├── .github └── FUNDING.yml ├── 30 days of interview preparation.pdf ├── Content - Guide!!.txt ├── Introduction to Statistical Learning with Applications in R.pdf ├── LICENSE ├── Practical Statistics for Data Scientists.pdf └── 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/marantmir/Data-Science-Roadmap-2023/f150fc88c14414871e2adc4d560dcd054edc954f/30 days of interview preparation.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 | -------------------------------------------------------------------------------- /Introduction to Statistical Learning with Applications in R.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marantmir/Data-Science-Roadmap-2023/f150fc88c14414871e2adc4d560dcd054edc954f/Introduction to Statistical Learning with Applications in R.pdf -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /Practical Statistics for Data Scientists.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marantmir/Data-Science-Roadmap-2023/f150fc88c14414871e2adc4d560dcd054edc954f/Practical Statistics for Data Scientists.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![photo_2022-04-20_03-18-03](https://user-images.githubusercontent.com/92026137/164127639-38317783-8c5e-4b85-acf5-3d6bd6dbf9c0.jpg) 2 |

     DATA SCIENCE ROADMAP :pirate_flag: 2023

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 interchangably 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 | ### :bulb: Roadmap Explanation :arrow_forward: [Youtube Video](https://youtu.be/HbIPJuvzRLk) :movie_camera: 58 | 59 | *** 60 | 61 | ## :beginner: Beginner :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**
68 |    :video_camera: [Intro to descriptive statistics](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827) | Same Course [on YouTube](https://www.youtube.com/playlist?list=PLAwxTw4SYaPn22DmaF6x8JtG4TeWOJk_1)
69 |    :video_camera: [Statistics Fundamentals - StatQuest - Youtube](https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9)
70 |    :closed_book: [Online statistics education](http://onlinestatbook.com/Online_Statistics_Education.pdf)
71 |    :closed_book: Intro to descriptive statistics [Article1](https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fdescriptive-statistics-f2beeaf7a8df) & [Article2](https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fintro-to-descriptive-statistics-252e9c464ac9)
72 |    :video_camera: [Arabic Course](https://www.youtube.com/watch?v=d5jh5mmwcKI&list=PLY99ZSsxRyJiu6kb4WRRpeEFqK1pAr-EO)
73 |    :video_camera: [Intro to Inferential Statistics](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)++
74 |    :closed_book: [Practical Statistics for Data Scientists](https://github.com/Moataz-Elmesmary/Data-Science-Roadmap/blob/main/Practical%20Statistics%20for%20Data%20Scientists.pdf)
75 | 76 | **2. Probability**
77 |    :video_camera: [Khan Academy](https://www.khanacademy.org/math/statistics-probability/probability-library)
78 |    :video_camera: [Arabic Course](https://www.youtube.com/playlist?list=PL158D091D26F47358)
79 |    :closed_book: [Introduction to Probability](https://drive.google.com/file/d/15Y0oFNHQRls1qvQNvO3DFLJVhIZvUjTD/view?usp=sharing)
80 | 81 | **3. Programming Languages**
82 | 83 |  :small_blue_diamond:*R* - *good tool for visualization and statistical analysis.*
84 |    :video_camera: [Introduction to R (Datacamp)](https://www.datacamp.com/courses/free-introduction-to-r)
85 |    :video_camera: [Data Science Specialization - coursera](https://www.coursera.org/specializations/jhu-data-science)
86 |    :closed_book: [An Introduction to R](https://cran.r-project.org/doc/manuals/R-intro.pdf)
87 |    :closed_book: [R for Data Science](https://r4ds.had.co.nz/)
