├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Coder-World04 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Complete Data Engineering with Projects Series 2 | 3 | This repository contains everything you need to become proficient in Data Engineering . 4 | 5 | ![0_SwVJoSmjpR4_aRuA](https://user-images.githubusercontent.com/104568275/186138504-a4d30c17-d25d-47a2-ae97-5069162b2f3c.png) 6 | 7 | Pic credits: infra 8 | 9 | Youtube for all the implemented projects and tech interview resources - [Ignito Youtube Channel](https://www.youtube.com/@ignito5917/about) 10 | 11 | [Complete Cheat Sheet for Tech Interviews - How to prepare efficiently](https://open.substack.com/pub/naina0405/p/cheat-sheet-for-tech-interviews-how-339?r=14q3sp&utm_campaign=post&utm_medium=web) 12 | 13 | [I took theses Projects Based Courses to Build Industry aligned Data Science and ML skills](https://open.substack.com/pub/naina0405/p/i-took-theses-projects-based-courses-af3?r=14q3sp&utm_campaign=post&utm_medium=web) 14 | 15 | [Part 1 - How to solve Any ML System Design Problem](https://open.substack.com/pub/naina0405/p/part-1-how-to-solve-any-ml-system?r=14q3sp&utm_campaign=post&utm_medium=web) 16 | 17 | ------------------ 18 | 19 | Pre-requisite : [Day 1 — Day 60 : Quick Recap of 60 days of Data Science and ML](https://medium.com/coders-mojo/day-1-day-60-quick-recap-of-60-days-of-data-science-and-ml-6fc021643d1?sk=4e75e043b7630a9f963562ebac94e129) 20 | 21 | ------------------ 22 | 23 | 1. [Data Engineering](https://medium.com/coders-mojo/day-1-of-30-days-of-data-engineering-894822fcb128?sk=76ba558bfe2d9f85cbe741e505295531) 24 | 25 | [What's Data Engineering](https://medium.com/coders-mojo/day-1-of-30-days-of-data-engineering-894822fcb128?sk=76ba558bfe2d9f85cbe741e505295531) 26 | 27 | [Why Data Engineering](https://medium.com/coders-mojo/day-1-of-30-days-of-data-engineering-894822fcb128?sk=76ba558bfe2d9f85cbe741e505295531) 28 | 29 | [Data Engineers - ML Engineers -- Data Scientists](https://medium.com/coders-mojo/day-1-of-30-days-of-data-engineering-894822fcb128?sk=76ba558bfe2d9f85cbe741e505295531) 30 | 31 | [Purpose and Scope](https://medium.com/coders-mojo/day-1-of-30-days-of-data-engineering-894822fcb128?sk=76ba558bfe2d9f85cbe741e505295531) 32 | 33 | -------------- 34 | 35 | 36 | 37 | 2. [Python for Data Engineering](https://medium.com/coders-mojo/day-2-of-30-days-of-data-engineering-7237dbb8c993?sk=69a04e5b1c00298cd908d9e6fc58bc86) 38 | 39 | [Basic Python with Project](https://medium.com/coders-mojo/day-2-of-30-days-of-data-engineering-7237dbb8c993?sk=69a04e5b1c00298cd908d9e6fc58bc86) 40 | 41 | [Advanced Python with Project](https://medium.com/coders-mojo/day-3-of-30-days-of-data-engineering-series-77d59c404ea0?sk=c8a6470cabb9b9907b18e145c4a97f69) 42 | 43 | [Techniques to write efficient and optimized code](https://medium.com/coders-mojo/day-4-of-30-days-of-data-engineering-series-22b88d116871?sk=01920d343535a05fc119c3a41f2061c0) 44 | 45 | --------------- 46 | 47 | 3. SQL Basics 48 | 49 | [Structured Query Language](https://medium.com/coders-mojo/day-5-of-30-days-of-data-engineering-series-c20129ff7019?sk=2d1328885b529c2e7fa3144c4cb7f8d5) 50 | 51 | [Query Structure](https://medium.com/coders-mojo/day-5-of-30-days-of-data-engineering-series-c20129ff7019?sk=2d1328885b529c2e7fa3144c4cb7f8d5) 52 | 53 | [Conditions](https://medium.com/coders-mojo/day-5-of-30-days-of-data-engineering-series-c20129ff7019?sk=2d1328885b529c2e7fa3144c4cb7f8d5) 54 | 55 | [Joins](https://medium.com/coders-mojo/day-5-of-30-days-of-data-engineering-series-c20129ff7019?sk=2d1328885b529c2e7fa3144c4cb7f8d5) 56 | 57 | [Stored Procedures](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 58 | 59 | -------------- 60 | 61 | 62 | 4. Aggregations 63 | 64 | [Wild cards](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 65 | 66 | [Grouping Data](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 67 | 68 | [Aggregation Functions](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 69 | 70 | [Filtering](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 71 | 72 | [Sequences](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 73 | 74 | [Group By, Order By](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 75 | 76 | [Having Clause](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 77 | 78 | [Write Sub queries](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 79 | 80 | [Grouping Sets](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 81 | 82 | [Analytical Functions](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 83 | 84 | ----------- 85 | 86 | 5. Window Functions 87 | 88 | [Row Numbering](https://medium.com/coders-mojo/day-8-of-30-days-of-data-engineering-series-with-projects-de46be64dafc?sk=637370c20d50f6085cc257b46f6f4fa8) 89 | 90 | [Percentile](https://medium.com/coders-mojo/day-8-of-30-days-of-data-engineering-series-with-projects-de46be64dafc?sk=637370c20d50f6085cc257b46f6f4fa8) 91 | 92 | [Advanced windowing techniques](https://medium.com/coders-mojo/day-8-of-30-days-of-data-engineering-series-with-projects-de46be64dafc?sk=637370c20d50f6085cc257b46f6f4fa8) 93 | 94 | ----------- 95 | 96 | 6. BigQuery 97 | 98 | [BigQuery Basics](https://medium.com/coders-mojo/day-7-of-30-days-of-data-engineering-series-with-projects-116a4479c81b?sk=432cd30e2a8ce7c1ab6a720694f8211e) 99 | 100 | [SELECT, FROM, WHERE and Date and Extract in BigQuery](https://medium.com/coders-mojo/day-7-of-30-days-of-data-engineering-series-with-projects-116a4479c81b?sk=432cd30e2a8ce7c1ab6a720694f8211e) 101 | 102 | [Common Expression Table](https://medium.com/coders-mojo/day-7-of-30-days-of-data-engineering-series-with-projects-116a4479c81b?sk=432cd30e2a8ce7c1ab6a720694f8211e) 103 | 104 | [UNNEST Clause](https://medium.com/coders-mojo/day-7-of-30-days-of-data-engineering-series-with-projects-116a4479c81b?sk=432cd30e2a8ce7c1ab6a720694f8211e) 105 | 106 | [SQL vs NoSQL Database](https://medium.com/coders-mojo/day-7-of-30-days-of-data-engineering-series-with-projects-116a4479c81b?sk=432cd30e2a8ce7c1ab6a720694f8211e) 107 | 108 | ----------- 109 | 110 | 7. Advanced Functions 111 | 112 | [Triggers](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 113 | 114 | [Pivot](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 115 | 116 | [Cursors](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 117 | 118 | [Views](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 119 | 120 | [Indexes](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 121 | 122 | [Auto Increment](https://medium.com/coders-mojo/day-6-of-30-days-of-data-engineering-series-with-projects-9d491908d822?sk=1f9e5d31cfb3ee5f38405070e91b985f) 123 | 124 | ------------- 125 | 126 | 8. Performance Tuning SQL Queries 127 | 128 | [Query Optimizations in SQL](https://medium.com/coders-mojo/day-9-of-30-days-of-data-engineering-series-with-projects-418386440bf?sk=a7efea213c65d9365b784d0fafedb477) 129 | 130 | ------------- 131 | 132 | 9. MySQL, PostgreSQL and MongoDB 133 | 134 | [Introduction to MySQL](https://medium.com/coders-mojo/day-10-of-30-days-of-data-engineering-series-with-projects-d6534af15cdd?sk=d4e0e07c2fbf0c57d1ec6d3ed96b8f7e) 135 | 136 | [Introduction to PostgreSQL](https://medium.com/coders-mojo/day-10-of-30-days-of-data-engineering-series-with-projects-d6534af15cdd?sk=d4e0e07c2fbf0c57d1ec6d3ed96b8f7e) 137 | 138 | [Introduction to Mongo DB](https://medium.com/coders-mojo/day-10-of-30-days-of-data-engineering-series-with-projects-d6534af15cdd?sk=d4e0e07c2fbf0c57d1ec6d3ed96b8f7e) 139 | 140 | [Comparison between MySQL and PostgreSQL and Mongo DB](https://medium.com/coders-mojo/day-10-of-30-days-of-data-engineering-series-with-projects-d6534af15cdd?sk=d4e0e07c2fbf0c57d1ec6d3ed96b8f7e) 