├── General Roadmap.md ├── README.md └── Roadmap Into Weeks.md /General Roadmap.md: -------------------------------------------------------------------------------- 1 |

General Roadmap

2 | 3 | ## Here you have the full roadmap divided into three levels as headlines and each one has some suggested courses..
4 | 5 | # ▶ What Is Data Science? 6 | 7 | #### Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value...... 8 | 9 | #### [For more information about Data Science :movie_camera:](https://youtu.be/X3paOmcrTjQ) 10 | 11 | #### ⚠️ I have a knot in mathematics, Do I enter this field? [ Check this](https://docs.google.com/document/d/1ljekCa5z9mkLHLN-itCseFff5SZeYdvVkBNzVVUyHM8/edit?usp=drivesdk) 12 | 13 | #### :bulb: If You Don’t Know What’s the Difference Between A Data Scientist, Data Analyst, Data Engineer, ML Engineer? [ Watch this](https://youtu.be/SLszG6sSInY) 14 | 15 | 16 | #### 📌 For Data Camp courses, the GitHub student pack gives 3 free months.[ How to get it](https://youtu.be/owO75M1Xv30) || [Register Here](https://education.github.com/pack) 17 | 18 | ## ▶ Before starting you should know these tips : 19 | #### :one: :**If you need to climb stairs, you must tread the first stair.**.....So the “Computer science fundamentals” are the first stair in CS stairs, It will help you to understand how the computer works, how to deal with data, how to deal with code, and many other things you should know to go freely on your career. Whatever the CS technology you choose to learn you need these fundamentals.
Many people who decided to start directly learning one of CS technology suffer from a Lack of some skills and information and they waste their time to go back and learn what they need and the process is repeated continuously.
20 | 21 | #### :two: :Here you have the main topics and for each one, there are some suggested courses that you should take one or more of them..
22 | 23 | #### :three: :Notice that any roadmap is not sacred, you may find some courses better than the suggested ones from your perspective and that's completely okay, so Choose the courses that suit you but you should go through all the topics.
24 | 25 | #### :four: :To get a better result, it is preferable to read a pdf or a book from those below in parallel with the course you choose. 26 | 27 | #### :five: :Don’t chase certifications. 28 | #### :six: :Don’t rush for ML without having a good background in programming & maths. 29 | #### :seven: :At the end of every level apply all that you've learned on some projects. 30 | 31 | 32 | # So let’s start our journey with preparing your workspace 🚀 33 |

:bell: Pick one and stick to it.(👉🏻Click👈🏻)

34 | 35 |
36 | 37 | [

**Anaconda**

](https://www.anaconda.com/products/distribution)Anaconda is 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). 38 | * [A guide toinstall anacondaa and run Jupyter Notebook.](https://www.youtube.com/watch?v=syijLJ3oQzU) 39 | * [A guide to deal with Jupyter Notebook.](https://www.youtube.com/watch?v=0nrjHslIvT4) 40 | * [Conda Essentials datacampp).](https://learn.datacamp.com/courses/conda-essentials) 41 | 42 | ![a](https://user-images.githubusercontent.com/92026137/163717819-4689c927-6022-47da-b204-169693bfa397.png) 43 | 44 | 45 | 46 | [

**Google Colab**

](https://colab.research.google.com/notebooks/intro.ipynb)Google Colab is 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
47 | 48 | [

**PyCharm**

](https://www.jetbrains.com/pycharm/)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. 49 | * [JetBrains student license](https://www.youtube.com/watch?v=QPESX-VBnEU) can let you freely install the paid professional version of Pycharm for educational purposes only. 50 | * [A Guide to install and use Pycharm](https://www.youtube.com/watch?v=NJXwP7o5TTM) 51 | * [Running Jupyter Notebook from Pycharm](https://www.youtube.com/watch?v=yr1M1H-GLR0)
52 | 53 | [