88 | 89 |  :small_blue_diamond:*Python*:100:
90 |    :video_camera: [Introduction to Python Programming](https://www.udacity.com/course/introduction-to-python--ud1110)
91 |    :video_camera: [OOP](https://learn.datacamp.com/courses/object-oriented-programming-in-python)
92 |    :video_camera: Arabic - [Hassouna](https://www.youtube.com/watch?v=MxYLqE3Ils8&list=PLHIfW1KZRIfnM9y0sQRwjVz2-IwvnEJep) | [Elzero](https://www.youtube.com/watch?v=mvZHDpCHphk&list=PLDoPjvoNmBAyE_gei5d18qkfIe-Z8mocs)
93 |    :video_camera: [Python Full Course - FreeCodeCamp on YouTube](https://www.youtube.com/watch?v=rfscVS0vtbw)
94 |    :closed_book: [Intro to Python for CS and Data Science](https://drive.google.com/file/d/1rXkYFjw1iKbXCra_B4Ykm0AMRgo6v93w/view?fbclid=IwAR2lg9omGaAsG3g1ZhHQHja8_uxkZ7QddnOUSxfoceRXShU1V_bl4V63xCQ)
95 |    [more in OOP](https://www.futurelearn.com/courses/object-oriented-principles)
96 | **4. Pandas**
97 |    :video_camera: [Corey Schafer-Youtube](https://www.youtube.com/watch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS)
98 |    :closed_book: [Kaggle](https://www.kaggle.com/learn/pandas)
99 |    :closed_book: [Docs](https://pandas.pydata.org/pandas-docs/version/0.15/tutorials.html)
100 |    :video_camera: [Data School-Youtube](https://www.youtube.com/watch?v=yzIMircGU5I&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=1)
101 |    :video_camera: [Arabic Course](https://www.youtube.com/watch?v=3ISW655DemU&list=PLvLvlVqNQGHCb2_ygmr1DQOMOv0yXp84F)
102 | **5. Numpy**
103 |    :closed_book: [Kaggle](https://www.kaggle.com/legendadnan/numpy-tutorial-for-beginners-data-science)
104 |    :video_camera: [Arabic Course](https://www.youtube.com/watch?v=5-5CrLmf2vk&list=PLIA_seGogbkGDYq-dnVCsELEIq_7HK7Ca)
105 |    :closed_book: [Tutorial](http://cs231n.github.io/python-numpy-tutorial/)
106 |    :closed_book: [Docs](https://numpy.org/doc/1.18/user/quickstart.html)
107 | **6. Scipy**
108 |    :closed_book: [Tutorial](https://cs231n.github.io/python-numpy-tutorial/#scipy)
109 |    :closed_book: [Docs](https://docs.scipy.org/doc/scipy/reference/tutorial/general.html)
110 | **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.
111 |    [Read this](https://towardsdatascience.com/the-ultimate-guide-to-data-cleaning-3969843991d4)
112 |    :video_camera: [Course 1](https://www.datacamp.com/courses/cleaning-data-in-python)
113 |    :closed_book: [Notebook1](https://www.kaggle.com/bandiatindra/telecom-churn-prediction)
114 |    :closed_book: [Notebook2](https://drive.google.com/drive/folders/1OQAEQ8rC4j6oBP7AyDU4bKpPr8sSStJI?fbclid=IwAR2dSrbyoZLM-Wm57yEYy8L8PmpPV9hqXdkNf-pURJC5C5xCz7UJB4YpJ7M)
115 |    :closed_book: [Notebook3](https://www.kaggle.com/ashishg21/data-cleaning-and-some-analysis-shoe-prices)
116 |    :closed_book: [Kaggle Data cleaning](https://www.kaggle.com/learn/data-cleaning)
117 | **8. Data Visualization** :bar_chart:
118 |    :video_camera: [Introduction to Data Visualization with Matplotlib](https://app.datacamp.com/learn/courses/introduction-to-data-visualization-with-matplotlib?fbclid=IwAR1OrJSdZ2LVD_c1o3d-_1I7Nhq8OZ3pzTu4010E_XWEmMc0KYsTosz8CIU) or
119 |    :video_camera: [ Corey Schafer - Playlist on Youtube](https://www.youtube.com/watch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_) or
120 |    :video_camera: [sentdex - Playlist on YouTube](https://www.youtube.com/watch?v=q7Bo_J8x_dw&list=PLQVvvaa0QuDfefDfXb9Yf0la1fPDKluPF)
121 |    :closed_book: [Kaggle to Data Visualization with Seaborn](https://www.kaggle.com/learn/data-visualization)
122 |    :video_camera: [Playlist-Youtube](https://www.youtube.com/watch?v=z7ZINBk8EUk&list=PL998lXKj66MpNd0_XkEXwzTGPxY2jYM2d)
