├── .gitignore ├── LICENSE └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Ignito 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-Analytics-with-Projects 2 | This repository contains everything you need to become proficient in Data Analytics 3 | 4 | Youtube for all the implemented projects and tech interview resources - [Ignito Youtube Channel](https://www.youtube.com/@ignito5917/about) 5 | 6 | [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) 7 | 8 | [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) 9 | 10 | [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) 11 | 12 | ------ 13 | 14 | Start here : [Day1 of Data Analytics Series](https://medium.com/coders-mojo/day-1-of-30-days-of-data-analytics-with-projects-series-6c2f939ec865?sk=55671d964311268ae548dbdac902ebe5) 15 | 16 | [Day 1 : Data Analytics basics and kickstart of Data analytics with projects series](https://medium.com/coders-mojo/day-1-of-30-days-of-data-analytics-with-projects-series-6c2f939ec865?sk=55671d964311268ae548dbdac902ebe5) 17 | 18 | [Day 2: Business Understanding — Data Driven Decision Making, Descriptive Analysis, Predictive Analysis, Diagnostic Analysis, Prescriptive Analysis](https://medium.com/coders-mojo/day-2-of-30-days-of-data-analytics-with-projects-series-9127b3ebd17a?sk=97e9632fb10a4862a03bd9b9073c2f50) 19 | 20 | [Day 3 : Data Analytics Ecosystem — Data Life Cycle, Data Analysis complete process ( most important things)](https://medium.com/coders-mojo/day-3-of-30-days-of-data-analytics-with-projects-series-b3f1f7b67bfb) 21 | 22 | [Day 4 : Probability, Conditional Probability, Binomial Distribution, Probability Density Function, Sampling Distribution](https://medium.com/coders-mojo/day-4-of-30-days-of-data-analytics-with-projects-series-ebf27d35ba71?sk=595261f8399a845b76d931c744aea20b) 23 | 24 | [Day 5 : Statistics](https://medium.com/coders-mojo/day-5-of-30-days-of-data-analytics-with-projects-series-2f91895e5be9?sk=b7f01bece5abbbb721a7c801f9fccf6b) 25 | 26 | [Day 6 : Basic and Advanced SQL](https://medium.com/coders-mojo/day-6-of-30-days-of-data-analytics-with-projects-series-bd8265f30684?sk=73ce1d0cc7d9d43fb7b1bd2e40f1611b) 27 | 28 | [Day 7 : Data Collection, Data Cleaning and Python](https://medium.com/coders-mojo/day-7-of-30-days-of-data-analytics-with-projects-series-53d8d858f1b6?sk=881ab7e84076c1983108476294fc3c2a) 29 | 30 | [Day 8 : Pandas and Numpy](https://medium.com/coders-mojo/day-8-of-30-days-of-data-analytics-with-projects-series-91f44c5b3a6a?sk=524cc08de0db2cac4b0202031565c69c) 31 | 32 | [Day 9 : Data Manipulation](https://medium.com/coders-mojo/day-9-of-30-days-of-data-analytics-with-projects-series-ab93f4fdcb1d?sk=93dd5e5b92410b1311fd91ed854db61f) 33 | 34 | [Day 10 : Data Visualization — Part 1](https://medium.com/coders-mojo/day-10-of-30-days-of-data-analytics-with-projects-series-ddcb480fd60e?sk=98ca498a00ff03392c4fe25caadc091b) 35 | 36 | [Day 11 : Project 1 : Data Visualization — Part 2](https://medium.com/coders-mojo/day-11-of-30-days-of-data-analytics-with-projects-series-c0bcba787dc3?sk=cc7d4d8d7c1382a47ccbd5c43df3fc31) 37 | 38 | [Day 12 : Data Visualization — Part 3](https://medium.com/coders-mojo/day-12-of-30-days-of-data-analytics-with-projects-series-122bcfda8261?sk=172b695ed5cb7136886aff15b29cc891) 39 | 40 | [Day 13: Tableau — Part 1](https://medium.com/coders-mojo/day-13-of-30-days-of-data-analytics-with-projects-series-3a3d8fcccb79?sk=f17c6d146d0c0c9922f5e5185690a3b0) 41 | 42 | [Day 14: Tableau — Part 2](https://medium.com/coders-mojo/day-14-of-30-days-of-data-analytics-with-projects-series-37dec2e38dac?sk=95359d7f13e7910b4c98b4fc72619044) 43 | 44 | [Day 15: Tableau — Part 3](https://medium.com/coders-mojo/day-15-of-30-days-of-data-analytics-with-projects-series-1d779d1008b3?sk=03309c9abdd5fe5b5ad24d61b8db2a13) 