**VScode**

](https://code.visualstudio.com/download)Visual Studio Code is a lightweight but powerful source code editor which runs on your desktop. It has a rich ecosystem of extensions for other languages and runtimes (such as C++, C#, Java, Python, PHP, Go, and NET). You can also run Jupyter Notebook from it. 54 | * [A guide to install and use VScode](https://www.youtube.com/watch?v=kE_FzZk3jGo) 55 | * [Running Jupyter Notebook from VScode](https://www.youtube.com/watch?v=h1sAzPojKMg) 56 | 57 | 58 |
59 | 60 | 61 | ## 🔰 Beginner Level 👇 62 | 63 | > I will recommend `Python`, although you may encounter `R` in more Data Analytics related jobs. Python mastery will come with time - learn enough basics to be able 64 | > to read code and implement. 65 | Learning Python will require time to reiterate several times to understand a concept, & trust me it's worth it. As said learning Data science requires time and learning the hard way rather than shortcuts which will make you nowhere. So, don't get demotivated if you're not able to understand a concept, just keep trying and you'll get it.
66 | 67 | 68 | | Topics | Resources & Links | 69 | | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------| 70 | |    **1.Intro To Python✨** | 🎥[Udacity](https://www.udacity.com/course/introduction-to-python--ud1110)
🎥[Corey Schafer](https://www.youtube.com/watch?v=YYXdXT2l-Gg&list=PL-osiE80TeTt2d9bfVyTiXJA-UTHn6WwU)
🎥[Arabic Course](https://youtube.com/playlist?list=PLuXY3ddo_8nzrO74UeZQVZOb5-wIS6krJ)
[📑Pdf ](https://drive.google.com/file/d/1xhO5X5iGJeottX2dc2gNejQll0sTBgHz/view?usp=drivesdk) | 71 | | Intermediate Python | 🎥[Corey Schafer](https://www.youtube.com/watch?v=ZDa-Z5JzLYM&list=PL-osiE80TeTsqhIuOqKhwlXsIBIdSeYtc)
🎥[Python Functions, Files, and Dictionaries](https://www.coursera.org/learn/python-functions-files-dictionaries) | 72 | | Advanced Python | 🎥[Durga Sir](https://www.youtube.com/watch?v=es457q7n3P8&list=PLd3UqWTnYXOkzPunQOObl4m_7i6aOIoQD) | 73 | | OOP with Python | 🎥[Python Classes and Inheritance](https://www.coursera.org/learn/python-classes-inheritance) | 74 | | Intro to cs with Python | 🎥[MIT](https://bit.ly/3hV3rqj) | 75 | | Data Structures and Algorithms | 📑[Data Structures and Algorithms BOOK](http://xpzhang.me/teach/DS19_Fall/book.pdf)
🎥[Introduction to Algorithms(MIT)](https://ocw.mit.edu/courses/6-006-introduction-to-algorithms-spring-2020/) | 76 | | Regular Expression | 📑[Tutorial](https://www.datacamp.com/community/tutorials/python-regular-expression-tutorial) | 77 | |  **2. Descriptive Statistics 📊** | 🎥[Udacity](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827)
🎥[YouTube playlist](https://www.youtube.com/playlist?list=PLTNMv857s9WVStKLco6ZBOsfSGXzJ1L0f)
🎥[Arabic Course 1](https://youtu.be/NyCqaxLW3p8)
🎥[Arabic Course 2](https://www.youtube.com/playlist?list=PLu1nnZ4q7vDHfhYtBLjxrAhh5IYX0v4KO)
🎥[Statistics Fundamentals StatQuest](https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9)
📑[Pdf](https://drive.google.com/file/d/1C4RMwG5HphNAHgYjzif7N_7Eb_w_IDAn/view?usp=sharing) | 78 | |     **3. Analysis**
    **🔹Pandas 🐼**|🎥[Corey Schafer](https://www.youtube.com/watch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS)
🎥[Arabic Course](https://www.youtube.com/watch?v=3ISW655DemU&list=PLvLvlVqNQGHCb2_ygmr1DQOMOv0yXp84F)
🎥[Google Course(Data Analysis)](https://bit.ly/3pR0480)
📑[Kaggle Course](https://www.kaggle.com/learn/pandas)
📑[Kaggle Pandas 100 tricks](https://www.kaggle.com/code/shivan118/pandas-100-tricks)
📑[Pandas user Guide](https://pandas.pydata.org/docs/user_guide/index.html#user-guide)
📑[Getting started with Pandas](https://pandas.pydata.org/docs/getting_started/intro_tutorials/index.html) | 79 | |     **🔹Numpy 🔢** | 🎥[DataCamp](https://app.datacamp.com/learn/courses/introduction-to-numpy)