123 |    :video_camera: [Course1: Intro to Data Visualization with Seaborn](https://learn.datacamp.com/courses/introduction-to-data-visualization-with-seaborn)
124 |    :video_camera: [Course2: Intermediate Data Visualization with Seaborn 125 | ](https://learn.datacamp.com/courses/intermediate-data-visualization-with-seaborn)
126 |    :video_camera: [Course3: Understanding and Visualizing with Python](https://www.coursera.org/learn/understanding-visualization-data)
127 | 128 | **9. EDA** 129 | Note: it's already mentioned in the above probability course
130 |    :video_camera: [DataCamp-EDA in Python](https://learn.datacamp.com/courses/exploratory-data-analysis-in-python)
131 |    :video_camera: [IBM-EDA for Machine Learning](https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning)
132 | 133 | **10. Dashboards**
134 | 135 |  *Power BI*
136 |    :video_camera: [Power BI - Youtube (Alex)](https://youtube.com/playlist?list=PLUaB-1hjhk8HqnmK0gQhfmIdCbxwoAoys&si=pR4VSrR1P2O-AaBJ)
137 |    :video_camera: [Power BI training](https://powerbi.microsoft.com/en-us/learning/)
138 |    :video_camera: [Arabic - Youtube (Zanoon)](https://www.youtube.com/watch?v=ykvAWKML9Gk&list=PLof3yw6ZFPFhV75Ptf-5Q88bgUtLOBvOw)
139 |    :video_camera: [Arabic - Youtube](https://www.youtube.com/watch?v=ykvAWKML9Gk&list=PLof3yw6ZFPFhV75Ptf-5Q88bgUtLOBvOw)
140 |  *Tableau*
141 |    :closed_book: [Tutorial](https://www.datacamp.com/community/tutorials/data-visualisation-tableau)
142 |    :video_camera: [docs](https://www.tableau.com/learn/training/20201)
143 |    :video_camera: [course - datacamp](https://learn.datacamp.com/courses/introduction-to-tableau)
144 |    :video_camera: [Simplilearn - Youtube](https://learn.datacamp.com/courses/introduction-to-tableau)
145 | 146 | 147 | **11. SQL and DB**
148 |    :video_camera: SQL for Data Analysis ([Udacity](https://www.udacity.com/course/sql-for-data-analysis--ud198)-notes[*l:clipboard:l*](https://github.com/julianjohannesen/Udacity-SQL-Notes/tree/main) or [simplilearn](https://www.simplilearn.com/free-online-course-to-learn-sql-basics-skillup))
149 |    :video_camera: [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)
150 |    :video_camera: [Intro to Relational Databases in SQL](https://learn.datacamp.com/courses/introduction-to-relational-databases-in-sql)
151 |    :video_camera: [Arabic Course](https://www.youtube.com/watch?v=B7evUQGmN6M&list=PLfM2wZNebA2zROxUcAbGxNrpVZncsF3oD)
152 |    :video_camera: [Arabic -ITI by Eng.Ramy](https://www.youtube.com/watch?v=QJHy89zMvj8&list=PLYpJKvLDuJhgMzOXRwUJ2_ZlVt3zSh8PA&index=1) *Advanced* - [[Course Materials]](https://drive.google.com/drive/folders/1P_uUWl6pBPC3MFrqR_ovoJQNCZhi9rAS?fbclid=IwAR1EyxPR3pLXABIcK2bkgh4_HYNSowb01Kij8OiHFh_x5maiqsiwvJNHvXg)
153 |    :video_camera: [365 Data Science - SQL](https://mega.nz/folder/wswGEIhb#tsqUggTZyfy5HyRWUkV9sg/folder/R1AxXCxB)
154 |    :pencil: Practice [HackerRank](https://www.hackerrank.com/domains/sql) & [DataLemur](https://datalemur.com/) 155 | 156 | **12. Python Regular Expression**
157 |    :closed_book: [Tutorial](https://www.datacamp.com/community/tutorials/python-regular-expression-tutorial)
158 | **13. Time Series Analysis**
159 |    :video_camera: [Track - DataCamp](https://learn.datacamp.com/skill-tracks/time-series-with-python)
160 |    :video_camera: [Course - Coursera](https://www.coursera.org/learn/practical-time-series-analysis)
161 |    :closed_book: [Book](https://www.oreilly.com/library/view/practical-time-series/9781492041641/?fbclid=IwAR20cq7hAdWf6voOd61u-pNzZCHvB0rZhT_BUoGTAXxPBhhi82p8BhxLEsI)
162 |    :closed_book: [fbprohet](https://facebook.github.io/prophet/docs/quick_start.html)
163 |    :video_camera: 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)
164 | 165 |

