├── .github └── FUNDING.yml ├── 30 days of interview preparation.pdf ├── Books ├── Abraham Silberschatz, Henry Korth and S. Sudarshan - Database System Concepts. 7-McGraw-Hill Education (2020).pdf ├── Advanced Applied Deep Learning-Umberto Michelucci.pdf ├── Data_Science_from_Scratch_First_Principles_with_Python_by_Joel_Grus.pdf ├── Deep Learning-Ian Goodfellow.pdf ├── Essential-Math-for-Data-Science-by-Thomas-Nield_bibis.ir.pdf ├── Hadoop-The.Definitive.Guide_4.edition_a_Tom.White_April-2015-1.pdf ├── Introduction to Statistical Learning with Applications in R.pdf ├── Masato_Hagiwara_Real_World_Natural_Language_Processing_Practical.pdf ├── Natural_Language_Processing_with_Python.pdf ├── Practical Statistics for Data Scientists.pdf ├── Python-for-Data-Analysis.pdf ├── SQL For Dummies.pdf ├── SQL for Data Analysis.pdf ├── The-Data-Warehouse-Toolkit-3rd-Edition.pdf └── Transfer Learning - Qiang Yang.pdf ├── Content - Guide!!.txt ├── LICENSE └── README.md /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | 3 | github: [Moataz-Elmesmary] 4 | patreon: # Replace with a single Patreon username 5 | open_collective: # Replace with a single Open Collective username 6 | ko_fi: # Replace with a single Ko-fi username 7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel 8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry 9 | liberapay: # Replace with a single Liberapay username 10 | issuehunt: # Replace with a single IssueHunt username 11 | otechie: # Replace with a single Otechie username 12 | lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry 13 | custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] 14 | -------------------------------------------------------------------------------- /30 days of interview preparation.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIS-Team/Data-Science-Roadmap-2025/60988015fd43776ded553d4732e9830214cee72d/30 days of interview preparation.pdf -------------------------------------------------------------------------------- /Books/Abraham Silberschatz, Henry Korth and S. Sudarshan - Database System Concepts. 7-McGraw-Hill Education (2020).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIS-Team/Data-Science-Roadmap-2025/60988015fd43776ded553d4732e9830214cee72d/Books/Abraham Silberschatz, Henry Korth and S. Sudarshan - Database System Concepts. 7-McGraw-Hill Education (2020).pdf -------------------------------------------------------------------------------- /Books/Advanced Applied Deep Learning-Umberto Michelucci.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIS-Team/Data-Science-Roadmap-2025/60988015fd43776ded553d4732e9830214cee72d/Books/Advanced Applied Deep Learning-Umberto Michelucci.pdf -------------------------------------------------------------------------------- /Books/Data_Science_from_Scratch_First_Principles_with_Python_by_Joel_Grus.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIS-Team/Data-Science-Roadmap-2025/60988015fd43776ded553d4732e9830214cee72d/Books/Data_Science_from_Scratch_First_Principles_with_Python_by_Joel_Grus.pdf -------------------------------------------------------------------------------- /Books/Deep Learning-Ian Goodfellow.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIS-Team/Data-Science-Roadmap-2025/60988015fd43776ded553d4732e9830214cee72d/Books/Deep Learning-Ian Goodfellow.pdf -------------------------------------------------------------------------------- /Books/Essential-Math-for-Data-Science-by-Thomas-Nield_bibis.ir.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIS-Team/Data-Science-Roadmap-2025/60988015fd43776ded553d4732e9830214cee72d/Books/Essential-Math-for-Data-Science-by-Thomas-Nield_bibis.ir.pdf -------------------------------------------------------------------------------- /Books/Hadoop-The.Definitive.Guide_4.edition_a_Tom.White_April-2015-1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIS-Team/Data-Science-Roadmap-2025/60988015fd43776ded553d4732e9830214cee72d/Books/Hadoop-The.Definitive.Guide_4.edition_a_Tom.White_April-2015-1.pdf -------------------------------------------------------------------------------- /Books/Introduction to Statistical Learning with Applications in R.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIS-Team/Data-Science-Roadmap-2025/60988015fd43776ded553d4732e9830214cee72d/Books/Introduction to Statistical Learning with Applications in R.pdf -------------------------------------------------------------------------------- /Books/Masato_Hagiwara_Real_World_Natural_Language_Processing_Practical.pdf: -------------------------------------------------------------------------------- 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Analysis.pdf -------------------------------------------------------------------------------- /Books/The-Data-Warehouse-Toolkit-3rd-Edition.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIS-Team/Data-Science-Roadmap-2025/60988015fd43776ded553d4732e9830214cee72d/Books/The-Data-Warehouse-Toolkit-3rd-Edition.pdf -------------------------------------------------------------------------------- /Books/Transfer Learning - Qiang Yang.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIS-Team/Data-Science-Roadmap-2025/60988015fd43776ded553d4732e9830214cee72d/Books/Transfer Learning - Qiang Yang.pdf -------------------------------------------------------------------------------- /Content - Guide!!.txt: -------------------------------------------------------------------------------- 1 | A complete guide to (Data Science, ML and Data Analysis) in 2022 without any background in the field. 2 | 3 | Free Self-Learning Roadmap from A to Z for anyone interested in breaking into this field! 4 | 5 | 📌Roadmap: https://github.com/Moataz-Elmesmary/Data-Science-Roadmap 6 | 7 | It provides : 8 | - Best resources (En/Ar) 9 | - Best Books (All Free!) 10 | - Most recommended Lectures/Courses by well-known seniors/professors. 11 | - Collection of best Cheat Sheets. 12 | - Guidance to the most used common tools. 13 | - Best collection of Data Science Interview Questions. 14 | - CV/Resume Guide - Tips - Templates. 15 | - Competitions full guidance. 16 | - Data Analysis Recommendations. 17 | - Data Engineering Recommendations. 18 | - Data about Ai/ML/Data Companies in Egypt. 19 | 20 | Let me know if I missed any good resources! 21 | This guide is regularly updated. 22 | 23 | 24 | 📌Roadmap Explanation: 25 | https://www.youtube.com/watch?v=HbIPJuvzRLk (Video) 26 | 27 | 28 | 29 | If you Don't know What`s Data Science or Projects Life Cycle (starting from Business Understanding to Deployment) or Which Programming Language you should go for or Job Descriptions or the required Soft & Hard Skills needed for this field or Data Science Applications or the Most Common Mistakes, then 30 | 📌This Video: https://youtu.be/5zRvq7CG6Zw is for you. 31 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Moataz Elmesmary 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 | 2 |

     DATA SCIENCE ROADMAP :pirate_flag: 2025

3 | 4 | ### Data Science Roadmap for anyone interested in how to break into the field! 5 | 6 |

This repository is intended to provide a free Self-Learning Roadmap to learn the field of Data Science. I provide some of the best free resources.


7 | 8 |   [Our Previous Roadmap](https://github.com/Seif-Mohamed1/DataScience-Squad) ♥️
9 |    :warning: *Before we start,* :warning: 10 | #### If you Dont know What`s Data Science or Projects Life Cycle (starting from Business Understanding to Deployment) or Which Programming Language you should go for or Job Descriptions or the required Soft & Hard Skills needed for this field or Data Science Applications or the Most Common Mistakes, then
11 | ### :pushpin:**[This Video](https://www.youtube.com/watch?v=5zRvq7CG6Zw&t=5s) is for you (Highly Recommended :heavy_check_mark:)** 12 | 13 | 14 |

Data Science vs Data Analytics vs Data Engineering - What's the Difference?