45 | 46 | [Day 16 : Data Analysis Project 2](https://medium.com/coders-mojo/project-day-16-of-30-days-of-data-analytics-with-projects-series-6992a946c868?sk=0be0825d7d944a67fc779fea277c0f98) 47 | 48 | [Day 17 : Data Analysis Project 3](https://medium.com/coders-mojo/project-3-day-17-of-30-days-of-data-analytics-with-projects-series-a76e254a4b65?sk=0b141a399d22f44c85975ec285efb95b) 49 | 50 | [Day 18: Data Analysis Project 4](https://medium.com/coders-mojo/project-4-day-18-of-30-days-of-data-analytics-with-projects-series-614b8a575d32?sk=2ca301772f1048d767a9947fc3caefda) 51 | 52 | [Day 19: Data Analysis Project 5](https://medium.com/coders-mojo/project-5-day-19-of-30-days-of-data-analytics-with-projects-series-407255f6ab56?sk=bf8aa373bd2d3611b7f6ee384025a925) 53 | 54 | [Day 20 : Data Analysis Project 6 — Part 1](https://medium.com/coders-mojo/project-6-day-20-of-30-days-of-data-analytics-with-projects-series-7f80a9753354?sk=97746824884dbab0803e69170df937b2) 55 | 56 | [Categorical and Numerical Features](https://medium.com/coders-mojo/project-6-day-20-of-30-days-of-data-analytics-with-projects-series-7f80a9753354?sk=97746824884dbab0803e69170df937b2) 57 | 58 | [Missing Value Analysis](https://medium.com/coders-mojo/project-6-day-20-of-30-days-of-data-analytics-with-projects-series-7f80a9753354?sk=97746824884dbab0803e69170df937b2) 59 | 60 | [Fill the missing Values](https://medium.com/coders-mojo/project-6-day-20-of-30-days-of-data-analytics-with-projects-series-7f80a9753354?sk=97746824884dbab0803e69170df937b2) 61 | 62 | [Unique Value Analysis](https://medium.com/coders-mojo/project-6-day-20-of-30-days-of-data-analytics-with-projects-series-7f80a9753354?sk=97746824884dbab0803e69170df937b2) 63 | 64 | [Univariate Analysis](https://medium.com/coders-mojo/project-6-day-20-of-30-days-of-data-analytics-with-projects-series-7f80a9753354?sk=97746824884dbab0803e69170df937b2) 65 | 66 | [Bivariate Analysis](https://medium.com/coders-mojo/project-6-day-20-of-30-days-of-data-analytics-with-projects-series-7f80a9753354?sk=97746824884dbab0803e69170df937b2) 67 | 68 | [Multivariate Analysis](https://medium.com/coders-mojo/project-6-day-20-of-30-days-of-data-analytics-with-projects-series-7f80a9753354?sk=97746824884dbab0803e69170df937b2) 69 | 70 | [Correlation Analysis](https://medium.com/coders-mojo/project-6-day-20-of-30-days-of-data-analytics-with-projects-series-7f80a9753354?sk=97746824884dbab0803e69170df937b2) 71 | 72 | [Day 21 : Data Analysis Project 7](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 73 | 74 | [Data Profiling](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 75 | 76 | [Feature Engineering](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 77 | 78 | [GroupBy Features](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 79 | 80 | [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) 81 | 82 | [Missing Value Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 83 | 84 | [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) 85 | 86 | [Unique Value Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 87 | 88 | [Univariate Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 89 | 90 | [Bivariate Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 91 | 92 | [Multivariate Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 93 | 94 | [Correlation Analysis](https://medium.com/coders-mojo/project-7-day-21-of-30-days-of-data-analytics-with-projects-series-ce24f02de56f?sk=66b7bfb40c9aaaf897ed8d7373d85bf6) 95 | 96 | [Day 22 : Data analysis Project 8](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 97 | 98 | [Linear Regression](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 99 | 100 | [Data