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

166 | 167 | *** 168 | 169 | 170 | ## :beginner: Intermediate :beginner: 171 | 172 | **1. Math for ML**: consists of Linear Algebra, Calculus and PCA.
173 | :video_camera: [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)
174 | :video_camera: [Specialization](https://www.coursera.org/specializations/mathematics-machine-learning)
175 | :video_camera: [Mathematics for Machine Learning - Most of the needed basics](https://www.youtube.com/watch?v=vLJcduC4lBM&list=PLcQCwsZDEzFmlSc6levE3UV9rZ8yY-D_7)

176 | :small_blue_diamond:Linear Algebra
177 |    :video_camera: [Khan Academy - Linear Algebra](https://www.khanacademy.org/math/linear-algebra)
178 |    :video_camera: [Mathematics for Machine Learning: Linear Algebra](https://www.coursera.org/learn/linear-algebra-machine-learning)
179 |    :video_camera: [3Blue1Brown - Essence of Linear Algebra](https://www.3blue1brown.com/topics/linear-algebra)
180 | :small_blue_diamond:Calculus
181 |    :video_camera: [Multivariate Calculus - Coursera](https://www.coursera.org/learn/multivariate-calculus-machine-learning?fbclid=IwAR243aoz0jxs4iUn539pnjSQliXtr7Y5QAsvgeRTietZT_tkyoRU3b6Sq1o)
182 |    :video_camera: [Essence of calculus - Youtube](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr)
183 | :small_blue_diamond:PCA
184 |    :video_camera: [PCA - Coursera](https://www.coursera.org/learn/pca-machine-learning)
185 | 186 | 187 | **2. Machine Learning**
188 |    :video_camera: [Coursera - Old Course by Andrew Ng (Octave/Matlab)](https://www.coursera.org/learn/machine-learning)
189 |    :video_camera: [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)
190 |    :video_camera: [Machine Learning - StatQuest - YouTube](https://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF)
191 |    :video_camera: [Machine Learning Stanford Full Course on YouTube by Andrew](https://www.youtube.com/watch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN)
192 |    :video_camera: [CS480/680 Intro to Machine Learning - Spring 2019 - University of Waterloo](https://www.youtube.com/playlist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k)
193 |    :video_camera: [SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)](https://www.youtube.com/playlist?list=PL4upCU5bnihwCX93Gv6AQnKmVMwx4AZoT)
194 |    :video_camera: [Machine Learning for Engineers 2022](https://apmonitor.com/pds/) / ([YouTube](https://www.youtube.com/watch?v=Gh5rbBLh4JY&list=PLLBUgWXdTBDg1K1bu60lHypSzSP-WSBmx))
195 |    :video_camera: [Introduction to Machine Learning Course - Udacity](https://www.udacity.com/course/intro-to-machine-learning--ud120)
196 |    :video_camera: [Hesham Asem - Arabic content](https://www.youtube.com/c/HeshamAsem/playlists)
197 |    :video_camera: [IBM ML with Python](https://www.coursera.org/learn/machine-learning-with-python)
198 |    :video_camera: [Machine Learning From Scratch - YouTube (Python Engineer)](https://www.youtube.com/watch?v=ngLyX54e1LU&list=PLqnslRFeH2Upcrywf-u2etjdxxkL8nl7E)
199 |    :closed_book: 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 | example code '[Notebooks](https://github.com/ageron/handson-ml?fbclid=IwAR3s31KlwkLKyrEwuEd4UMOcvHN1Q9Z2LLGzPg5vP4UKSwjriHxU0uO405c)'
200 |    :video_camera: [ML Algorithms in Practice](https://www.coursera.org/specializations/machine-learning-algorithms-real-world?utm_medium=email&utm_source=marketing&utm_campaign=A39CcMUuEempyReieZALEQ)
201 |    :video_camera: [ML scientist](https://learn.datacamp.com/career-tracks/machine-learning-scientist-with-python?version=1)
202 |    :video_camera: [Project](https://www.coursera.org/learn/applied-data-science-capstone)
203 | 204 | **3. Web Scraping/APIs**
205 |    :video_camera: [course](https://learn.datacamp.com/courses/web-scraping-with-python)
206 |    :closed_book: [intro2](https://www.dataquest.io/blog/web-scraping-tutorial-python/)
207 |    :closed_book: [Tutorial](https://realpython.com/beautiful-soup-web-scraper-python/)
208 |    :closed_book: [Book for both topics](https://b-ok.africa/book/3515980/5d50aa)
209 | APIs
210 |    :closed_book: [Tutorial](https://www.dataquest.io/blog/python-api-tutorial/)
211 |    :closed_book: [Article](https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-pull-data-from-an-api-using-python-requests-edcc8d6441b1)
212 |    :closed_book: [Tutorial](https://rapidapi.com/blog/how-to-use-an-api-with-python/)
213 | **4. Stats.**
214 |    :closed_book: [This stats - Book](https://greenteapress.com/thinkstats/thinkstats.pdf)
215 |    :closed_book: [Think Bayes - Book](https://www.greenteapress.com/thinkbayes/thinkbayes.pdf)
216 | **5. Advanced SQL**
217 |    :video_camera: [Joining Data in SQL - DataCamp](https://learn.datacamp.com/courses/joining-data-in-postgresql)
218 |    :video_camera: [Intermediate SQL - DataCamp](https://learn.datacamp.com/courses/joining-data-in-postgresql)
219 |    :video_camera: [More advanced SQL](https://www.coursera.org/lecture/data-driven-astronomy/more-advanced-sql-GDmo5)
220 | 221 | **7. Feature Engineering**
222 |    :closed_book: [Tutorial](https://www.kaggle.com/learn/feature-engineering)
223 |    :closed_book: [Article](https://www.medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Ffeature-engineering-for-machine-learning-3a5e293a5114)
224 |    :closed_book: [Book](https://drive.google.com/file/d/1BkJYO0tqMYptTWUDQ7X0vd2aygohHRm8/view?usp=sharing)
225 | **8. interpet Shapley-based explanations of ML models.**
226 |    :closed_book: [SHAP](https://shap.readthedocs.io/en/latest/)
227 |    :closed_book: [Kaggle ML explainability](https://www.kaggle.com/learn/machine-learning-explainability)
228 |