15 | 16 | ![aaa](https://user-images.githubusercontent.com/92026137/163718013-30b3af7b-5b3c-4a08-a54e-1c81b994a5f6.png) 17 | 18 |

These terms are wrongly used interchangeably among people. There are distinct differences:

19 | 20 | | :small_orange_diamond: **Data Science** | :small_orange_diamond: **Data Analytics** | :small_orange_diamond: **Data Engineering** | 21 | | ------------- | --------------------- | -------------------- | 22 | |
Is a multidisciplinary field that focuses on looking at raw and structured data sets and providing potential actionable insights. The field of Data Science looks at ensuring we are asking the right questions as opposed to finding exact answers. Data Scientist require skillsets that are centered on Computer Science, Mathematics, and Statistics. Data Scientist use several unique techniques to analyze data such as machine learning, trends, linear regressions, and predictive modeling. The tools Data Scientist use to apply these techniques include Python and R.
|
Focuses on looking at existing data sets and creating solutions to capture data, process data, and finally organize data to draw actionable insights. This field looks at finding general process, business, and engineering improvements we can make based on questions we don't know the answers to. Data Analytics require skillsets that are centered on Statistics, Mathematics, and high level understanding of Computer Science. It involves data cleaning, data visualization, and simple modeling. Common Data Analytic tools used include Microsoft Power Bi, Tableau, and SQL.
|
Focuses on creating the correct infrastructure and tools required to support the business. Data Engineers look at what are the optimal ways to store and extract data and involves writing scripts and building data warehouses. Data Engineering require skillsets that are centered on Software Engineering, Computer Science and high level Data Science. The tools Data Engineers utilize are mainly Python, Java, Scala, Hadoop, and Spark.
| 23 | 24 | # Prepare your workspace 25 |

Tip :one: : Pick one and stick to it. (:file_folder:Click)

