothesis Testing Problems Z Test & T Statistics One & Two Tailed Tests 2](https://www.youtube.com/watch?v=zJ8e_wAWUzE) 124 | 125 | ### Adicionales 126 | 127 | - Introducción 128 | - [ANOVA, ANCOVA, MANOVA and MANCOVA: Understand the difference](https://www.youtube.com/watch?v=Q116ZnLy5uA) 129 | - ANOVA 130 | - [ANOVA: Crash Course Statistics](https://www.youtube.com/watch?v=oOuu8IBd-yo) 131 | - [Analysis of Variance (ANOVA)](https://www.youtube.com/watch?v=ITf4vHhyGpc) 132 | - [Statistics 101: ANOVA, A Visual Introduction](https://www.youtube.com/watch?v=0Vj2V2qRU10) 133 | - MANOVA 134 | - [Introduction to One-Way Multivariate Analysis of Variance (One-Way MANOVA)](https://www.youtube.com/watch?v=lChY_6DZGSs) 135 | - ANCOVA 136 | - [Analysis of Covariance (ANCOVA) easily explained](https://www.youtube.com/watch?v=a61mkzQRf6c) 137 | 138 | ### Cursos gratuitos 139 | 140 | - [Elementary Statistics](https://www.udacity.com/course/statistics--st095) - Udacity 141 | - [High School statistics](https://www.khanacademy.org/math/probability) - Khan Academy 142 | 143 | ### Libros recomendados 144 | 145 | - [Introduction to Statistics and Data Analysis](https://link.springer.com/book/10.1007%2F978-3-319-46162-5) (Free) 146 | 147 | ### Videos 148 | 149 | - [Why you should love statistics](https://www.ted.com/talks/alan_smith_why_you_should_love_statistics) 150 | - [The best stats you've ever seen](https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seen](https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seen)) 151 | - [Playlist: Statistical rethinking (2017)](https://www.youtube.com/playlist?list=PLDcUM9US4XdM9_N6XUUFrhghGJ4K25bFc) 152 | 153 | ### Lecturas 154 | 155 | - [Introducción a la probabilidad](https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf) 156 | - [Applied statistics and probability for Engineers](http://www.um.edu.ar/math/montgomery.pdf) 157 | 158 | ### Para practicar 159 | 160 | - [Exercises](https://www0.gsb.columbia.edu/faculty/pglasserman/B6014/exercises.pdf) 161 | - [HackerRank: 10 days of statistics](https://www.hackerrank.com/domains/tutorials/10-days-of-statistics) -------------------------------------------------------------------------------- /layouts/README.md: -------------------------------------------------------------------------------- 1 | # LAYOUTS 2 | 3 | **This directory is not required, you can delete it if you don't want to use it.** 4 | 5 | This directory contains your Application Layouts. 6 | 7 | More information about the usage of this directory in [the documentation](https://nuxtjs.org/guide/views#layouts). 8 | -------------------------------------------------------------------------------- /layouts/default.vue: -------------------------------------------------------------------------------- 1 | 22 | 23 | 41 | 42 | 43 | -------------------------------------------------------------------------------- /middleware/README.md: -------------------------------------------------------------------------------- 1 | # MIDDLEWARE 2 | 3 | **This directory is not required, you can delete it if you don't want to use it.** 4 | 5 | This directory contains your application middleware. 6 | Middleware let you define custom functions that can be run before rendering either a page or a group of pages. 7 | 8 | More information about the usage of this directory in [the documentation](https://nuxtjs.org/guide/routing#middleware). 9 | -------------------------------------------------------------------------------- /nuxt.config.js: -------------------------------------------------------------------------------- 1 | const path = require('path') 2 | const glob = require('glob') 3 | 4 | const markdownPaths = ['blog'] 5 | 6 | module.exports = { 7 | mode: 'spa', 8 | /* 9 | ** Headers of the page 10 | */ 11 | head: { 12 | title: 'Data scientist: Learning path', 13 | meta: [ 14 | { charset: 'utf-8' }, 15 | { name: 'viewport', content: 'width=device-width, initial-scale=1' }, 16 | { hid: 'description', name: 'description', content: 'Data Scientist: Learning Path' } 17 | ], 18 | link: [ 19 | { rel: 'icon', type: 'image/x-icon', href: '/favicon.ico' }, 20 | { rel: 'stylesheet', href: 'https://fonts.googleapis.com/css?family=Source+Sans+Pro:400,400i,600' }, 21 | { rel: 'stylesheet', href: 'https://fonts.googleapis.com/css?family=Open+Sans' } 22 | ] 23 | }, 24 | /* 25 | ** Customize the progress-bar color 26 | */ 27 | loading: { color: '#fff' }, 28 | /* 29 | ** Global CSS 30 | */ 31 | css: [ 32 | '@assets/styles/main.css' 33 | ], 34 | /* 35 | ** Plugins to load before mounting the App 36 | */ 37 | plugins: [ 38 | ], 39 | /* 40 | ** Nuxt.js dev-modules 41 | */ 42 | buildModules: [ 43 | ], 44 | /* 45 | ** Nuxt.js modules 46 | */ 47 | modules: [ 48 | ], 49 | generate: { 50 | routes: dynamicMarkdownRoutes(), 51 | fallback: true, // if you want to use '404.html' instead of the default '200.html' 52 | }, 53 | /* 