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

229 | 230 | Read this book, please :open_book: [Introduction to Statistical Learning with Applications in R](https://github.com/Moataz-Elmesmary/Data-Science-Roadmap/blob/main/Introduction%20to%20Statistical%20Learning%20with%20Applications%20in%20R.pdf) بقولك اقرأه
231 | *** 232 | ## :beginner: Advanced :beginner: 233 | 234 | **1. Deep Learning**
235 |    :video_camera: [Deep Learning Fundamentals](https://www.youtube.com/playlist?list=PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU)
236 |    :video_camera: [Introduction to 237 | Deep Learning - MIT](http://introtodeeplearning.com/?fbclid=IwAR35rIygYlCn84DV7mlHvdvs4sMUm2D6RLYVwFpp2nT2t1Zj1GGy3QAWQvQ)
238 |    :video_camera: [Specialization](https://www.coursera.org/specializations/deep-learning)
239 |    :closed_book: [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)
240 |    :video_camera: [Deep Learning UC Berkely](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW)
241 |    :closed_book: [github of Dive into DL](https://github.com/d2l-ai/d2l-en?fbclid=IwAR0QN35b-NHHWq_zKISA1cbI063aRqqoKqR_0e3cpnT5h58GkcNbCIJs3iw)
242 |    :video_camera: [Stanford Lecture - Convolutional Neural Networks for Visual Recognition](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)
243 |    :video_camera: [University of Waterloo - ML / DL](https://www.youtube.com/playlist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k)
244 |    :closed_book: [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)
245 | 246 | 247 | **2. Tensorflow**
248 |    :video_camera: [Specialization](https://www.coursera.org/specializations/tensorflow-in-practice)
249 |    :video_camera: [Youtube](https://www.youtube.com/playlist?list=PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL)
250 |     [fast.ai's Deep Learning Courses](https://www.fast.ai/)
251 | ###### TensorFlow beats PyTorch in visualization capabilities and deploying trained models. Go for PyTorch if you want flexibility, debugging capabilities, and short training duration. 252 | 253 | **3. PyTorch**
254 |    :video_camera: [PyTorch (UC Berkeley - Youtube) - Lec3 (The 5 parts)](https://m.youtube.com/watch?v=AOypIa_8RXg&list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH&index=11)
255 |    :video_camera: [PyTorch - Dr. Data Science - Youtube](https://www.youtube.com/watch?v=vVQs4h6HUvA&list=PLLeO8f6PhlKb_FAC7qxOBtxT9-8EPDAqk)
256 |    :video_camera: [Pytorch Tutorial - Aladdin - Youtube](https://www.youtube.com/watch?v=2S1dgHpqCdk&list=PLhhyoLH6IjfxeoooqP9rhU3HJIAVAJ3Vz)
257 |    :video_camera: [PyTorch Course (2022) - Youtube](https://www.youtube.com/watch?v=v43SlgBcZ5Y&list=PLkdGijFCNuVk9fO1IMfdV1Igob0FUHhkB)
258 |    :closed_book: [Deep Learning With Pytorch](https://drive.google.com/file/d/1-KG_ufeg7zw2iLgG5RrJSFpyonLwulgF/view?usp=sharing)
259 |    :closed_book: [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)
260 | 261 | **4. Advanced Data Science**
262 |    :video_camera: [Advanced Data Science with IBM Specialization](https://www.coursera.org/specializations/advanced-data-science-ibm) *Includes Apache Spark*
263 |  ☠️*Advanced ML Topics🧠 | Lecs (YouTube)*
264 |    :video_camera: [Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022](https://www.youtube.com/playlist?list=PLoROMvodv4rNjRoawgt72BBNwL2V7doGI) - [Materials](https://cs330.stanford.edu/)
265 |    :video_camera: [18.409 Algorithmic Aspects of Machine Learning Spring 2015 - MIT](https://www.youtube.com/playlist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx)
266 |  ☠️*ML based Computer Vision | Lecs (YouTube)*
267 |    :video_camera: [CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)](https://www.youtube.com/playlist?list=PLzWRmD0Vi2KVsrCqA4VnztE4t71KnTnP5)
268 |    :video_camera: [NOC:Deep Learning For Visual Computing - IIT Kharagpur](https://nptel.ac.in/courses/108105103)