26 | 27 |
28 | 29 | [Anaconda](https://www.anaconda.com/products/distribution): It’s a tool kit that fulfills all your necessities in writing and running code. From Powershell prompt to Jupyter Notebook and PyCharm, even R Studio (if interested to try R) 30 | 31 | ![a](https://user-images.githubusercontent.com/92026137/163717819-4689c927-6022-47da-b204-169693bfa397.png) 32 | 33 | 34 | [Atom](https://atom.io/packages/ide-python): A more advanced Python interface, highly recommended by experts.
35 | [Google Colab](https://colab.research.google.com/notebooks/intro.ipynb): It’s like a Jupyter Notebook but in the cloud. You don’t need to install anything locally. All the important libraries are already installed. For example NumPy, Pandas, Matplotlib, and Sci-kit Learn
36 | [PyCharm](https://www.jetbrains.com/): PyCharm is another excellent IDE that enables you to integrate with libraries such as NumPy and Matplotlib, allowing you to work with array viewers and interactive plots.
37 | [Thonny](https://thonny.org/): Thonny is an IDE for teaching and learning programming. Thonny is equipped with a debugger, and supports code completion, and highlights syntax errors.
38 | Most learning platforms have integrated code exercises where you don’t need to install anything locally. But to learn it right, you should have an IDE installed on your local machine. Suggestions will be a marketplace with many options and few improvements from one platform to another. 39 | 40 | ### Tip :two: : Focus on one course at least. 41 | ### Tip :three: : Don’t chase certifications. 42 | ### Tip :four: : Don’t rush for ML without having a good background in programming & maths. 43 | 44 | 45 | ## This track is divided into 3 phases :arrow_down: : 46 | 47 | ####   1. Beginner: you get a basic understanding of data analysis, tools and techniques. 48 | ####   2. Intermediate: dive deeper in more complex topics of ML, Math and data engineering. 49 | ####   3. Advanced: where we learn more advanced Math, DL and Deployment. 50 | 51 | :bell: For Data Camp courses, github student pack gives 3 free months. Google how to get it.
if you already used it, do not hesitate to contact us to have an account with free access. :hibiscus: 52 | 53 | 54 | ## Legend 55 | * :video_camera: Video Content 56 | * :closed_book: Online Article Content / Book 57 | ### 💡 Roadmap Explanation ▶️ [Youtube Video](https://youtu.be/HbIPJuvzRLk) :movie_camera: 58 | 59 | *** 60 | 61 | ## 🔰 Beginner 🔰 62 | 63 | [Algorithms Book](https://github.com/cjbt/Free-Algorithm-Books/blob/master/book/Grokking%20Algorithms%20-%20An%20illustrated%20guide%20for%20programmers%20and%20other%20curious%20people.pdf) Every piece of code could be called an algorithm, but this book covers the 64 | more interesting bits.
65 | [Specializations (data structures-algorithms)](https://www.coursera.org/specializations/data-structures-algorithms) 66 | 67 | **1. Descriptive Statistics** Statistics
68 |    📹 [Introduction to Statistics - DataCamp](https://app.datacamp.com/learn/courses/introduction-to-statistics)
69 |    📹 [Intro to Descriptive Statistics - YouTube](https://www.youtube.com/watch?v=ApEV6IupW7o&list=PLAwxTw4SYaPn22DmaF6x8JtG4TeWOJk_1&index=1) old Udacity Course
70 |    📹 [Statistics Fundamentals - StatQuest - YouTube](https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9)
71 |    📹 [Introduction to Statistics - YouTube](https://www.youtube.com/playlist?list=PL0KQuRyPJoe6KjlUM6iNYgt8d0DwI-IGR)
72 |    📕 [Online statistics education](http://onlinestatbook.com/Online_Statistics_Education.pdf)
73 |    📹 Arabic Courses [1](https://www.youtube.com/watch?v=_Lg1QtwZHvk&list=PLO3fADoO5fwNTr4Zjmz-cacmMh1S0o4Ml&index=1) - [2](https://www.youtube.com/watch?v=d5jh5mmwcKI&list=PLY99ZSsxRyJiu6kb4WRRpeEFqK1pAr-EO)
74 |    📹 [Intro to Inferential Statistics](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)++
75 |    📕 [Practical Statistics for Data Scientists](https://github.com/Moataz-Elmesmary/Data-Science-Roadmap/blob/main/Books/Practical%20Statistics%20for%20Data%20Scientists.pdf)
76 | 77 | **2. Probability**
78 |    📹 [Khan Academy](https://www.khanacademy.org/math/statistics-probability/probability-library)
79 |    📹 [Probability Bootcamp by Dr.Steve - Oct 2024- YouTube](https://www.youtube.com/playlist?list=PLMrJAkhIeNNR3sNYvfgiKgcStwuPSts9V)
80 |    📹 [Arabic Course](https://www.youtube.com/playlist?list=PL158D091D26F47358)
81 |    📹 [Probability and Statistics for AI and DS - Arabic (Dr.Hatem Elattar)](https://www.youtube.com/playlist?list=PLJM7jJIw2GC2Ihr__bRSeMxzsiFMZEsx7)
82 |    📕 [Introduction to Probability](https://drive.google.com/file/d/15Y0oFNHQRls1qvQNvO3DFLJVhIZvUjTD/view?usp=sharing)
83 | 84 | **3. Programming Languages**
85 | 86 |  🔹*R* - *good tool for visualization and statistical analysis.*
87 |    📹 [Introduction to R (DataCamp)](https://www.datacamp.com/courses/free-introduction-to-r)
88 |    📹 [Data Science Specialization - Coursera](https://www.coursera.org/specializations/jhu-data-science)
89 |    📕 [An Introduction to R](https://cran.r-project.org/doc/manuals/R-intro.pdf)
90 |    📕 [R for Data Science](https://r4ds.had.co.nz/)
91 | 92 |  🔹*Python*:100:
93 |    📹 [Introduction to Python Programming](https://www.udacity.com/course/introduction-to-python--ud1110)
94 |    📹 [OOP](https://learn.datacamp.com/courses/object-oriented-programming-in-python)
95 |    📹 Arabic - [Hassouna](https://www.youtube.com/watch?v=MxYLqE3Ils8&list=PLHIfW1KZRIfnM9y0sQRwjVz2-IwvnEJep) | [Elzero](https://www.youtube.com/watch?v=mvZHDpCHphk&list=PLDoPjvoNmBAyE_gei5d18qkfIe-Z8mocs)
96 |    📹 [Python Full Course - FreeCodeCamp on YouTube](https://www.youtube.com/watch?v=rfscVS0vtbw)
97 |    📕 [Intro to Python for CS and Data Science](https://drive.google.com/file/d/1rXkYFjw1iKbXCra_B4Ykm0AMRgo6v93w/view?fbclid=IwAR2lg9omGaAsG3g1ZhHQHja8_uxkZ7QddnOUSxfoceRXShU1V_bl4V63xCQ)
98 |    [more in OOP](https://www.futurelearn.com/courses/object-oriented-principles)
99 | **4. Pandas**
100 |    📹 [Corey Schafer-YouTube](https://www.youtube.com/watch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS)
101 |    📕 [Kaggle](https://www.kaggle.com/learn/pandas)
102 |    📕 [Docs](https://pandas.pydata.org/pandas-docs/version/0.15/tutorials.html)
103 |    📹 [Data School-YouTube](https://www.youtube.com/watch?v=yzIMircGU5I&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=1)
104 |    📹 [Arabic Course](https://www.youtube.com/watch?v=3ISW655DemU&list=PLvLvlVqNQGHCb2_ygmr1DQOMOv0yXp84F)
105 |    📹 PandasAI🐼[1](https://www.youtube.com/watch?v=BtmMNZLxbuI) - [2](https://www.youtube.com/watch?v=5w6eZaoDVVk) *Enhances the capabilities of Pandas by integrating Generative AI functionalities into it.*
106 | **5. Numpy**
107 |    📕 [Kaggle](https://www.kaggle.com/legendadnan/numpy-tutorial-for-beginners-data-science)  numpy
108 |    📹 [NumPy Tutorial by Keith Galli - YouTube](https://www.youtube.com/watch?v=5-5CrLmf2vk&list=PLIA_seGogbkGDYq-dnVCsELEIq_7HK7Ca)
109 |    📹 [Arabic Course - Elzero](https://www.youtube.com/playlist?list=PLUgz8T_NoatsJCH-DmieQhqhSL2WBvlm-)
110 |    📕 [Tutorial](http://cs231n.github.io/python-numpy-tutorial/)
111 |    📕 [Docs](https://numpy.org/doc/1.18/user/quickstart.html)
112 | **6. Scipy**
113 |    📕 [Tutorial](https://cs231n.github.io/python-numpy-tutorial/#scipy)
114 |    📕 [Docs](https://docs.scipy.org/doc/scipy/reference/tutorial/general.html)
115 | **7. Data Cleaning**: One of the **MOST** important skills that you need to master to become a good data scientist, you need to practice on many datasets to master it.
116 |    [Read this](https://towardsdatascience.com/the-ultimate-guide-to-data-cleaning-3969843991d4)
117 |    📹 [Course 1](https://www.datacamp.com/courses/cleaning-data-in-python)
118 |    📕 [Notebook1](https://www.kaggle.com/bandiatindra/telecom-churn-prediction)
119 |    📕 [Notebook2](https://drive.google.com/drive/folders/1OQAEQ8rC4j6oBP7AyDU4bKpPr8sSStJI?fbclid=IwAR2dSrbyoZLM-Wm57yEYy8L8PmpPV9hqXdkNf-pURJC5C5xCz7UJB4YpJ7M)
120 |    📕 [Notebook3](https://www.kaggle.com/ashishg21/data-cleaning-and-some-analysis-shoe-prices)
121 |    📕 [Kaggle Data cleaning](https://www.kaggle.com/learn/data-cleaning)
122 | **8. Data Visualization** :bar_chart:
123 |    📹 [Introduction to Data Visualization with Matplotlib](https://app.datacamp.com/learn/courses/introduction-to-data-visualization-with-matplotlib?fbclid=IwAR1OrJSdZ2LVD_c1o3d-_1I7Nhq8OZ3pzTu4010E_XWEmMc0KYsTosz8CIU) or
124 |    📹 [ Corey Schafer - Playlist on Youtube](https://www.youtube.com/watch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_) or
125 |    📹 [sentdex - Playlist on YouTube](https://www.youtube.com/watch?v=q7Bo_J8x_dw&list=PLQVvvaa0QuDfefDfXb9Yf0la1fPDKluPF)
126 |    📕 [Kaggle to Data Visualization with Seaborn](https://www.kaggle.com/learn/data-visualization)
127 |    📹 [Playlist-Youtube](https://www.youtube.com/watch?v=z7ZINBk8EUk&list=PL998lXKj66MpNd0_XkEXwzTGPxY2jYM2d)
128 |    📹 [Course1: Intro to Data Visualization with Seaborn](https://learn.datacamp.com/courses/introduction-to-data-visualization-with-seaborn)
129 |    📹 [Course2: Intermediate Data Visualization with Seaborn 130 | ](https://learn.datacamp.com/courses/intermediate-data-visualization-with-seaborn)
131 |    📹 [Course3: Understanding and Visualizing with Python](https://www.coursera.org/learn/understanding-visualization-data)
132 | 133 | **9. EDA** 134 | Note: it's already mentioned in the above probability course
135 |    📹 [DataCamp-EDA in Python](https://learn.datacamp.com/courses/exploratory-data-analysis-in-python)
136 |    📹 [IBM-EDA for Machine Learning](https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning)
137 | 138 | 139 | 140 | **10. Dashboards**
141 | 142 |  *Power BI*
143 |    📹 [Power BI - YouTube (Alex)](https://youtube.com/playlist?list=PLUaB-1hjhk8HqnmK0gQhfmIdCbxwoAoys&si=pR4VSrR1P2O-AaBJ)
144 |    📹 [Power BI training](https://powerbi.microsoft.com/en-us/learning/)
145 |    📹 [Arabic - YouTube (Zanoon)](https://www.youtube.com/watch?v=P_Nr0FMyn9w&list=PL69umUTzySPGWMxnmhX9SV5PIEbdnHv63&index=1)
146 |    📹 [Arabic - YouTube](https://www.youtube.com/watch?v=ykvAWKML9Gk&list=PLof3yw6ZFPFhV75Ptf-5Q88bgUtLOBvOw)
147 |    📹 [Guy in a Cube - YouTube](https://www.youtube.com/@GuyInACube/featured)
148 |  *Tableau* tableau
149 |    📹 [Data With Baraa - YouTube](https://www.youtube.com/watch?v=_TT1D3tH1_c&list=PLNcg_FV9n7qZJqrKcUUCWCWPYCrlcVm9v)
150 |    📕 [Tutorial](https://www.datacamp.com/community/tutorials/data-visualisation-tableau)
151 |    📹 [Tableau Training](https://www.tableau.com/learn/training/20201)
152 |    📹 [Course - DataCamp](https://learn.datacamp.com/courses/introduction-to-tableau)
153 |    📹 [Simplilearn - YouTube](https://www.youtube.com/watch?v=SSq5NwsUNGI&list=PLEiEAq2VkUUJEvrsey26P-Bj4Vk6BLBVC)
154 | 155 | 156 | **11. SQL and DB**
157 |    📹 SQL for Data Analysis ([Udacity](https://www.udacity.com/course/sql-for-data-analysis--ud198)-notes[*l📋l*](https://github.com/julianjohannesen/Udacity-SQL-Notes/tree/main) or [simplilearn](https://www.simplilearn.com/free-online-course-to-learn-sql-basics-skillup))
158 |    📹 [Intro to SQL](https://learn.datacamp.com/courses/introduction-to-sql) **or** [IBM (SQL for Data Science)](https://www.coursera.org/learn/sql-data-science)
159 |    📹 [Intro to Relational Databases in SQL](https://learn.datacamp.com/courses/introduction-to-relational-databases-in-sql)
160 |    📹 Arabic Course ([Theoritical](https://www.youtube.com/playlist?list=PL37D52B7714788190) - [Practical](https://www.youtube.com/playlist?list=PL1DUmTEdeA6J6oDLTveTt4Z7E5qEfFluE)) Eldesouki
161 |    📹 Arabic - [ITI by Eng.Ramy](https://www.youtube.com/playlist?list=PLSGEGD0dbMKrvd5ppnyFLm7q3xEH97T-t) *Advanced* - *([Labs Answers + Notes + Full Materials](https://github.com/Moataz-Elmesmary/ITI-SQL-Labs))*
162 |    📹 Arabic - [SQL for Data Analysis](https://www.youtube.com/watch?v=kb-_GbpH3sQ&t=38s) by Ahmed Sami
163 |    📹 [Data With Baraa - YouTube](https://www.youtube.com/@DataWithBaraa/playlists) - [[Materials]](https://datawithbaraa.substack.com/p/access-to-course-materials)
164 |    📹 [365 Data Science - SQL](https://mega.nz/folder/wswGEIhb#tsqUggTZyfy5HyRWUkV9sg/folder/R1AxXCxB)
165 |    📹 [CMU Intro to DB - Fall 2022](https://www.youtube.com/playlist?list=PLSE8ODhjZXjaKScG3l0nuOiDTTqpfnWFf) - *<[Schedule📅](https://15445.courses.cs.cmu.edu/fall2022/schedule.html)>* - [Book📕](https://github.com/Moataz-Elmesmary/Data-Science-Roadmap/blob/main/Books/Abraham%20Silberschatz%2C%20Henry%20Korth%20and%20S.%20Sudarshan%20-%20Database%20System%20Concepts.%207-McGraw-Hill%20Education%20(2020).pdf)
166 |    📕 [SQL for Data Analysis](https://github.com/Moataz-Elmesmary/Data-Science-Roadmap/blob/main/Books/SQL%20for%20Data%20Analysis.pdf)
167 |    📝 Practice [InterviewMaster](https://www.interviewmaster.ai/) & [HackerRank](https://www.hackerrank.com/domains/sql) & [LeetCode](https://leetcode.com/studyplan/top-sql-50/) & [DataLemur](https://datalemur.com/) 168 | 169 | **12. DWH** : *A system used for reporting - A core component of business intelligence.*
170 |      *Mostly used by Data Engineers.*
171 |    📕 [The Data Warehouse Toolkit](https://github.com/Moataz-Elmesmary/Data-Science-Roadmap/blob/main/Books/The-Data-Warehouse-Toolkit-3rd-Edition.pdf)
172 |    📹 [Data Warehousing Tutorial Videos](https://www.youtube.com/playlist?list=PL9ooVrP1hQOEDSc5QEbI8WYVV_EbWKJwX)
173 |    📹 [Garage Education](https://www.youtube.com/playlist?list=PLxNoJq6k39G_Ffv8Na1oRbob0sVHfFc_T) (Ar)
174 |    📹 [Implementing Data Warehouse in Arabic](https://www.youtube.com/playlist?list=PL1565idytjOTwGN63vZK7lNK6pVXpGo3s) (Ar)
175 |    📹 [More in Arabic?](https://www.youtube.com/playlist?list=PLx5yn1EeCC_6ampJnoF2hHnHMj_-EGkU4) (Ar)
176 |    📹 [Data Warehouse - University of Colorado](https://www.coursera.org/learn/dwdesign)
177 |    📹 **[SSIS]** [SQL Server Integration Services](https://www.youtube.com/playlist?list=PLgOQg5m1pmp84jmXHGNWWYuU3m4bNCmfs) (Ar)
178 |    📹 [Project - Building Sales Data Mart Using SSIS](https://www.youtube.com/playlist?list=PLcAbhg_RWLaLUaYpAAvOLu2hlyVgZlRjb) (Ar)
179 |    📹 [Project - Building DWH Step by Step](https://www.youtube.com/playlist?list=PLcAbhg_RWLaLUaYpAAvOLu2hlyVgZlRjb)
180 |    📹 [Project - Create DWH Fact and Dimensions](https://www.youtube.com/watch?v=8TSUoolAk2I) (Ar)
181 |    📹 [Implement SCD in SSIS](https://www.youtube.com/watch?v=7uj463csru0) *Continue the playlist*
182 |    📹 [CDC in SSIS tutorial](https://www.youtube.com/watch?v=QVF1JGFFt8w)
183 | 184 | **13. Python Regular Expression**
185 |    📕 [Tutorial](https://www.datacamp.com/community/tutorials/python-regular-expression-tutorial)
186 |    📹 [Regular Expressions by Corey - YouTube](https://www.youtube.com/playlist?list=PLDoPjvoNmBAyE_gei5d18qkfIe-Z8mocs)
187 |    📹 [Arabic Course - Elzero](https://www.youtube.com/playlist?list=PLDoPjvoNmBAyE_gei5d18qkfIe-Z8mocs) *starting from the 95th video.*
188 | 189 | **14. Time Series Analysis**
190 |    📹 [Track - DataCamp](https://learn.datacamp.com/skill-tracks/time-series-with-python)
191 |    📹 [Course - Coursera](https://www.coursera.org/learn/practical-time-series-analysis)
192 |    📕 [Book](https://www.oreilly.com/library/view/practical-time-series/9781492041641/?fbclid=IwAR20cq7hAdWf6voOd61u-pNzZCHvB0rZhT_BUoGTAXxPBhhi82p8BhxLEsI)
193 |    📕 [fbprohet](https://facebook.github.io/prophet/docs/quick_start.html)
194 |    📹 Arabic Source [Video1](https://www.youtube.com/watch?v=TvhaHPq6xLU&list=TLPQMjYwNzIwMjEPGXX6392WJA&index=1) & [Video2](https://www.youtube.com/watch?v=mipF7mRVpk0&list=TLPQMjYwNzIwMjEPGXX6392WJA&index=2)
195 | 196 |