Profiling](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 101 | 102 | [Feature Engineering](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 103 | 104 | [Sort Values](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 105 | 106 | [Categorical and Numerical Features](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 107 | 108 | [Missing Value Analysis](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 109 | 110 | [Unique Value Analysis](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 111 | 112 | [Univariate Analysis](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 113 | 114 | [Bivariate Analysis](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 115 | 116 | [Multivariate Analysis](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 117 | 118 | [Correlation Analysis](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 119 | 120 | [Correlation Coefficients](https://medium.com/coders-mojo/project-8-day-22-of-30-days-of-data-analytics-with-projects-series-dc8f463adac6?sk=2a7ac02cb6f0c6be7568c1ba5c2552b5) 121 | 122 | [Day 23: Data Analytics Project 9](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 123 | 124 | [Linear Regression](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 125 | 126 | [Data Profiling](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 127 | 128 | [Correlation Coefficients](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 129 | 130 | [Spearman’s ρ](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 131 | 132 | [Pearson’s r](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 133 | 134 | [Kendall’s τ](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 135 | 136 | [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) 137 | 138 | [Phik (φk)](https://medium.com/coders-mojo/project-9-day-23-of-30-days-of-data-analytics-with-projects-series-6747f695d570?sk=9c51bec759e96404208cebf448409adc) 139 | 140 | [Day 24: Data Analytics Project 10](https://medium.com/coders-mojo/project-10-day-24-of-30-days-of-data-analytics-with-projects-series-7614ea846ab0?sk=3ff451f1dd67c846b5064395dde49f0a) 141 | 142 | [Standardization](https://medium.com/coders-mojo/project-10-day-24-of-30-days-of-data-analytics-with-projects-series-7614ea846ab0?sk=3ff451f1dd67c846b5064395dde49f0a) 143 | 144 | [Encoding](https://medium.com/coders-mojo/project-10-day-24-of-30-days-of-data-analytics-with-projects-series-7614ea846ab0?sk=3ff451f1dd67c846b5064395dde49f0a) 145 | 146 | [Linear Regression](https://medium.com/coders-mojo/project-10-day-24-of-30-days-of-data-analytics-with-projects-series-7614ea846ab0?sk=3ff451f1dd67c846b5064395dde49f0a) 147 | 148 | [Data Profiling](https://medium.com/coders-mojo/project-10-day-24-of-30-days-of-data-analytics-with-projects-series-7614ea846ab0?sk=3ff451f1dd67c846b5064395dde49f0a) 149 | 150 | [Categorical and Numerical Features](https://medium.com/coders-mojo/project-10-day-24-of-30-days-of-data-analytics-with-projects-series-7614ea846ab0?sk=3ff451f1dd67c846b5064395dde49f0a) 151 | 152 | [Missing Value Analysis](https://medium.com/coders-mojo/project-10-day-24-of-30-days-of-data-analytics-with-projects-series-7614ea846ab0?sk=3ff451f1dd67c846b5064395dde49f0a) 153 | 154 | [Unique Value Analysis](https://medium.com/coders-mojo/project-10-day-24-of-30-days-of-data-analytics-with-projects-series-7614ea846ab0?sk=3ff451f1dd67c846b5064395dde49f0a) 155 | 156 | [Univariate Analysis](https://medium.com/coders-mojo/project-10-day-24-of-30-days-of-data-analytics-with-projects-series-7614ea846ab0?sk=3ff451f1dd67c846b5064395dde49f0a) 157 | 158 | [Bivariate Analysis](https://medium.com/coders-mojo/project-10-day-24-of-30-days-of-data-analytics-with-projects-series-7614ea846ab0?sk=3ff451f1dd67c846b5064395dde49f0a) 159 | 160 | [Multivariate Analysis](https://medium.com/coders-mojo/project-10-day-24-of-30-days-of-data-analytics-with-projects-series-7614ea846ab0?sk=3ff451f1dd67c846b5064395dde49f0a) 161 | 162 | [Day 25: Data Analytics Project 11](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 163 | 164 | [Summary Functions](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 165 | 166 | [Indexing](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 167 | 168 | [Grouping](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 169 | 170 | [Sorting](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 171 | 172 | [Data Profiling](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 173 | 174 | [Categorical and Numerical Features](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 175 | 176 | [Missing Value Analysis](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 177 | 178 | [Unique Value Analysis](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 179 | 180 | [Data Visualization](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 181 | 182 | [Correlation Coefficients](https://medium.com/coders-mojo/project-11-day-25-of-30-days-of-data-analytics-with-projects-series-ee6f16db5d59?sk=d6a03230090f77bcadc2918207899cd0) 183 | 184 | [Day 26: Power BI](https://medium.com/coders-mojo/day-26-of-30-days-of-data-analytics-with-projects-series-power-bi-20cfb26a34be?sk=885dd316a5be71677327dfce75b3f87d) 185 | 186 | [Day 27: Performance Metrics](https://medium.com/coders-mojo/day-27-of-30-days-of-data-analytics-with-projects-series-performance-metrics-9f0abdd7b9dd?sk=c15c32be4bbb498818d32ac92c72c529) 187 | 188 | [Day 28: Regression](https://medium.com/coders-mojo/day-28-of-30-days-of-data-analytics-with-projects-series-regression-part-1-e525680782d9?sk=57d3774ca59e7eb7971a567b4005cf78) 189 | 190 | [Linear Regression](https://medium.com/coders-mojo/day-28-of-30-days-of-data-analytics-with-projects-series-regression-part-1-e525680782d9?sk=57d3774ca59e7eb7971a567b4005cf78) 191 | 192 | [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) 193 | 194 | [Polynomial Regression](https://medium.com/coders-mojo/day-28-of-30-days-of-data-analytics-with-projects-series-regression-part-1-e525680782d9?sk=57d3774ca59e7eb7971a567b4005cf78) 195 | 196 | [Day 29: Regression](https://medium.com/coders-mojo/day-29-of-30-days-of-data-analytics-with-projects-series-regression-part-2-48783d03da21?sk=97f4cdd6f04de3ab584773e435cca757) 197 | 198 | [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) 199 | 200 | [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) 201 | 202 | [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) 203 | 204 | [Day 30: Classification](https://medium.com/coders-mojo/day-30-of-30-days-of-data-analytics-with-projects-series-classification-ba0326b5151b?sk=7ff8ff12c31a15e4396eedc2a7eb768e) 205 | 206 | [Naive Bayes](https://medium.com/coders-mojo/day-30-of-30-days-of-data-analytics-with-projects-series-classification-ba0326b5151b?sk=7ff8ff12c31a15e4396eedc2a7eb768e) 207 | 208 | [Random Forest](https://medium.com/coders-mojo/day-30-of-30-days-of-data-analytics-with-projects-series-classification-ba0326b5151b?sk=7ff8ff12c31a15e4396eedc2a7eb768e) 209 | 210 | [Missing Value Analysis](https://medium.com/coders-mojo/day-30-of-30-days-of-data-analytics-with-projects-series-classification-ba0326b5151b?sk=7ff8ff12c31a15e4396eedc2a7eb768e) 211 | 212 | [Unique Value Analysis](https://medium.com/coders-mojo/day-30-of-30-days-of-data-analytics-with-projects-series-classification-ba0326b5151b?sk=7ff8ff12c31a15e4396eedc2a7eb768e) 213 | 214 | Take Complete Hands On Tableau Course : [Link](https://www.udemy.com/course/complete-tableau-2021-hands-on-tableau-for-data-science/) 215 | 216 | 217 | ----------------------- 