141 | 142 | [Introduction to SQL and NoSQL Databases](https://medium.com/coders-mojo/day-10-of-30-days-of-data-engineering-series-with-projects-d6534af15cdd?sk=d4e0e07c2fbf0c57d1ec6d3ed96b8f7e) 143 | 144 | [MySQL in Depth](https://medium.com/coders-mojo/day-10-of-30-days-of-data-engineering-series-with-projects-d6534af15cdd?sk=d4e0e07c2fbf0c57d1ec6d3ed96b8f7e) 145 | 146 | ----------- 147 | 148 | 10. Scripting and Automation 149 | 150 | [Shell Scripting](https://medium.com/coders-mojo/day-11-of-30-days-of-data-engineering-series-with-projects-3a3e8881ce34?sk=2da1d170968a55b21b6f1541e01c7244) 151 | 152 | [ETL ( Extract, Tranform and Load) basics](https://medium.com/coders-mojo/day-20-of-30-days-of-data-engineering-series-with-projects-d8b4d08fac38?sk=af37983782660e0f58fb15e27f9598f6) 153 | 154 | [Why ETL is important?](https://medium.com/coders-mojo/day-20-of-30-days-of-data-engineering-series-with-projects-d8b4d08fac38?sk=af37983782660e0f58fb15e27f9598f6) 155 | 156 | [How ETL works](https://medium.com/coders-mojo/day-20-of-30-days-of-data-engineering-series-with-projects-d8b4d08fac38?sk=af37983782660e0f58fb15e27f9598f6) 157 | 158 | [ETL Tools](https://medium.com/coders-mojo/day-20-of-30-days-of-data-engineering-series-with-projects-d8b4d08fac38?sk=af37983782660e0f58fb15e27f9598f6) 159 | 160 | ----------- 161 | 162 | 11. Relational Databases and SQL 163 | 164 | [Basic SQL](https://medium.com/coders-mojo/day-5-of-30-days-of-data-engineering-series-c20129ff7019?sk=2d1328885b529c2e7fa3144c4cb7f8d5) 165 | 166 | [Advanced SQL](https://medium.com/coders-mojo/day-5-of-30-days-of-data-engineering-series-c20129ff7019?sk=2d1328885b529c2e7fa3144c4cb7f8d5) 167 | 168 | ----------- 169 | 170 | 12. NoSQL Data bases and Map Reduce 171 | 172 | [Data Warehouses](https://medium.com/coders-mojo/day-12-of-30-days-of-data-engineering-series-with-projects-a4f0654080aa?sk=cc1e6e3094cf5accc41bf6e7aa8534f6) 173 | 174 | [Data Lakes](https://medium.com/coders-mojo/day-12-of-30-days-of-data-engineering-series-with-projects-a4f0654080aa?sk=cc1e6e3094cf5accc41bf6e7aa8534f6) 175 | 176 | [Structured Data](https://medium.com/coders-mojo/day-21-of-30-days-of-data-engineering-series-with-projects-b93ac7b1386c?sk=51bed942fd1afc69b31dcf82d1c30b9d) 177 | 178 | [Semi Structured Data](https://medium.com/coders-mojo/day-21-of-30-days-of-data-engineering-series-with-projects-b93ac7b1386c?sk=51bed942fd1afc69b31dcf82d1c30b9d) 179 | 180 | [Unstructured Data](https://medium.com/coders-mojo/day-21-of-30-days-of-data-engineering-series-with-projects-b93ac7b1386c?sk=51bed942fd1afc69b31dcf82d1c30b9d) 181 | 182 | [Data Mart](https://medium.com/coders-mojo/day-21-of-30-days-of-data-engineering-series-with-projects-b93ac7b1386c?sk=51bed942fd1afc69b31dcf82d1c30b9d) 183 | 184 | [Map-Reduce](https://medium.com/coders-mojo/day-12-of-30-days-of-data-engineering-series-with-projects-a4f0654080aa?sk=cc1e6e3094cf5accc41bf6e7aa8534f6) 185 | 186 | ----------- 187 | 188 | 13.Data Analysis 189 | 190 | [Pandas](https://medium.com/coders-mojo/day-13-of-30-days-of-data-engineering-series-with-projects-24d70bdf0baa?sk=30a5b77b65234aefe09d2fa6e71a7d1a) 191 | 192 | [Numpy](https://medium.com/coders-mojo/day-14-of-30-days-of-data-engineering-series-with-projects-fb72a73cd394?sk=8a5ebb3e9ef9f1d7f848c41bb792c9c8) 193 | 194 | [Advanced Pandas Techniques](https://medium.com/coders-mojo/day-15-of-30-days-of-data-engineering-series-with-projects-cd339b6445fe?sk=9b6e2443144ae66b9d2087a783f751c6) 195 | 196 | [Data Pre-processing](https://medium.com/coders-mojo/day-16-of-30-days-of-data-engineering-series-with-projects-3e528488aae2?sk=1ee79211d1455ef46cd73fe1c62344cb) 197 | 198 | [Handling missing values](https://medium.com/coders-mojo/day-16-of-30-days-of-data-engineering-series-with-projects-3e528488aae2?sk=1ee79211d1455ef46cd73fe1c62344cb) 