At The end of the Beginner phase apply what you've learned on a project.

197 | 198 | *** 199 | 200 | 201 | ## 🔰 Intermediate 🔰 202 | 203 | **1. Math for ML**: consists of Linear Algebra, Calculus and PCA.
204 | 📹 [Mathematics for Machine Learning and Data Science - Andrew Ng](https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science?irclickid=zzy33K1O0xyNUAmxqWUjDwedUkAUtSWUJXKyTY0&irgwc=1&utm_medium=partners&utm_source=impact&utm_campaign=3117765&utm_content=b2c#courses)
205 | 📹 [Specialization](https://www.coursera.org/specializations/mathematics-machine-learning)
206 | 📹 [Mathematics for Machine Learning - Most of the needed basics](https://www.youtube.com/watch?v=vLJcduC4lBM&list=PLcQCwsZDEzFmlSc6levE3UV9rZ8yY-D_7)

207 | :small_blue_diamond:Linear Algebra
208 |    📹 [Khan Academy - Linear Algebra](https://www.khanacademy.org/math/linear-algebra)
209 |    📹 [Mathematics for Machine Learning: Linear Algebra](https://www.coursera.org/learn/linear-algebra-machine-learning)
210 |    📹 [3Blue1Brown - Essence of Linear Algebra](https://www.3blue1brown.com/topics/linear-algebra)
211 | :small_blue_diamond:Calculus
212 |    📹 [Multivariate Calculus - Coursera](https://www.coursera.org/learn/multivariate-calculus-machine-learning?fbclid=IwAR243aoz0jxs4iUn539pnjSQliXtr7Y5QAsvgeRTietZT_tkyoRU3b6Sq1o)
213 |    📹 [Essence of calculus - Youtube](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr)
214 | :small_blue_diamond:PCA
215 |    📹 [PCA - Coursera](https://www.coursera.org/learn/pca-machine-learning)
216 | 217 | 218 | **2. Machine Learning**
219 |    📹 [Coursera - Old Course by Andrew Ng (Octave/Matlab)](https://www.coursera.org/learn/machine-learning)
220 |    📹 [Coursera Andrew`s new ML Specialization (Python)](https://www.coursera.org/specializations/machine-learning-introduction?_hsenc=p2ANqtz-_R9x3Nm07uCw6YAw9VpCpdjRdfaUFyxdOcvgDljRt7j_NXiahN1plnI_Ob9jn0jSNipuE_Y08llrfPSt_1P7EBvj4LuImpBTKG3bsR6Z9bzjzBoRY&_hsmi=216611333&action=enroll&utm_campaign=mls-launch-2022&utm_content=216613012&utm_medium=email&utm_source=hs_email#courses)
221 |    📹 [Machine Learning - StatQuest - YouTube](https://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF)
222 |    📹 [Machine Learning Stanford Full Course on YouTube by Andrew](https://www.youtube.com/watch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN)
223 |    📹 [CS480/680 Intro to Machine Learning - Spring 2019 - University of Waterloo](https://www.youtube.com/playlist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k)
224 |    📹 [SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)](https://www.youtube.com/playlist?list=PL4upCU5bnihwCX93Gv6AQnKmVMwx4AZoT)
225 |    📹 [Machine Learning for Engineers 2022](https://apmonitor.com/pds/) / ([YouTube](https://www.youtube.com/watch?v=Gh5rbBLh4JY&list=PLLBUgWXdTBDg1K1bu60lHypSzSP-WSBmx))
226 |    📹 [Introduction to Machine Learning Course - Udacity](https://www.udacity.com/course/intro-to-machine-learning--ud120)
227 |    📹 [Hesham Asem - Arabic content](https://www.youtube.com/c/HeshamAsem/playlists)
228 |    📹 [IBM ML with Python](https://www.coursera.org/learn/machine-learning-with-python)
229 |    📹 [Machine Learning From Scratch - YouTube (Python Engineer)](https://www.youtube.com/watch?v=ngLyX54e1LU&list=PLqnslRFeH2Upcrywf-u2etjdxxkL8nl7E)
230 |    📕 Hands On ML ([1st](https://drive.google.com/file/d/1uro1p6SlYolSkF0fbFKau0pOQ9ENZqny/view?usp=sharing) & [2nd](https://drive.google.com/file/d/1rS95FTNfiVG4WjGnPjd73GqrmEKey4N1/view?usp=sharing) & [3rd](https://drive.google.com/file/d/11VeqPJw8s9SC9Ru7IVeQhiTyV_9TliOE/view?usp=sharing)) Editions | Code: [![View on Github](https://img.shields.io/badge/Examples-Notebooks-orange?logo=Github)](https://github.com/ageron/handson-ml?fbclid=IwAR3s31KlwkLKyrEwuEd4UMOcvHN1Q9Z2LLGzPg5vP4UKSwjriHxU0uO405c)
231 |    📹 [ML Algorithms in Practice](https://www.coursera.org/specializations/machine-learning-algorithms-real-world?utm_medium=email&utm_source=marketing&utm_campaign=A39CcMUuEempyReieZALEQ)
232 |    📹 [ML scientist](https://learn.datacamp.com/career-tracks/machine-learning-scientist-with-python?version=1)
233 |    📹 [Project](https://www.coursera.org/learn/applied-data-science-capstone)
234 | 235 | **3. Web Scraping/APIs**
236 |    📹 [course](https://learn.datacamp.com/courses/web-scraping-with-python)
237 |    📕 [intro2](https://www.dataquest.io/blog/web-scraping-tutorial-python/)
238 |    📕 [Tutorial](https://realpython.com/beautiful-soup-web-scraper-python/)
239 |    📕 [Book for both topics](https://b-ok.africa/book/3515980/5d50aa)
240 | APIs
241 |    📕 [Tutorial](https://www.dataquest.io/blog/python-api-tutorial/)
242 |    📕 [Article](https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-pull-data-from-an-api-using-python-requests-edcc8d6441b1)
243 |    📕 [Tutorial](https://rapidapi.com/blog/how-to-use-an-api-with-python/)
244 | **4. Stats.**
245 |    📕 [This stats - Book](https://greenteapress.com/thinkstats/thinkstats.pdf)
246 |    📕 [Think Bayes - Book](https://www.greenteapress.com/thinkbayes/thinkbayes.pdf)
247 | **5. Advanced SQL**
248 |    📹 [Joining Data in SQL - DataCamp](https://learn.datacamp.com/courses/joining-data-in-postgresql)
249 |    📹 [Intermediate SQL - DataCamp](https://learn.datacamp.com/courses/joining-data-in-postgresql)
250 |    📹 [More advanced SQL](https://www.coursera.org/lecture/data-driven-astronomy/more-advanced-sql-GDmo5)
251 | 252 | **7. Feature Engineering**
253 |    📕 [Tutorial](https://www.kaggle.com/learn/feature-engineering)
254 |    📕 [Article](https://www.medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Ffeature-engineering-for-machine-learning-3a5e293a5114)
255 |    📕 [Book](https://drive.google.com/file/d/1BkJYO0tqMYptTWUDQ7X0vd2aygohHRm8/view?usp=sharing)
256 | **8. interpret Shapley-based explanations of ML models.**
257 |    📕 [SHAP](https://shap.readthedocs.io/en/latest/)
258 |    📕 [Kaggle ML explainability](https://www.kaggle.com/learn/machine-learning-explainability)
259 |