218 | 219 | 220 | # Some of the other best Series - 221 | 222 | [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) 223 | 224 | [30 days of Machine Learning Ops](https://medium.com/coders-mojo/day-1-of-30-days-of-machine-learning-ops-7c299e4b09be?sk=4ab48350a5c359fc157109e48b1d738f) 225 | 226 | [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) 227 | 228 | [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) 229 | 230 | [30 days of Data Engineering with projects Series](https://medium.com/coders-mojo/day-1-of-30-days-of-data-engineering-894822fcb128?sk=76ba558bfe2d9f85cbe741e505295531) 231 | 232 | [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) 233 | 234 | [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) 235 | 236 | [23 Data Science Techniques You Should Know](https://ai.plainenglish.io/23-data-science-techniques-you-should-know-61bc2c9d1b3a?sk=1680c36193eb22198974c9008d62a33c) 237 | 238 | [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) 239 | 240 | [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) 241 | 242 | [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) 243 | 244 | [Complete Python Series with Projects](https://medium.com/coders-mojo/complete-python-and-projects-mega-compilation-7ec8f7adfe71?sk=ee0ecf43f23c6dd44dd35d984b3e5df4) 245 | 246 | [Complete Advanced Python Series with Projects](https://medium.com/coders-mojo/complete-advanced-python-with-projects-mega-compilation-part-6-729c1826032b?sk=7faffe20f8039fa57099f7a372b6d665) 247 | 248 | [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) 249 | 250 | [Complete Developers Guide to Git](https://medium.datadriveninvestor.com/the-complete-developers-guide-to-git-6a23125996e1?sk=e30479bbe713930ea93018e1a46d9185) 251 | 252 | [Exceptional Github Repos — Part 1](https://medium.com/coders-mojo/6-exceptional-github-repos-for-all-developers-part-1-21e8fa04e150?sk=9140b249af6fe73d45717185fad48962) 253 | 254 | [Exceptional Github Repos — Part 2](https://medium.com/coders-mojo/6-exceptional-github-repos-for-all-developers-part-2-3eec9a68c31c?sk=8e31d0eb7eb1d2d0bbbcecaa66bd4e7e) 255 | 256 | [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) 257 | 258 | [210 Machine Learning Projects](https://medium.datadriveninvestor.com/210-machine-learning-projects-with-source-code-that-you-can-build-today-721b035649e0?sk=da5f593572a0261a6314afad99a0356c) 259 | 260 | ------- 261 | 262 | 263 | # 6 Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) -  264 | 265 | 1. Complete Data Scientist : https://bit.ly/3wiIo8u 266 | 267 | Learn to run data pipelines, design experiments , build recommendation systems, and deploy solutions to the cloud. 268 | 269 | ---- 270 | 271 | 2. Complete Data Engineering : https://bit.ly/3A9oVs5 272 | 273 | Learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets 274 | 275 | ----- 276 | 277 | 3. Complete Machine Learning Engineer : https://bit.ly/3Tir8ub 278 | 279 | Learn advanced machine learning techniques and algorithms - including how to package and deploy your models to a production environment. 280 | 281 | ----- 282 | 283 | 4. Complete Data Product Manager : https://bit.ly/3QGUtwi 284 | 285 | 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. 286 | 287 | ------ 288 | 289 | 5. Complete Natural Language Processing : https://bit.ly/3T7J8qY 290 | 291 | Build models on real data, and get hands-on experience with sentiment analysis, machine translation, and more. 292 | 293 | ------ 294 | 295 | 6. Complete Deep Learning: https://bit.ly/3T5ppIo 296 | 297 | Learn to implement Neural Networks using the deep learning framework PyTorch 298 | 299 | ------ 300 | 301 | 302 | 303 | 304 | 305 | --------------------------------------------------------------------------------