54 | ** Build configuration 55 | */ 56 | build: { 57 | extend (config, ctx) { 58 | config.module.rules.push( 59 | { 60 | test: /\.md$/, 61 | include: path.resolve(__dirname, "content"), 62 | loader: "frontmatter-markdown-loader", 63 | } 64 | ); 65 | } 66 | } 67 | } 68 | 69 | function dynamicMarkdownRoutes() { 70 | return [].concat( 71 | ...markdownPaths.map(mdPath => { 72 | return glob.sync(`${mdPath}/*.md`, { cwd: 'content' }) 73 | .map(filepath => `${mdPath}/${path.basename(filepath, '.md')}`); 74 | }) 75 | ); 76 | } 77 | 78 | 79 | -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "ds-learning-path", 3 | "version": "1.0.0", 4 | "description": "My first project", 5 | "author": "Yesi", 6 | "private": true, 7 | "scripts": { 8 | "dev": "cross-env NODE_ENV=development nodemon server/index.js --watch server", 9 | "build": "nuxt build", 10 | "start": "cross-env NODE_ENV=production node server/index.js", 11 | "generate": "nuxt generate" 12 | }, 13 | "dependencies": { 14 | "cross-env": "^5.2.0", 15 | "express": "^4.16.4", 16 | "marked": "^1.1.0", 17 | "nuxt": "^2.0.0" 18 | }, 19 | "devDependencies": { 20 | "@nuxtjs/markdownit": "^1.2.9", 21 | "frontmatter-markdown-loader": "^3.5.0", 22 | "nodemon": "^1.18.9" 23 | } 24 | } 25 | -------------------------------------------------------------------------------- /pages/README.md: -------------------------------------------------------------------------------- 1 | # PAGES 2 | 3 | This directory contains your Application Views and Routes. 4 | The framework reads all the `*.vue` files inside this directory and creates the router of your application. 5 | 6 | More information about the usage of this directory in [the documentation](https://nuxtjs.org/guide/routing). 7 | -------------------------------------------------------------------------------- /pages/about.vue: -------------------------------------------------------------------------------- 1 | 72 | 73 | 95 | 96 | 97 | -------------------------------------------------------------------------------- /pages/index.vue: -------------------------------------------------------------------------------- 1 | 114 | 115 | 125 | 126 | 127 | -------------------------------------------------------------------------------- /pages/sections/_slug.vue: -------------------------------------------------------------------------------- 1 | 40 | 41 | 65 | 66 | 67 | -------------------------------------------------------------------------------- /plugins/README.md: -------------------------------------------------------------------------------- 1 | # PLUGINS 2 | 3 | **This directory is not required, you can delete it if you don't want to use it.** 4 | 5 | This directory contains Javascript plugins that you want to run before mounting the root Vue.js application. 6 | 7 | More information about the usage of this directory in [the documentation](https://nuxtjs.org/guide/plugins). 8 | -------------------------------------------------------------------------------- /server/index.js: -------------------------------------------------------------------------------- 1 | const express = require('express') 2 | const consola = require('consola') 3 | const { Nuxt, Builder } = require('nuxt') 4 | const app = express() 5 | 6 | // Import and Set Nuxt.js options 7 | const config = require('../nuxt.config.js') 8 | config.dev = process.env.NODE_ENV !== 'production' 9 | 10 | async function start () { 11 | // Init Nuxt.js 12 | const nuxt = new Nuxt(config) 13 | 14 | const { host, port } = nuxt.options.server 15 | 16 | await nuxt.ready() 17 | // Build only in dev mode 18 | if (config.dev) { 19 | const builder = new Builder(nuxt) 20 | await builder.build() 21 | } 22 | 23 | // Give nuxt middleware to express 24 | app.use(nuxt.render) 25 | 26 | // Listen the server 27 | app.listen(port, host) 28 | consola.ready({ 29 | message: `Server listening on http://${host}:${port}`, 30 | badge: true 31 | }) 32 | } 33 | start() 34 | -------------------------------------------------------------------------------- /static/README.md: -------------------------------------------------------------------------------- 1 | # STATIC 2 | 3 | **This directory is not required, you can delete it if you don't want to use it.** 4 | 5 | This directory contains your static files. 6 | Each file inside this directory is mapped to `/`. 7 | Thus you'd want to delete this README.md before deploying to production. 8 | 9 | Example: `/static/robots.txt` is mapped as `/robots.txt`. 10 | 11 | More information about the usage of this directory in [the documentation](https://nuxtjs.org/guide/assets#static). 12 | -------------------------------------------------------------------------------- /static/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yesidays/ds-learning-path/70814aa390b9efaf54a92999e877c33e64f3aa4e/static/favicon.ico --------------------------------------------------------------------------------