After finishing this level apply to 2 or 3 good sized projects.

260 | 261 | Read this book, please :open_book: [Introduction to Statistical Learning with Applications in R](https://github.com/Moataz-Elmesmary/Data-Science-Roadmap/blob/main/Books/Introduction%20to%20Statistical%20Learning%20with%20Applications%20in%20R.pdf) بقولك اقرأه
262 | *** 263 | ## 🔰 Advanced 🔰 264 | 265 | **1. Deep Learning**
266 |    📹 [Deep Learning Fundamentals](https://www.youtube.com/playlist?list=PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU)
267 |    📹 [Introduction to 268 | Deep Learning - MIT](http://introtodeeplearning.com/?fbclid=IwAR35rIygYlCn84DV7mlHvdvs4sMUm2D6RLYVwFpp2nT2t1Zj1GGy3QAWQvQ)
269 |    📹 [Specialization](https://www.coursera.org/specializations/deep-learning)
270 |    📕 [Dive into Deep Learning (En)](https://d2l.ai/d2l-en.pdf?fbclid=IwAR0sVdA8VFYpNZCpYZHgo_kl_HYrjcjDfjEka26D8xRWAhbhh6mmSNIXg3U) | (Ar) version :arrow_right:[Part1](https://drive.google.com/file/d/1SrmT_r8dNK42IqyS0gwXtbLCZbk5G8eu/view?fbclid=IwAR1Xcf8PNKkPJMg0uHRE1QyIW4_BMxISIdoB8pPaepw38njhaIf04MYM218) & [Part2](https://drive.google.com/file/d/1UqEu0amRfAvJD0L1HosIn3UJi0FkNemU/view?fbclid=IwAR1og8pkWr1gT3jdUwqikCZVrOCpyrm0x6ZRL63Kitwhki35pazHdo_ScJI)
271 |    📹 [Deep Learning UC Berkely](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW)
272 |    📕 [github of Dive into DL](https://github.com/d2l-ai/d2l-en?fbclid=IwAR0QN35b-NHHWq_zKISA1cbI063aRqqoKqR_0e3cpnT5h58GkcNbCIJs3iw)
273 |    📹 [Stanford Lecture - Convolutional Neural Networks for Visual Recognition](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)
274 |    📹 [University of Waterloo - ML / DL](https://www.youtube.com/playlist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k)
275 |    📕 [Deep Learning for coders with fastai & PyTorch](https://dl.ebooksworld.ir/books/Deep.Learning.for.Coders.with.fastai.and.PyTorch.Howard.Gugger.OReilly.9781492045526.EBooksWorld.ir.pdf)
276 | 277 | 278 | **2. Tensorflow**
279 |    📹 [Specialization](https://www.coursera.org/specializations/tensorflow-in-practice)
280 |    📹 [Youtube](https://www.youtube.com/playlist?list=PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL)
281 |     [fast.ai's Deep Learning Courses](https://www.fast.ai/)
282 | ###### TensorFlow beats PyTorch in visualization capabilities and deploying trained models. Go for PyTorch if you want flexibility, debugging capabilities, and short training duration. 283 | 284 | **3. PyTorch**
285 |    📹 [PyTorch (UC Berkeley - Youtube) - Lec3 (The 5 parts)](https://m.youtube.com/watch?v=AOypIa_8RXg&list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH&index=11)
286 |    📹 [PyTorch - Dr. Data Science - Youtube](https://www.youtube.com/watch?v=vVQs4h6HUvA&list=PLLeO8f6PhlKb_FAC7qxOBtxT9-8EPDAqk)
287 |    📹 [Pytorch Tutorial - Aladdin - Youtube](https://www.youtube.com/watch?v=2S1dgHpqCdk&list=PLhhyoLH6IjfxeoooqP9rhU3HJIAVAJ3Vz)
288 |    📹 [PyTorch Course (2022) - Youtube](https://www.youtube.com/watch?v=v43SlgBcZ5Y&list=PLkdGijFCNuVk9fO1IMfdV1Igob0FUHhkB)
289 |    📕 [Deep Learning With Pytorch](https://drive.google.com/file/d/1-KG_ufeg7zw2iLgG5RrJSFpyonLwulgF/view?usp=sharing)
290 |    📕 [Machine Learning with PyTorch and Scikit-Learn -2022](https://dl.ebooksworld.ir/books/Machine.Learning.with.PyTorch.and.Scikit-Learn.Sebastian.Raschka.Packt.9781801819312.EBooksWorld.ir.pdf)
291 | 292 | **4. Advanced Data Science**
293 |    📹 [Advanced Data Science with IBM Specialization](https://www.coursera.org/specializations/advanced-data-science-ibm) *Includes Apache Spark*
294 |  ☠️*Advanced ML Topics🧠 | Lecs (YouTube)*
295 |    📹 [Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022](https://www.youtube.com/playlist?list=PLoROMvodv4rNjRoawgt72BBNwL2V7doGI) - [Materials](https://cs330.stanford.edu/)
296 |    📹 [18.409 Algorithmic Aspects of Machine Learning Spring 2015 - MIT](https://www.youtube.com/playlist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx)
297 |  ☠️*ML based Computer Vision | Lecs (YouTube)*
298 |    📹 [CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)](https://www.youtube.com/playlist?list=PLzWRmD0Vi2KVsrCqA4VnztE4t71KnTnP5)
299 |    📹 [NOC:Deep Learning For Visual Computing - IIT Kharagpur](https://nptel.ac.in/courses/108105103)
300 |    📹 [Deep Learning for Computer Vision - Michigan](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r)
301 | 302 | **5. NLP**
303 |    📹 [Specialization - Coursera](https://www.coursera.org/specializations/natural-language-processing)
304 |    📹 [Arabic - Ahmed El Sallab](https://www.youtube.com/playlist?list=PLxmZ0b-n395VxzEUL8-Dy257zSqYZe4yU)
305 |    📹 [Stanford CS224N Lectures - Winter 2021- YouTube](https://www.youtube.com/playlist?app=desktop&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&si=k91y-bepIiPjHMrj&fbclid=IwAR2h6KcYboHCjG9YBIEB08srgYSesqZ5UHXr0ni8yxOqrxNV3-_TGxq0Csg)
306 |    📹 [Stanford XCS224U Lectures - Spring 2021- YouTube](https://www.youtube.com/playlist?app=desktop&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&si=k91y-bepIiPjHMrj&fbclid=IwAR2h6KcYboHCjG9YBIEB08srgYSesqZ5UHXr0ni8yxOqrxNV3-_TGxq0Csg)
307 |    📹 [Introduction to Natural Language Processing in Python](https://www.datacamp.com/courses/natural-language-processing-fundamentals-in-python)
308 |  🔸*LLMS [What`s Large Language Model](https://www.snowflake.com/guides/what-large-language-model-and-what-can-llms-do-data-science)?*
309 |    📹 [Generative AI for Everyone (Andrew Nj) - Coursera](https://www.coursera.org/learn/generative-ai-for-everyone?utm_campaign=genai4e-launch&utm_medium=institutions&utm_source=deeplearning-ai#modules)🆕
310 |    📹 [Generative AI with LLMs](https://www.deeplearning.ai/courses/generative-ai-with-llms/)
311 |    📹 [Stanford CS236: Deep Generative Models I 2023 - YouTube](https://www.deeplearning.ai/courses/generative-ai-with-llms/)
312 |    📹 [Stanford CS25 - Transformers United 2023 - YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM)
313 |    📹 [Recent Advances on Foundation Models - Winter 2024 - University of Waterloo](https://cs.uwaterloo.ca/~wenhuche/teaching/cs886/)
314 |    📹 [Understanding LLMs Foundations and Safety UC Berkeley - Spring 2024 - YouTube](https://www.youtube.com/playlist?list=PLJ66BAXN6D8H_gRQJGjmbnS5qCWoxJNfe)
315 |    📹 [LLM Foundations](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/llm-foundations/)
316 |    📹 How ChatGPTs / Transformers work?[1](https://www.youtube.com/watch?v=bQ5BoolX9Ag) - [2](https://jalammar.github.io/how-gpt3-works-visualizations-animations/) - [3](https://jalammar.github.io/illustrated-transformer/) *overview & Maths behind*
317 |    📹 [Prompt Engineering](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/prompt-engineering/) | ([Ar](https://www.youtube.com/watch?v=A-sNuzZgY8g&list=PLvLvlVqNQGHDNUshQJBWWCIRGgC0PN7VL)) *If you want to get the most out of LLMs*
318 |    📹 [LLMOps](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/llmops/) *A Lec going through the entire LLM pipeline*
319 | 320 | 321 | **6. Inferential Statistics**
322 | 323 |    📹 [Specialization, 2nd & 3rd courses](https://www.coursera.org/specializations/statistics-with-python)
324 |    📹 [course](https://www.coursera.org/learn/statistical-inferences)
325 | **7. Bayesian Statistics**
326 |    📹 [1 - From Concept to Data Analysis](https://www.coursera.org/learn/bayesian-statistics)
327 |    📹 [2 - Techniques and Models](https://www.coursera.org/learn/mcmc-bayesian-statistics)
328 |    📹 [3 - Mixture Models](https://www.coursera.org/learn/mixture-models)
329 | **8. Model Deployment**
330 |    📕 [Flask tutorial](https://towardsdatascience.com/deploying-a-deep-learning-model-using-flask-3ec166ef59fb)
331 |    📹 [TensorFlow: Data and Deployment Specialization](https://www.coursera.org/specializations/tensorflow-data-and-deployment)
332 |    📹 [Deploy Models with TensorFlow Serving and Flask](https://www.coursera.org/projects/deploy-models-tensorflow-serving-flask)
333 |    📹 [How to Deploy a Machine Learning Model to Google Cloud - Daniel Bourke](https://www.youtube.com/watch?v=fw6NMQrYc6w)
334 |    if you`re interested in more deployment methods, search for (_FastAPI - Heroku - chitra_)
335 | 336 | **9. MLOps** : is a combination of Model Deployment, Model Serving, Model Monitoring, and Model Maintenance. 337 |    🔗 [MLOps-zoomcamp](https://github.com/DataTalksClub/mlops-zoomcamp)
338 |    🔗 [MLOps-guide](https://github.com/Nyandwi/machine_learning_complete/blob/main/010_mlops/1_mlops_guide.md)
339 |    📕 [Practical MLOps](https://drive.google.com/file/d/17RhXQ2ix6rFMaas3HI7bnM_GL8lS7u3f/view?usp=sharing)
340 | **10. Probabilistic Graphical Models** 341 |    📹 [Specialization - Coursera](https://www.coursera.org/specializations/probabilistic-graphical-models)
342 |    📹 [Spring 2016, University of Utah - YouTube](https://www.youtube.com/playlist?list=PLbuogVdPnkCpvxdF-Gy3gwaBObx7AnQut)
343 |