269 |    :video_camera: [Deep Learning for Computer Vision - Michigan](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r)
270 | 271 | **5. NLP**
272 |    :video_camera: [Specialization - Coursera](https://www.coursera.org/specializations/natural-language-processing)
273 |    :video_camera: [Arabic - Ahmed El Sallab](https://www.youtube.com/playlist?list=PLxmZ0b-n395VxzEUL8-Dy257zSqYZe4yU)
274 |    :video_camera: [Stanford CS224N Lectures - Winter 2021- YouTube](https://www.youtube.com/playlist?app=desktop&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&si=k91y-bepIiPjHMrj&fbclid=IwAR2h6KcYboHCjG9YBIEB08srgYSesqZ5UHXr0ni8yxOqrxNV3-_TGxq0Csg)
275 |    :video_camera: [Stanford XCS224U Lectures - Spring 2021- YouTube](https://www.youtube.com/playlist?app=desktop&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&si=k91y-bepIiPjHMrj&fbclid=IwAR2h6KcYboHCjG9YBIEB08srgYSesqZ5UHXr0ni8yxOqrxNV3-_TGxq0Csg)
276 |    :video_camera: [Introduction to Natural Language Processing in Python](https://www.datacamp.com/courses/natural-language-processing-fundamentals-in-python)
277 |  :small_orange_diamond:*LLMS [What`s Large Language Model](https://www.snowflake.com/guides/what-large-language-model-and-what-can-llms-do-data-science)?*
278 |    :video_camera: [Generative AI with LLMs](https://www.deeplearning.ai/courses/generative-ai-with-llms/)
279 |    :video_camera: [LLM Foundations](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/llm-foundations/)
280 |    :video_camera: 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*
281 |    :video_camera: [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*
282 |    :video_camera: [LLMOps](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/llmops/) *A Lec going through the entire LLM pipeline*
283 | 284 | 285 | **6. Inferential Statistics**
286 |    :video_camera: [Specialization, 2nd & 3rd courses](https://www.coursera.org/specializations/statistics-with-python)
287 |    :video_camera: [course](https://www.coursera.org/learn/statistical-inferences)
288 | **7. Bayesian Statistics**
289 |    :video_camera: [1 - From Concept to Data Analysis](https://www.coursera.org/learn/bayesian-statistics)
290 |    :video_camera: [2 - Techniques and Models](https://www.coursera.org/learn/mcmc-bayesian-statistics)
291 |    :video_camera: [3 - Mixture Models](https://www.coursera.org/learn/mixture-models)
292 | **8. Model Deployment**
293 |    :closed_book: [Flask tutorial](https://towardsdatascience.com/deploying-a-deep-learning-model-using-flask-3ec166ef59fb)
294 |    :video_camera: [TensorFlow: Data and Deployment Specialization](https://www.coursera.org/specializations/tensorflow-data-and-deployment)
295 |    :video_camera: [Deploy Models with TensorFlow Serving and Flask](https://www.coursera.org/projects/deploy-models-tensorflow-serving-flask)
296 |    :video_camera: [How to Deploy a Machine Learning Model to Google Cloud - Daniel Bourke](https://www.youtube.com/watch?v=fw6NMQrYc6w)
297 |    if you`re intersted in more deployment methods, search for (_FastAPI - Heroku - chitra_)
298 | 299 | **9. MLOps** : is a combination of Model Deployment, Model Serving, Model Monitoring, and Model Maintenance. 300 |    :link: [MLOps-zoomcamp](https://github.com/DataTalksClub/mlops-zoomcamp)
301 |    :link: [MLOps-guide](https://github.com/Nyandwi/machine_learning_complete/blob/main/010_mlops/1_mlops_guide.md)
302 |    :closed_book: [Practical MLOps](https://drive.google.com/file/d/17RhXQ2ix6rFMaas3HI7bnM_GL8lS7u3f/view?usp=sharing)
303 | **10. Probabilistic Graphical Models** 304 |    :video_camera: [Specialization - Coursera](https://www.coursera.org/specializations/probabilistic-graphical-models)
305 |    :video_camera: [Spring 2016, University of Utah - YouTube](https://www.youtube.com/playlist?list=PLbuogVdPnkCpvxdF-Gy3gwaBObx7AnQut)
306 |