199 | 200 | [Data Cleaning](https://medium.com/coders-mojo/day-16-of-30-days-of-data-engineering-series-with-projects-3e528488aae2?sk=1ee79211d1455ef46cd73fe1c62344cb) 201 | 202 | [Mean/mode/median Imputation](https://medium.com/coders-mojo/day-16-of-30-days-of-data-engineering-series-with-projects-3e528488aae2?sk=1ee79211d1455ef46cd73fe1c62344cb) 203 | 204 | [Hot Deck Imputation](https://medium.com/coders-mojo/day-16-of-30-days-of-data-engineering-series-with-projects-3e528488aae2?sk=1ee79211d1455ef46cd73fe1c62344cb) 205 | 206 | [Rescale Data](https://medium.com/coders-mojo/day-16-of-30-days-of-data-engineering-series-with-projects-3e528488aae2?sk=1ee79211d1455ef46cd73fe1c62344cb) 207 | 208 | [Binarize Data](https://medium.com/coders-mojo/day-16-of-30-days-of-data-engineering-series-with-projects-3e528488aae2?sk=1ee79211d1455ef46cd73fe1c62344cb) 209 | 210 | [Regression Imputation](https://medium.com/coders-mojo/day-16-of-30-days-of-data-engineering-series-with-projects-3e528488aae2?sk=1ee79211d1455ef46cd73fe1c62344cb) 211 | 212 | [Stochastic regression imputation](https://medium.com/coders-mojo/day-16-of-30-days-of-data-engineering-series-with-projects-3e528488aae2?sk=1ee79211d1455ef46cd73fe1c62344cb) 213 | 214 | [Feature Scaling](https://medium.com/coders-mojo/day-16-of-30-days-of-data-engineering-series-with-projects-3e528488aae2?sk=1ee79211d1455ef46cd73fe1c62344cb) 215 | 216 | [Data Augmentation](https://medium.com/coders-mojo/day-17-of-30-days-of-data-engineering-series-with-projects-8271d43171f2?sk=9c20abe78f0aae3fd768a0ee81684b08) 217 | 218 | [Read and Process Large Datasets](https://medium.com/coders-mojo/day-17-of-30-days-of-data-engineering-series-with-projects-8271d43171f2?sk=9c20abe78f0aae3fd768a0ee81684b08) 219 | 220 | [Data Visualization basics](https://naina0412.medium.com/day-18-of-30-days-of-data-engineering-series-with-projects-eda8e857aa3b?sk=39883c3111ce1f8a88b3b1bdc567d704) 221 | 222 | [Data Visualization Projects](https://naina0412.medium.com/day-18-of-30-days-of-data-engineering-series-with-projects-eda8e857aa3b?sk=39883c3111ce1f8a88b3b1bdc567d704) 223 | 224 | [Data Visualization using Plotly and Bokeh](https://naina0412.medium.com/day-18-of-30-days-of-data-engineering-series-with-projects-eda8e857aa3b?sk=39883c3111ce1f8a88b3b1bdc567d704) 225 | 226 | [Data Profiling](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 227 | 228 | [Summary Functions](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 229 | 230 | [Indexing](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 231 | 232 | [Grouping](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 233 | 234 | [Linear Regression](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 235 | 236 | [Multi Linear Regression](https://medium.com/coders-mojo/day-28-of-30-days-of-data-analytics-with-projects-series-regression-part-1-e525680782d9?sk=57d3774ca59e7eb7971a567b4005cf78) 237 | 238 | [Polynomial Regression](https://medium.com/coders-mojo/day-28-of-30-days-of-data-analytics-with-projects-series-regression-part-1-e525680782d9?sk=57d3774ca59e7eb7971a567b4005cf78) 239 | 240 | [Regression](https://medium.com/coders-mojo/day-29-of-30-days-of-data-analytics-with-projects-series-regression-part-2-48783d03da21?sk=97f4cdd6f04de3ab584773e435cca757) 241 | 242 | [Support Vector Regression](https://medium.com/coders-mojo/day-29-of-30-days-of-data-analytics-with-projects-series-regression-part-2-48783d03da21?sk=97f4cdd6f04de3ab584773e435cca757) 243 | 244 | [Decision Tree Regression](https://medium.com/coders-mojo/day-29-of-30-days-of-data-analytics-with-projects-series-regression-part-2-48783d03da21?sk=97f4cdd6f04de3ab584773e435cca757) 245 | 246 | [Random Forest Regression](https://medium.com/coders-mojo/day-29-of-30-days-of-data-analytics-with-projects-series-regression-part-2-48783d03da21?sk=97f4cdd6f04de3ab584773e435cca757) 