344 | 345 | 346 | :star2: Read these books, they will be beneficial to you.
347 |   :open_book: [Bayesian Reasoning and Machine Learning](https://drive.google.com/file/d/18fh0orqSNAaIyhLkVwh9cGuWBywCBbuw/view?usp=sharing)
348 |   :open_book: [The Elements of Statistical Learning](https://drive.google.com/file/d/1ePRkuB9Zm5Fkw-1-VG8prQXfj8pI6dWX/view?usp=sharing)
349 |   :open_book: [Pattern Recognition and Machine Learning - Bishop](https://drive.google.com/file/d/1QkQj_azL6O7qUzshB8lPzueYWj0TRwEu/view?usp=sharing) (Advanced)
350 | #####    Recommended by [Eng.Mohamed Hammad](https://www.linkedin.com/posts/mohamed-hammad-a720a622_%D9%83%D8%AA%D8%A7%D8%A8-%D9%83%D9%84-%D9%85%D8%B1%D9%87-%D8%A7%D8%AD%D8%AA%D8%A7%D8%AC%D9%87-%D9%88%D8%A7%D8%B1%D8%AC%D8%B9%D9%84%D9%87-%D8%A7%D8%A8%D9%82%D9%8A-%D8%B9%D8%A7%D9%88%D8%B2-%D9%83%D9%84-%D8%A7%D9%84%D9%84%D9%8A-activity-7080526619525693441-nNn0?utm_source=share&utm_medium=member_desktop). 351 | *** 352 | 353 |

📌PROJECTS ⏬


354 | 355 |    🎥[Deena Gergis - End to end Project](https://www.youtube.com/playlist?list=PLatl6hdtJ0RnbkReSAuel6PeCPO155FpG)
356 |    🎥[Machine Learning Projects - Youtube](https://www.youtube.com/watch?v=fiz1ORTBGpY&list=PLfFghEzKVmjvuSA67LszN1dZ-Dd_pkus6)
357 |    💻[Top 10 Data Science Projects for Beginners](https://www.kdnuggets.com/2021/06/top-10-data-science-projects-beginners.html)
358 |    💻[12 Data Science Projects for Beginners and Experts](https://builtin.com/data-science/data-science-projects)
359 |    💻[Data Science Projects & Ideas](https://nevonprojects.com/data-science-projects-solutions/)
360 |    💻[Top 310+ Machine Learning Projects for 2023](https://data-flair.training/blogs/machine-learning-project-ideas/)
361 |    💻[10 End-to-End Guided Data Science Projects](https://pub.towardsai.net/10-end-to-end-guided-data-science-projects-to-build-your-portfolio-b7b9047fe6c9)
362 |    🎥[Real-World ML Tutorial w/ Scikit Learn](https://www.youtube.com/watch?v=M9Itm95JzL0)
363 |    💻[Python Codes in Data Science](https://github.com/RubensZimbres/Repo-2017/)
364 |    🎥[End To End ML Project With Dockers,Github Actions And Deployment](https://www.youtube.com/watch?v=MJ1vWb1rGwM)
365 |    💻[12 free Data Science projects to practice Python and Pandas (resolve interactive online)](https://www.datawars.io/articles/12-free-data-science-projects-to-practice-python-and-pandas)
366 | 367 | *** 368 |