307 | 308 | 309 | :star2: Read these books, they will be beneficial to you.
310 |   :open_book: [Bayesian Reasoning and Machine Learning](https://drive.google.com/file/d/18fh0orqSNAaIyhLkVwh9cGuWBywCBbuw/view?usp=sharing)
311 |   :open_book: [The Elements of Statistical Learning](https://drive.google.com/file/d/1ePRkuB9Zm5Fkw-1-VG8prQXfj8pI6dWX/view?usp=sharing)
312 |   :open_book: [Pattern Recognition and Machine Learning - Bishop](https://drive.google.com/file/d/1QkQj_azL6O7qUzshB8lPzueYWj0TRwEu/view?usp=sharing) (Advanced)
313 | #####    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). 314 | *** 315 | 316 | ### :pushpin:PROJECTS :arrow_double_down:
317 |    :movie_camera:[Deena Gergis - End to end Project](https://www.youtube.com/playlist?list=PLatl6hdtJ0RnbkReSAuel6PeCPO155FpG)
318 |    :movie_camera:[Machine Learning Projects - Youtube](https://www.youtube.com/watch?v=fiz1ORTBGpY&list=PLfFghEzKVmjvuSA67LszN1dZ-Dd_pkus6)
319 |    :computer:[Top 10 Data Science Projects for Beginners](https://www.kdnuggets.com/2021/06/top-10-data-science-projects-beginners.html)
320 |    :computer:[12 Data Science Projects for Beginners and Experts](https://builtin.com/data-science/data-science-projects)
321 |    :computer:[Data Science Projects & Ideas](https://nevonprojects.com/data-science-projects-solutions/)
322 |    :computer:[Top 310+ Machine Learning Projects for 2023](https://data-flair.training/blogs/machine-learning-project-ideas/)
323 |    :computer:[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)
324 |    :movie_camera:[Real-World ML Tutorial w/ Scikit Learn](https://www.youtube.com/watch?v=M9Itm95JzL0)
325 |    :movie_camera:[End To End ML Project With Dockers,Github Actions And Deployment](https://www.youtube.com/watch?v=MJ1vWb1rGwM)
326 |    :computer:[Python Codes in Data Science](https://github.com/RubensZimbres/Repo-2017/)
   :computer:[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)
327 | 328 | *** 329 | 330 | ### :pushpin: Common Tools :arrow_heading_down:
331 |    :closed_book:[Git](https://git-scm.com/book/en/v2) 332 |   :movie_camera:[Git - Udacity](https://www.udacity.com/course/version-control-with-git--ud123) 333 |   :movie_camera:[Arabic - Youtube](https://www.youtube.com/watch?v=Q6G-J54vgKc)
334 |     - [Anaconda](https://learn.datacamp.com/courses/conda-essentials)
335 | 336 | 337 | ### :pushpin: **More Books ~ [:pushpin: Check This!](https://drive.google.com/drive/folders/1iW7IPrVUqsHumgXUMH_rgeBLpJjRDCmJ?usp=sharing)**
338 |   :atom::atom::atom::atom::atom:
339 |    :closed_book: [:fire: 65 Free Important Books :fire:](https://techgrabyte.com/springer-released-65-machine-learning-data-science-books-free/?fbclid=IwAR3x9Mfd4FSrBo_y2F92cAghhZ8851qv3wQ1R_u87s70MZTDtKeG5yn7Hts)
340 |    :closed_book: [Mathematics for Machine Learning ](https://mml-book.github.io/)
341 |    :closed_book: [An Introduction to Statistical Learning](https://www.statlearning.com/)
342 |    :closed_book: [Understanding Machine Learning: From Theory to Algorithms ](https://www.cs.huji.ac.il/w~shais/UnderstandingMachineLearning/copy.html)
343 |    :closed_book: [Probabilistic Machine Learning: An Introduction](https://probml.github.io/pml-book/book1.html)
344 |    :closed_book: [storytelling with data](https://drive.google.com/file/d/1OQu6ZWImGnHbuI_WJOLPdSvKWCABSWMH/view?usp=sharing) Important data visualization guide.
345 | 346 | *** 347 |