247 | 248 | [Feature Engineering](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 249 | 250 | [GroupBy Features](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 251 | 252 | [Categorical and Numerical Features](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 253 | 254 | [Missing Value Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 255 | 256 | [Fill the missing Values](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 257 | 258 | [Unique Value Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 259 | 260 | [Univariate Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 261 | 262 | [Bivariate Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 263 | 264 | [Multivariate Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 265 | 266 | [Correlation Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 267 | 268 | [Spearman’s ρ](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 269 | 270 | [Pearson’s r](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 271 | 272 | [Kendall’s τ](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 273 | 274 | [Cramér’s V (φc)](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 275 | 276 | [Phik (φk)](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 277 | 278 | ----------- 279 | 280 | 14. Data Processing Techniques 281 | 282 | [Batch Processing](https://medium.com/coders-mojo/day-23-of-30-days-of-data-engineering-series-with-projects-cc0f8f8be3e2?sk=e71f06903a8ce01ca75b57b4ed7a5d38) 283 | 284 | [Stream Processing](https://medium.com/coders-mojo/day-23-of-30-days-of-data-engineering-series-with-projects-cc0f8f8be3e2?sk=e71f06903a8ce01ca75b57b4ed7a5d38) 285 | 286 | [Apache Spark](https://medium.com/coders-mojo/day-23-of-30-days-of-data-engineering-series-with-projects-cc0f8f8be3e2?sk=e71f06903a8ce01ca75b57b4ed7a5d38) 287 | 288 | [Apache Spark Commands](https://medium.com/coders-mojo/day-23-of-30-days-of-data-engineering-series-with-projects-cc0f8f8be3e2?sk=e71f06903a8ce01ca75b57b4ed7a5d38) 289 | 290 | [Apache Kafka](https://medium.com/coders-mojo/day-23-of-30-days-of-data-engineering-series-with-projects-cc0f8f8be3e2?sk=e71f06903a8ce01ca75b57b4ed7a5d38) 291 | 292 | [How Apache Kafka works](https://medium.com/coders-mojo/day-23-of-30-days-of-data-engineering-series-with-projects-cc0f8f8be3e2?sk=e71f06903a8ce01ca75b57b4ed7a5d38) 293 | 294 | ----------- 295 | 296 | 15. Big Data 297 | 298 | [Big Data](https://medium.com/coders-mojo/day-22-of-30-days-of-data-engineering-series-with-projects-6af0daa939a3?sk=6fdf51209a4dc02b91f2908ea7ed34ad) 299 | 300 | [Types of Big Data](https://medium.com/coders-mojo/day-22-of-30-days-of-data-engineering-series-with-projects-6af0daa939a3?sk=6fdf51209a4dc02b91f2908ea7ed34ad) 301 | 302 | [Big data tools](https://medium.com/coders-mojo/day-22-of-30-days-of-data-engineering-series-with-projects-6af0daa939a3?sk=6fdf51209a4dc02b91f2908ea7ed34ad) 303 | 304 | [SQL and NoSQL Databases](https://medium.com/coders-mojo/day-22-of-30-days-of-data-engineering-series-with-projects-6af0daa939a3?sk=6fdf51209a4dc02b91f2908ea7ed34ad) 305 | 306 | [Hadoop](https://medium.com/coders-mojo/day-22-of-30-days-of-data-engineering-series-with-projects-6af0daa939a3?sk=6fdf51209a4dc02b91f2908ea7ed34ad) 307 | 308 | [Hadoop HDFS](https://medium.com/coders-mojo/day-22-of-30-days-of-data-engineering-series-with-projects-6af0daa939a3?sk=6fdf51209a4dc02b91f2908ea7ed34ad) 309 | 310 | [Hadoop Yarn](https://medium.com/coders-mojo/day-22-of-30-days-of-data-engineering-series-with-projects-6af0daa939a3?sk=6fdf51209a4dc02b91f2908ea7ed34ad) 311 | 312 | [Hive](https://medium.com/coders-mojo/day-24-of-30-days-of-data-engineering-series-with-projects-8761692e7a51?sk=306f0084ded06f638b1478eeb46b8cad) 313 | 314 | [Zookeeper](https://medium.com/coders-mojo/day-24-of-30-days-of-data-engineering-series-with-projects-8761692e7a51?sk=306f0084ded06f638b1478eeb46b8cad) 315 | 316 | [Pig](https://medium.com/coders-mojo/day-24-of-30-days-of-data-engineering-series-with-projects-8761692e7a51?sk=306f0084ded06f638b1478eeb46b8cad) 317 | 318 | [Cassandra](https://medium.com/coders-mojo/day-24-of-30-days-of-data-engineering-series-with-projects-8761692e7a51?sk=306f0084ded06f638b1478eeb46b8cad) 319 | 320 | [Sqoop](https://medium.com/coders-mojo/day-24-of-30-days-of-data-engineering-series-with-projects-8761692e7a51?sk=306f0084ded06f638b1478eeb46b8cad) 321 | 322 | ----------- 323 | 324 | 16. Data Pipelines and WorkFlows 325 | 326 | [Data Pipelines](https://medium.com/coders-mojo/day-26-of-30-days-of-data-engineering-series-with-projects-8798d9e6213d?sk=1ca8a3ee44e8cc58cfca3dc87bd8c17d) 327 | 328 | [Transformation](https://medium.com/coders-mojo/day-26-of-30-days-of-data-engineering-series-with-projects-8798d9e6213d?sk=1ca8a3ee44e8cc58cfca3dc87bd8c17d) 329 | 330 | [Processing](https://medium.com/coders-mojo/day-26-of-30-days-of-data-engineering-series-with-projects-8798d9e6213d?sk=1ca8a3ee44e8cc58cfca3dc87bd8c17d) 331 | 332 | [Workflow](https://medium.com/coders-mojo/day-26-of-30-days-of-data-engineering-series-with-projects-8798d9e6213d?sk=1ca8a3ee44e8cc58cfca3dc87bd8c17d) 333 | 334 | [Monitoring](https://medium.com/coders-mojo/day-26-of-30-days-of-data-engineering-series-with-projects-8798d9e6213d?sk=1ca8a3ee44e8cc58cfca3dc87bd8c17d) 335 | 336 | [Airflow](https://medium.com/coders-mojo/day-26-of-30-days-of-data-engineering-series-with-projects-8798d9e6213d?sk=1ca8a3ee44e8cc58cfca3dc87bd8c17d) 337 | 338 | [DAG](https://medium.com/coders-mojo/day-26-of-30-days-of-data-engineering-series-with-projects-8798d9e6213d?sk=1ca8a3ee44e8cc58cfca3dc87bd8c17d) 339 | 340 | ----------- 341 | 342 | 17. Infrastructure 343 | 344 | [Docker](https://medium.com/coders-mojo/day-25-of-30-days-of-data-engineering-series-with-projects-437bc40598fe?sk=c7b7a4ad4318571764a26c8e0a7542c0) 345 | 346 | [Docker vs Virtual Machines](https://medium.com/coders-mojo/day-25-of-30-days-of-data-engineering-series-with-projects-437bc40598fe?sk=c7b7a4ad4318571764a26c8e0a7542c0) 347 | 348 | [Most important Docker commands](https://medium.com/coders-mojo/day-25-of-30-days-of-data-engineering-series-with-projects-437bc40598fe?sk=c7b7a4ad4318571764a26c8e0a7542c0) 349 | 350 | [Kubernetes](https://medium.com/coders-mojo/day-25-of-30-days-of-data-engineering-series-with-projects-437bc40598fe?sk=c7b7a4ad4318571764a26c8e0a7542c0) 351 | 352 | [Snowflake](https://medium.com/coders-mojo/day-25-of-30-days-of-data-engineering-series-with-projects-437bc40598fe?sk=c7b7a4ad4318571764a26c8e0a7542c0) 353 | 354 | ----------- 355 | 356 | 18. Power BI 357 | 358 | [Power BI](https://medium.com/coders-mojo/day-27-of-30-days-of-data-engineering-series-with-projects-b132bb7fdcc3?sk=a4613412d75226d22084a56fb5184f57) 359 | 360 | [Which chart to use and When?](https://medium.com/coders-mojo/day-27-of-30-days-of-data-engineering-series-with-projects-b132bb7fdcc3?sk=a4613412d75226d22084a56fb5184f57) 361 | 362 | [Power BI — Data Analysis Expressions](https://medium.com/coders-mojo/day-27-of-30-days-of-data-engineering-series-with-projects-b132bb7fdcc3?sk=a4613412d75226d22084a56fb5184f57) 363 | 364 | [Joins](https://medium.com/coders-mojo/day-27-of-30-days-of-data-engineering-series-with-projects-b132bb7fdcc3?sk=a4613412d75226d22084a56fb5184f57) 365 | 366 | [Data Profiling](https://medium.com/coders-mojo/day-27-of-30-days-of-data-engineering-series-with-projects-b132bb7fdcc3?sk=a4613412d75226d22084a56fb5184f57) 367 | 368 | ----------- 369 | 370 | 19. Cloud Data Engineering 371 | 372 | [Data Engineering on cloud](https://medium.com/coders-mojo/day-29-of-30-days-of-data-engineering-series-with-projects-cd7882ec1885?sk=661b3a4170e19079df5a264bba835815) 