📌 Common Tools ⤵️


369 | 370 | 371 | English | Arabic | Book 372 | --- | --- | --- 373 | :movie_camera: [Git - Udacity](https://www.udacity.com/course/version-control-with-git--ud123) | :movie_camera: [شخبط وانت مطمن ](https://www.youtube.com/watch?v=Q6G-J54vgKc)🚀 | :closed_book: [Pro Git](https://git-scm.com/book/en/v2) 374 | 📖 [w3schools](https://www.w3schools.com/git/) | :movie_camera: [almadrasa](https://almdrasa.com/tracks/programming-foundations/courses/git-github/) 375 |   | :movie_camera: [Elzero](https://www.youtube.com/playlist?list=PLDoPjvoNmBAw4eOj58MZPakHjaO3frVMF) 376 | 377 | *** 378 | ### :pushpin: **More Books :atom::atom: [:pushpin: Check This!](https://drive.google.com/drive/folders/1iW7IPrVUqsHumgXUMH_rgeBLpJjRDCmJ?usp=sharing)**
379 | 380 | 381 |   📕 [:fire: 12 Free Important Books :fire:](https://github.com/Moataz-Elmesmary/Data-Science-Roadmap/tree/main/Books)
382 |   📕 [Mathematics for Machine Learning ](https://mml-book.github.io/)
383 |   📕 [An Introduction to Statistical Learning](https://www.statlearning.com/)
384 |   📕 [Understanding ML: From Theory to Algorithms ](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf)
385 |   📕 [Probabilistic Machine Learning: An Introduction](https://probml.github.io/pml-book/book1.html)
386 |   📕 [storytelling with data](https://drive.google.com/file/d/1OQu6ZWImGnHbuI_WJOLPdSvKWCABSWMH/view?usp=sharing) ✔️Important data visualization guide.
387 | 388 | *** 389 |
390 |

:pushpin: Collection of the best Cheat sheets

 391 | 392 | 1. [Importing Data](https://lnkd.in/e3jnyTEi) 393 | 394 | 2. Pandas 395 | 396 |    - [(1)](https://lnkd.in/eiXuBbWh_) 397 |    - [(2)](https://lnkd.in/e8PKwQQQ) 398 |    - [(3)](https://lnkd.in/ewQfqe8q) 399 | 400 | 3. [Matplotlib](https://lnkd.in/ejxbW8ak) 401 | 402 | 4. [Seaborn](https://lnkd.in/ejhxUp2K) 403 | 404 | 5. [Probability](https://lnkd.in/e4Jxx6xP) 405 | 406 | 6. [Supervised Learning](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf) 407 | 408 | 7. [Unsupervised Learning](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf) 409 | 410 | 8. [Deep Learning](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf) 411 | 412 | 9. [Machine Learning Tips and Tricks](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf) 413 | 414 | 10. [Probabilities and Statistics](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf) 415 | 416 | 11. [Comprehensive Stanford Master Cheat Sheet](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf) 417 | 418 | 12. [Linear Algebra and Calculus](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf) 419 | 420 | 13. [Data Science Cheat Sheet](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf) 421 | 422 | 14. [Keras Cheat Sheet](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf) 423 | 424 | 15. [Deep Learning with Keras Cheat Sheet](https://github.com/rstudio/cheatsheets/raw/master/keras.pdf) 425 | 426 | 16. [Visual Guide to Neural Network Infrastructures](http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png) 427 | 428 | 17. [Skicit-Learn Python Cheat Sheet](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf) 429 | 430 | 18. [Scikit-learn Cheat Sheet: Choosing the Right Estimator](https://scikit-learn.org/stable/tutorial/machine_learning_map/) 431 | 432 | 19. [Tensorflow Cheat Sheet](https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf) 433 | 434 | 20. [Machine Learning Test Cheat Sheet](https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/) 435 | 436 | 21. [Machine Learning Cheat Sheets (Recommended Guide)](https://drive.google.com/file/d/1rQRJvWk5s9rUCesri0apxutbF4eDHR69/view?usp=sharing) *راجع المواضيع اللي في الشيت دي يا عزيزي وشوف اللي ناقصك*
437 | *** 438 | 439 | ### The best way to practice is to take part in competitions.
440 | **Competitions will make you even more proficient in Data Science.**
441 | When we talk about top data science competitions, [**Kaggle**](https://www.kaggle.com/) is one of the most popular platforms for data science. Kaggle has a lot of competitions where you can participate according to your knowledge level.
442 | 443 | **You can also check these platforms for data science competitions-**
444 | - [Driven Data](https://www.drivendata.org/competitions/)
445 | - [Codalab](https://competitions.codalab.org/)
446 | - [Iron Viz](https://www.tableau.com/community/iron-viz)
447 | - [Topcoder](https://www.topcoder.com/challenges)
448 | - [CrowdANALYTIX Community](https://www.crowdanalytix.com/community)
449 | - [Bitgrit](https://bitgrit.net/)
450 | 451 | 452 | *** 453 |