348 |

:pushpin: Collection of the best Cheat sheets

 349 | 350 | 1. [Importing Data](https://lnkd.in/e3jnyTEi) 351 | 352 | 2. Pandas 353 | 354 |    - [(1)](https://lnkd.in/eiXuBbWh_) 355 |    - [(2)](https://lnkd.in/e8PKwQQQ) 356 |    - [(3)](https://lnkd.in/ewQfqe8q) 357 | 358 | 3. [Matplotlib](https://lnkd.in/ejxbW8ak) 359 | 360 | 4. [Seaborn](https://lnkd.in/ejhxUp2K) 361 | 362 | 5. [Probability](https://lnkd.in/e4Jxx6xP) 363 | 364 | 6. [Supervised Learning](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf) 365 | 366 | 7. [Unsupervised Learning](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf) 367 | 368 | 8. [Deep Learning](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf) 369 | 370 | 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) 371 | 372 | 10. [Probabilities and Statistics](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf) 373 | 374 | 11. [Comprehensive Stanford Master Cheat Sheet](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf) 375 | 376 | 12. [Linear Algebra and Calculus](https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf) 377 | 378 | 13. [Data Science Cheat Sheet](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf) 379 | 380 | 14. [Keras Cheat Sheet](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf) 381 | 382 | 15. [Deep Learning with Keras Cheat Sheet](https://github.com/rstudio/cheatsheets/raw/master/keras.pdf) 383 | 384 | 16. [Visual Guide to Neural Network Infrastructures](http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png) 385 | 386 | 17. [Skicit-Learn Python Cheat Sheet](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf) 387 | 388 | 18. [Scikit-learn Cheat Sheet: Choosing the Right Estimator](https://scikit-learn.org/stable/tutorial/machine_learning_map/) 389 | 390 | 19. [Tensorflow Cheat Sheet](https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf) 391 | 392 | 20. [Machine Learning Test Cheat Sheet](https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/) 393 | 394 | 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*
395 | *** 396 | 397 | ### The best way to practice is to take part in competitions. :trophy::trophy:
398 | **Competitions will make you even more proficient in Data Science.**
399 | 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.
400 | 401 | **You can also check these platforms for data science competitions-**
402 | - [Driven Data](https://www.drivendata.org/competitions/)
403 | - [Codalab](https://competitions.codalab.org/)
404 | - [Iron Viz](https://www.tableau.com/community/iron-viz)
405 | - [Topcoder](https://www.topcoder.com/challenges)
406 | - [CrowdANALYTIX Community](https://www.crowdanalytix.com/community)
407 | - [Bitgrit](https://bitgrit.net/)
408 | 409 | 410 | *** 411 | :notebook: Data Science Interview Questions: :arrow_forward: 412 |   - [(1)](https://github.com/youssefHosni/Data-Science-Interview-Questions) 413 |  - [(2)](https://github.com/alexeygrigorev/data-science-interviews) 414 |  - [(3)](https://github.com/rbhatia46/Data-Science-Interview-Resources) 415 |  - [(4)](https://github.com/iamtodor/data-science-interview-questions-and-answers) 416 |  - [(5)](https://github.com/milaan9/DataScience_Interview_Questions) 417 |  - [(6) Arabic Podcast](https://www.youtube.com/watch?v=YjloQOreudk):headphones:
418 |                     - [(7) 30 days of interview preparation](https://github.com/Moataz-Elmesmary/Data-Science-Roadmap/blob/main/30%20days%20of%20interview%20preparation.pdf):book: 419 | 420 | *** 421 | :pushpin: **Data Analysis Recommendations.**
422 |     Books (:closed_book: [The Data Analysis Workshop](https://drive.google.com/file/d/1BjKsffA2SCY0jY8OIIzgQgM0ZS7E9v_v/view?fbclid=IwAR2_GBlrX7VYoo8WCRO9R2qqrYEqtytoGrObxy1QHWcQ7sRaFjRLb0GmuxM) & 423 | :closed_book: [Head First Data Analysis](https://drive.google.com/file/d/1HXHkwrgsSJLYSeB6I0wPUXIGGnm2-HQ6/view?fbclid=IwAR27M-dlPN6o0YuZg3bXH6_DP9L2fBhkKDEkChvO4SPG-SXfkxrzuoGP5RM))
424 |     [FWD - (The 3 Levels)](https://egfwd.com/?fbclid=IwAR1phYmHHgi0L4E9nOPZcSfAdHWsDs9EvBh3dJgO6gXN4B1A-nV8vspGggs)
425 |     [Google Data Analytics Professional Certificate](https://www.coursera.org/professional-certificates/google-data-analytics)
426 |     [IBM Data Analyst Professional Certificate](https://www.coursera.org/professional-certificates/ibm-data-analyst?fbclid=IwAR1IajEEe2yydVWRt3hbj4qLioXP6oR-fdbw8f1kHAVpAXSA4Z8Eww1Y-fs)
427 |     [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)
428 |    *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*.
429 |      - Excel More Resources: ([Arabic 1:video_camera:](https://www.youtube.com/watch?v=9Z5MPeyuLhg&t=397s) - [Arabic 2:video_camera:](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))
430 |

431 | 432 | 433 | :pushpin: **[Data Engineering](https://youtu.be/qWru-b6m030) Recommendations.**
434 |     Books (:closed_book: [Fundamentals of Data Engineering](https://drive.google.com/file/d/1CbQFN0Lw8o6v4KlF64LsCyaooMccT45T/view?usp=sharing) & 435 | :closed_book: [Designing Data-Intensive Applications](https://drive.google.com/file/d/1CrzA--WWNcxxQwLqzg1yPfiI3FaEo49z/view?usp=sharing))
436 |     Arabic Podcast, [Starting a Career in Data Engineering.](https://www.youtube.com/watch?v=OtaBhXjrbX4)
437 |     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))
438 |     [Roadmap 1](https://www.educba.com/data-engineer-roadmap/)
439 |     [Roadmap 2](https://github.com/datastacktv/data-engineer-roadmap)
440 |     [IBM Data Engineering Professional Certificate](https://www.coursera.org/professional-certificates/ibm-data-engineer)
441 |    *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*.
442 | *** 443 |

:file_folder: CV / Resumes :memo:   444 | 445 | 446 | 447 | 448 | 449 | - [Common mistakes by Yehia Arafa Mostafa](https://www.facebook.com/yehia.arafa.mostafa/posts/110086229517000)
450 | - [CV Tips by Omar Yasser](https://medium.com/@oyaraouf/cv-tips-5faaec55ec07)
451 | - [This Is What A GOOD Resume Should Look Like by careercup](https://www.careercup.com/resume)
452 | - 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)
453 | - [After Graduation by Yasser Alaa](https://www.linkedin.com/feed/update/urn:li:activity:6964595411839799296/)
454 | - [How to make Data Science Resume](https://enhancv.com/resume-examples/data-scientist/)
455 | - [Data Science Resume Guide](https://www.beamjobs.com/resumes/data-science-resume-example-guide)
456 | - Resume/CV building for Data Jobs (Arabic)
457 |   :video_camera:[Video 1](https://www.youtube.com/watch?v=R0hsJiNxdDE)
458 |   :video_camera:[Video 2](https://www.youtube.com/watch?v=CrTO0hrC-zQ) 459 |
460 | 461 | *** 462 | :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) 463 | *** 464 | 465 |

Contact Me :iphone:


466 | 467 | 468 | 469 |

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