373 | 374 | [AWS](https://medium.com/coders-mojo/day-29-of-30-days-of-data-engineering-series-with-projects-cd7882ec1885?sk=661b3a4170e19079df5a264bba835815) 375 | 376 | [AWS Services](https://medium.com/coders-mojo/day-29-of-30-days-of-data-engineering-series-with-projects-cd7882ec1885?sk=661b3a4170e19079df5a264bba835815) 377 | 378 | [Google Cloud Platform](https://medium.com/coders-mojo/day-29-of-30-days-of-data-engineering-series-with-projects-cd7882ec1885?sk=661b3a4170e19079df5a264bba835815) 379 | 380 | [Google Cloud Platform services](https://medium.com/coders-mojo/day-29-of-30-days-of-data-engineering-series-with-projects-cd7882ec1885?sk=661b3a4170e19079df5a264bba835815) 381 | 382 | ----------- 383 | 384 | 20. Machine Learning Algorithms 385 | 386 | [Linear Regression](https://medium.com/coders-mojo/day-30-of-30-days-of-data-engineering-series-with-projects-f73681fa0e2?sk=9065c4c01a7a03ba075a3489a29a019a) 387 | 388 | [Logistic Regression](https://medium.com/coders-mojo/day-30-of-30-days-of-data-engineering-series-with-projects-f73681fa0e2?sk=9065c4c01a7a03ba075a3489a29a019a) 389 | 390 | [Decision Trees](https://medium.com/coders-mojo/day-30-of-30-days-of-data-engineering-series-with-projects-f73681fa0e2?sk=9065c4c01a7a03ba075a3489a29a019a) 391 | 392 | [Random Forest](https://medium.com/coders-mojo/day-30-of-30-days-of-data-engineering-series-with-projects-f73681fa0e2?sk=9065c4c01a7a03ba075a3489a29a019a) 393 | 394 | [Support Vector Machines](https://medium.com/coders-mojo/day-30-of-30-days-of-data-engineering-series-with-projects-f73681fa0e2?sk=9065c4c01a7a03ba075a3489a29a019a) 395 | 396 | [K Nearest Neighbors](https://medium.com/coders-mojo/day-30-of-30-days-of-data-engineering-series-with-projects-f73681fa0e2?sk=9065c4c01a7a03ba075a3489a29a019a) 397 | 398 | [K means Clustering](https://medium.com/coders-mojo/day-30-of-30-days-of-data-engineering-series-with-projects-f73681fa0e2?sk=9065c4c01a7a03ba075a3489a29a019a) 399 | 400 | [Hierarchical Clustering](https://medium.com/coders-mojo/day-30-of-30-days-of-data-engineering-series-with-projects-f73681fa0e2?sk=9065c4c01a7a03ba075a3489a29a019a) 401 | 402 | [Neural Networks](https://medium.com/coders-mojo/day-30-of-30-days-of-data-engineering-series-with-projects-f73681fa0e2?sk=9065c4c01a7a03ba075a3489a29a019a) 403 | 404 | 405 | ---------- 406 | 407 | 408 | # Some of the other best Series- 409 | 410 | [Complete 60 Days of Data Science and Machine Learning Series ](https://medium.com/coders-mojo/day-1-day-60-quick-recap-of-60-days-of-data-science-and-ml-6fc021643d1?sk=4e75e043b7630a9f963562ebac94e129) 411 | 412 | [30 days of Machine Learning Ops](https://medium.com/coders-mojo/day-1-of-30-days-of-machine-learning-ops-7c299e4b09be?sk=4ab48350a5c359fc157109e48b1d738f) 413 | 414 | [30 Days of Natural Language Processing ( NLP) Series](https://medium.com/coders-mojo/quick-recap-30-days-of-natural-language-processing-nlp-with-projects-series-ceb674e3c09b?sk=ca09b27b3d5867f23ab4dc367b6c0c32) 415 | 416 | [Data Science and Machine Learning Research ( papers) Simplified **](https://medium.com/coders-mojo/day-1-data-science-and-ml-research-papers-simplified-a68b00a3b1c4?sk=56136229ff738bd734f19d2b6953f78c) 417 | 418 | [30 days of Data Engineering with projects Series](https://medium.com/coders-mojo/day-1-of-30-days-of-data-engineering-894822fcb128?sk=76ba558bfe2d9f85cbe741e505295531) 419 | 420 | [60 days of Data Science and ML Series with projects](https://medium.com/coders-mojo/day-1-day-60-quick-recap-of-60-days-of-data-science-and-ml-6fc021643d1?sk=4e75e043b7630a9f963562ebac94e129) 421 | 422 | [100 days : Your Data Science and Machine Learning Degree Series with projects](https://medium.com/coders-mojo/100-days-your-data-science-and-ml-degree-part-3-c621ecfdf711?sk=1a8c7b0c204d73432d56b7d1a3a26474) 423 | 424 | [23 Data