Interview Preparation: Your Roadmap to Success 🚀

454 | 455 | 📓 Data Science Interview Questions: :arrow_forward: 456 |   - [(1)](https://github.com/youssefHosni/Data-Science-Interview-Questions) 457 |  - [(2)](https://github.com/alexeygrigorev/data-science-interviews) 458 |  - [(3)](https://github.com/rbhatia46/Data-Science-Interview-Resources) 459 |  - [(4)](https://github.com/iamtodor/data-science-interview-questions-and-answers) 460 |  - [(5)](https://github.com/milaan9/DataScience_Interview_Questions) 461 |  - [(6) Arabic Podcast](https://www.youtube.com/watch?v=YjloQOreudk):headphones:
462 |                     - [(7) 30 days of interview preparation](https://github.com/Moataz-Elmesmary/Data-Science-Roadmap/blob/main/30%20days%20of%20interview%20preparation.pdf):book:
463 | 🚀 Practical Interview Questions from Actual Companies: [Data Analysis](https://prepare.sh/interviews/data-analysis) & [Data Engineering](https://prepare.sh/interviews/data-engineering) by @Prepare.sh. 464 | 465 | *** 466 | 467 |
🎧Data Science Podcasts: 🎙️
The Best Way to Stay Up-to-Date on the Latest Data Science Trends and Developments 468 | 469 | 470 |
471 | 472 | Podcasts | About | Produced by 473 | -- | --------------------------- | -- 474 | [Data Science at Home](https://datascienceathome.com/)|A podcast that provides practical advice and tutorials on data science topics.|Greg Linhardt, a data scientist and machine learning engineer at Google AI 475 | [Data Stories](https://datastori.es/)|An interview-driven podcast that tells the stories of data scientists and how they're using their skills to make a difference in the world.| Kirill Eremenko, a data scientist and machine learning engineer at Netflix 476 | [O'Reilly Data Show](https://www.oreilly.com/radar/topics/oreilly-data-show-podcast/)|A podcast that covers a wide range of data science topics, from machine learning to artificial intelligence to big data.|Ben Lorica, the Chief Data Scientist at O'Reilly 477 | [Learning Machines 101](https://www.learningmachines101.com/) |Mathematics, statistics, and algorithms that power the machine learning systems that we rely on every day.|Richard Golden, a machine learning engineer and researcher at Google AI 478 | [Data Engineering Podcast](https://www.dataengineeringpodcast.com/) |Tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation.|Tobias Macey, a data engineer at Netflix 479 | [Data Science Mixer](https://community.alteryx.com/t5/Data-Science-Mixer/bg-p/mixer) |A great resource for anyone who wants to learn more about data science and the latest trends in the field. It is also a great way to get inspired by the work of other data scientists and machine learning engineers.|Alteryx, a data science and analytics software company 480 | [Chai Time Data Science Show](https://www.youtube.com/playlist?list=PLLvvXm0q8zUbiNdoIazGzlENMXvZ9bd3x) |Interviews top data scientists, practitioners, and researchers from around the world.|Sanyam Bhutani, a data scientist and machine learning engineer at Google AI. 481 | [Becoming a Data Scientist](https://www.becomingadatascientist.com/category/podcast/)|Podcast that interviews data scientists about their journey to becoming a data scientist.|Renee Teate, a data scientist and machine learning engineer at Google AI. 482 | [AI Today Podcast](https://www.aidatatoday.com/aitoday/)|Explores the latest trends and developments in artificial intelligence.|Ron Schmelzer and Kathleen Walch 483 | [Gradient Dissent](https://wandb.ai/fully-connected/podcast)|A weekly podcast that explores the latest research in machine learning and artificial intelligence.|Chris Olah, a machine learning engineer at Google AI 484 | [Data Skeptic](https://dataskeptic.com/)|A podcast that challenges the conventional wisdom in data science and asks tough questions about the ethics and implications of data-driven decision making.|Kyle Polich, a data scientist and machine learning engineer 485 | [Linear Digressions](https://lineardigressions.com/)|A podcast that covers a wide range of data science topics, from the technical to the theoretical.|Ben Recht and Noah Smith, two machine learning researchers at the University of California, Berkeley 486 | [The Data Engineering Show](https://www.dataengineeringshow.com/)|For data engineering and BI practitioners to go beyond theory, and learn from the biggest influencers in tech about their practical day to day data challenges.|Eldad Farkash and Benjamin Wagner, who are both data engineering experts with experience at companies like Firebolt and Sisense 487 | [DataTalks.Club](https://podcasters.spotify.com/pod/show/datatalksclub)|A weekly online community of data enthusiasts and practitioners that learn from each other and share their knowledge and experiences through meetups, workshops, and a podcast.|A rotating cast of data experts 488 | [Datacast](https://jameskle.com/writes/category/Datacast)|Top data scientists and practitioners in the data and AI infrastructure space.|James Le, who is a data infrastructure expert with experience at companies like Google and Netflix 489 | [How to Get an Analytics Job Podcast](https://www.youtube.com/playlist?list=PLBvzkZLydYX0D28bbnfRCV6M4zMQrhXsd)|A great resource for anyone who is interested in a career in analytics. The guests share their insights and advice on how to get started in analytics and how to succeed in an analytics career.|John David Ariansen, an analytics agency owner and career coach 490 | [The Analytics Power Hour](https://analyticshour.io/)|Five awesome people, an occasional guest, and drinks all around tackling the hottest data and analytics topics of the day.|Tim Wilson, Michael Helbling, Josh Crowhurst, and Val Kroll. They are all analytics experts from different companies 491 |
492 | 493 |
494 | 495 |
    :eyes: Arabic Podcasts?? 496 | 497 | ######      :trollface:شايفك ياللي زهقان في المواصلات 498 |    📻[Arabic Data Podcast](https://www.youtube.com/@arabic_data_podcast) | [Spotify](https://open.spotify.com/show/6xo79RT4NP73wQA39TgAq1) by Eng. Kareem Abdelsalam
499 |    📻[lإلي البيانات وما بعدها](https://www.youtube.com/watch?v=3znPvz6P2oM&list=PL9yAM5pvSfU5EdppOCf-YvttRsabeAmbN) by Eng. Youssef Hosni
500 |    📻[Garage Education](https://www.youtube.com/@GarageEducation/playlists) by Eng. Mostafa Alaa
501 |    📻[Data Science بالعربي](https://www.boomplay.com/podcasts/29169)
502 |
503 | 504 | *** 505 | 506 | :pushpin: **Data Analysis Recommendations.**
507 | 508 | Books (📕 [The Data Analysis Workshop](https://drive.google.com/file/d/1BjKsffA2SCY0jY8OIIzgQgM0ZS7E9v_v/view?fbclid=IwAR2_GBlrX7VYoo8WCRO9R2qqrYEqtytoGrObxy1QHWcQ7sRaFjRLb0GmuxM) & 509 | 📕 [Head First Data Analysis](https://drive.google.com/file/d/1HXHkwrgsSJLYSeB6I0wPUXIGGnm2-HQ6/view?fbclid=IwAR27M-dlPN6o0YuZg3bXH6_DP9L2fBhkKDEkChvO4SPG-SXfkxrzuoGP5RM))
510 | [FWD - (The 3 Levels)](https://egfwd.com/?fbclid=IwAR1phYmHHgi0L4E9nOPZcSfAdHWsDs9EvBh3dJgO6gXN4B1A-nV8vspGggs)
511 | [Google Data Analytics Professional Certificate](https://www.coursera.org/professional-certificates/google-data-analytics)
512 | [IBM Data Analyst Professional Certificate](https://www.coursera.org/professional-certificates/ibm-data-analyst?fbclid=IwAR1IajEEe2yydVWRt3hbj4qLioXP6oR-fdbw8f1kHAVpAXSA4Z8Eww1Y-fs)
513 | [Google Advanced Data Analytics Professional Certificate :new:](https://www.coursera.org/professional-certificates/google-advanced-data-analytics?irclickid=zzy33K1O0xyNUAmxqWUjDwedUkAQlBwwJ21EwA0&irgwc=1&utm_medium=partners&utm_source=impact&utm_campaign=2624140&utm_content=b2c)
514 | [Alex The Analyst - YouTube📺](https://www.youtube.com/@AlexTheAnalyst/playlists)
515 | *Note: A good knowledge & projects in just [Excel](https://www.coursera.org/learn/excel-basics-data-analysis-ibm), SQL & Power BI / Tableau can bring you great opportunities*.
516 |   -excel Excel More Resources: ([Arabic 1📹](https://www.youtube.com/watch?v=9Z5MPeyuLhg&t=397s) - [Arabic 2📹](https://www.youtube.com/watch?v=uRs8_EJqTFo&list=PLXlHqMRg9lAYiiutr-Ou0J1uU20T-5a4-&pp=iAQB) - [Books :page_facing_up: and cheat sheets for revising](https://drive.google.com/drive/folders/1CAUKDb5jv1pMez1WO74ogkpX44UMW_ky))
517 |

518 | 519 | 520 | :pushpin: **[Data Engineering](https://youtu.be/qWru-b6m030) Recommendations.**
521 | Books (📕 [Fundamentals of Data Engineering](https://drive.google.com/file/d/1CbQFN0Lw8o6v4KlF64LsCyaooMccT45T/view?usp=sharing) & 522 | 📕 [Designing Data-Intensive Applications](https://drive.google.com/file/d/1CrzA--WWNcxxQwLqzg1yPfiI3FaEo49z/view?usp=sharing))
523 | 524 | Arabic Podcast, [Starting a Career in Data Engineering.](https://www.youtube.com/watch?v=OtaBhXjrbX4)
525 | For Arab, I recommend 2 YouTube Channels: ([Garage Education](https://www.youtube.com/@GarageEducation) & [Big Data بالعربي](https://www.youtube.com/playlist?list=PLrooD4hY1QqAK5pbBpcthLuMa-cXnXJLE))
526 | [Roadmap 1](https://github.com/OmarEhab007/Data_Engineering_Mentorship) - *(Recommended)*
527 | [Roadmap 2](https://www.educba.com/data-engineer-roadmap/)
528 | [Roadmap 3](https://github.com/datastacktv/data-engineer-roadmap)
529 | [IBM Data Engineering Professional Certificate](https://www.coursera.org/professional-certificates/ibm-data-engineer)
530 | *Note: A good knowledge & projects in SQL, Python, Apache [Spark](https://www.udacity.com/course/learn-spark-at-udacity--ud2002)/Hadoop, Data Modeling and [[Data Warehouse](https://www.coursera.org/learn/dwdesign) - {Arabic-[Starting from the 7th video](https://www.youtube.com/playlist?list=PLxNoJq6k39G_m6DYjpz-V92DkaQEiXxkF)} can bring you great opportunities. Start with them then go for the other tools,concepts and cloud platforms*.
531 | *** 532 |

:file_folder: CV / Resumes :memo:   533 | 534 | 535 | 536 | 537 | 538 | - [Common mistakes by Yehia Arafa Mostafa](https://www.facebook.com/yehia.arafa.mostafa/posts/110086229517000)
539 | - [CV Tips by Omar Yasser](https://medium.com/@oyaraouf/cv-tips-5faaec55ec07)
540 | - [This Is What A GOOD Resume Should Look Like by careercup](https://www.careercup.com/resume)
541 | - After you have made your beta-version resume, check those [reviews from Mostafa Nageeb](https://www.facebook.com/story.php?story_fbid=2928705840553931&id=445112032246670)
542 | - [After Graduation by Yasser Alaa](https://www.linkedin.com/feed/update/urn:li:activity:6964595411839799296/)
543 | - [How to make Data Science Resume](https://enhancv.com/resume-examples/data-scientist/)
544 | - [Data Science Resume Guide](https://www.beamjobs.com/resumes/data-science-resume-example-guide)
545 | - Resume/CV building for Data Jobs (Arabic)
546 |   📹[Video 1](https://www.youtube.com/watch?v=R0hsJiNxdDE)
547 |   📹[Video 2](https://www.youtube.com/watch?v=CrTO0hrC-zQ) 548 |
549 | 550 | *** 551 | :pushpin: [Data & AI Companies in Egypt](https://trello.com/b/u4HH9Anu/data-ai-jobs-in-egypt)   -   [AI/ML Driven Companies In Egypt](https://github.com/harryadel/AI-ML-Driven-Companies-In-Egypt) 552 | *** 553 | 554 |

Contact Me :iphone:


555 | 556 | 557 | 558 |

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