Science Techniques You Should Know](https://ai.plainenglish.io/23-data-science-techniques-you-should-know-61bc2c9d1b3a?sk=1680c36193eb22198974c9008d62a33c) 425 | 426 | [Tech Interview Series — Curated List of coding questions](https://medium.com/coders-mojo/mega-post-tech-interview-the-only-list-of-questions-you-need-to-practice-ee349ea197bb?sk=fac3614684daff4b50a70c0a71e4d528) 427 | 428 | [Complete System Design with most popular Questions Series](https://medium.com/coders-mojo/system-design-made-easy-quick-recap-of-complete-system-design-34af7e3aedfb?sk=bdd6a19edc1f3ce4a5064923f5b68721) 429 | 430 | [Complete Data Visualization and Pre-processing Series with projects](https://medium.com/coders-mojo/complete-data-preprocessing-and-data-visualization-with-projects-mega-compilation-part-2-41584ef0920e?sk=842390da51689b8d43148c3980570db0) 431 | 432 | [Complete Python Series with Projects](https://medium.com/coders-mojo/complete-python-and-projects-mega-compilation-7ec8f7adfe71?sk=ee0ecf43f23c6dd44dd35d984b3e5df4) 433 | 434 | [Complete Advanced Python Series with Projects](https://medium.com/coders-mojo/complete-advanced-python-with-projects-mega-compilation-part-6-729c1826032b?sk=7faffe20f8039fa57099f7a372b6d665) 435 | 436 | [Kaggle Best Notebooks that will teach you the most](https://medium.com/coders-mojo/my-list-of-kaggle-best-notebooks-topic-wise-data-science-and-machine-learning-part-2-84772863e9ae?sk=5ed02e419854a6c11add3ddc1e52947f) 437 | 438 | [Complete Developers Guide to Git](https://medium.datadriveninvestor.com/the-complete-developers-guide-to-git-6a23125996e1?sk=e30479bbe713930ea93018e1a46d9185) 439 | 440 | [Exceptional Github Repos — Part 1](https://medium.com/coders-mojo/6-exceptional-github-repos-for-all-developers-part-1-21e8fa04e150?sk=9140b249af6fe73d45717185fad48962) 441 | 442 | [Exceptional Github Repos — Part 2](https://medium.com/coders-mojo/6-exceptional-github-repos-for-all-developers-part-2-3eec9a68c31c?sk=8e31d0eb7eb1d2d0bbbcecaa66bd4e7e) 443 | 444 | [All the Data Science and Machine Learning Resources](https://medium.datadriveninvestor.com/best-resources-for-data-science-and-machine-learning-full-list-5ceb9a2791bf?sk=cf85b2cef95560c58509877a794577ff) 445 | 446 | [210 Machine Learning Projects](https://medium.datadriveninvestor.com/210-machine-learning-projects-with-source-code-that-you-can-build-today-721b035649e0?sk=da5f593572a0261a6314afad99a0356c) 447 | 448 | ------- 449 | 450 | 451 | # 6 Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) -  452 | 453 | 1. Complete Data Scientist : https://bit.ly/3wiIo8u 454 | 455 | Learn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud. 456 | 457 | ---- 458 | 459 | 2. Complete Data Engineering : https://bit.ly/3A9oVs5 460 | 461 | Learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets 462 | 463 | ----- 464 | 465 | 3. Complete Machine Learning Engineer : https://bit.ly/3Tir8ub 466 | 467 | Learn advanced machine learning techniques and algorithms - including how to package and deploy your models to a production environment. 468 | 469 | ----- 470 | 471 | 4. Complete Data Product Manager : https://bit.ly/3QGUtwi 472 | 473 | Leverage data to build products that deliver the right experiences, to the right users, at the right time. Lead the development of data-driven products that position businesses to win in their market. 474 | 475 | ------ 476 | 477 | 5. Complete Natural Language Processing : https://bit.ly/3T7J8qY 478 | 479 | Build models on real data, and get hands-on experience with sentiment analysis, machine translation, and more. 480 | 481 | ------ 482 | 483 | 6. Complete Deep Learning: https://bit.ly/3T5ppIo 484 | 485 | Learn to implement Neural Networks using the deep learning framework PyTorch 486 | 487 | ------ 488 | --------------------------------------------------------------------------------