🎥[YouTube freeCodeCamp](https://www.youtube.com/watch?v=QUT1VHiLmmI)
🎥[Arabic Course](https://www.youtube.com/watch?v=5-5CrLmf2vk&list=PLIA_seGogbkGDYq-dnVCsELEIq_7HK7Ca)
📑[Kaggle](https://www.kaggle.com/legendadnan/numpy-tutorial-for-beginners-data-science)
📑[Tutorial](http://cs231n.github.io/python-numpy-tutorial/)
📑[Numpy DOC](https://numpy.org/learn/) | 80 | |    **🔹Data Cleaning 🧹
    [(👉🏻Click👈🏻)](https://towardsdatascience.com/the-ultimate-guide-to-data-cleaning-3969843991d4)**| 🎥[Datacamp](https://www.datacamp.com/courses/cleaning-data-in-python)
🎥[Arabic video](https://www.youtube.com/watch?v=Mrd56i_U6cM) **Not enough**
📑[Kaggle Course](https://www.kaggle.com/learn/data-cleaning)
📑[Pandas 100 tricks](https://www.kaggle.com/code/shivan118/pandas-100-tricks) **Already mentioned in pandas**
📑[Cleaning Blog](https://bit.ly/3vXqybR) | 81 | | **🔹Data Visualization & EDA 📉**| 🎥[Ask Questions (from google specialization)](https://www.coursera.org/learn/ask-questions-make-decisions?specialization=google-data-analytics)
🎥[Understanding and Visualizing with Python](https://www.coursera.org/learn/understanding-visualization-data)
🎥[Matplotlib (data camp)](https://app.datacamp.com/learn/courses/introduction-to-data-visualization-with-matplotlib)
🎥[Corey Schafer - Matplotlib](https://www.youtube.com/watch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_)
🎥[Matplotlib sentdex](https://www.youtube.com/watch?v=q7Bo_J8x_dw&list=PLQVvvaa0QuDfefDfXb9Yf0la1fPDKluPF)
🎥[Intro to seaborn (data camp)](https://bit.ly/3KprvxE)
🎥[Intermediate in seaborn(data camp)](https://app.datacamp.com/learn/courses/intermediate-data-visualization-with-seaborn )
🎥 [Seaborn Youtube](https://www.youtube.com/watch?v=vaf4ir8eT38&list=PLtPIclEQf-3cG31dxSMZ8KTcDG7zYng1j&index=3)
🎥[Improving Your Data Visualizations(data camp)](https://app.datacamp.com/learn/courses/improving-your-data-visualizations-in-python)
🎥[EDA (data camp)](https://bit.ly/3KChY6z )
📑[Matplotlib Tutorial](https://matplotlib.org/stable/tutorials/index.html)
📑[How to ask good questions?](https://towardsdatascience.com/how-to-ask-good-questions-be41b517c1b)
📑[Kaggle Course with Seaborn](https://www.kaggle.com/learn/data-visualization)
| 82 | |    **🔹Dashboards**
  **(Tableau & Power BI)🗺️**|🎥[Tableau training](https://www.tableau.com/learn/training/20201)
🎥[Tableau(data camp)](https://learn.datacamp.com/courses/introduction-to-tableau)
🎥[Data Visualization with Tableau Specialization](https://www.coursera.org/specializations/data-visualization)
🎥[Tableau (Udacity)](https://www.udacity.com/course/data-visualization-in-tableau--ud1006 )
🎥[Tableau and visualizing (from google specialization)](https://www.coursera.org/learn/visualize-data?specialization=google-data-analytics)
📑[Tableau Tutorial](https://www.datacamp.com/community/tutorials/data-visualisation-tableau)
🎥[Power BI training](https://powerbi.microsoft.com/en-us/learning/)
🎥[Power BI (Coursera)](https://www.coursera.org/projects/power-bi-desktop)
🎥[Arabic - Power BI](https://www.youtube.com/watch?v=ykvAWKML9Gk&list=PLof3yw6ZFPFhV75Ptf-5Q88bgUtLOBvOw)
🎥[Tableau & Power BI(sessions)](https://drive.google.com/drive/folders/1gwxJtxsshN0pQHXzLhkl4Syw42Go11-n)
| 83 | |     **🔹SQL & DB**|🎥[Introduction to SQL(data camp)](https://app.datacamp.com/learn/courses/introduction-to-sql)
🎥[Intro to Relational Databases DB(data camp)](https://app.datacamp.com/learn/courses/introduction-to-relational-databases-in-sql)
🎥[Databases and SQL for Data IBM](https://www.coursera.org/learn/sql-data-science)
🎥[Joining Data in SQL (data camp)](https://www.datacamp.com/courses/joining-data-in-postgresql)
🎥[freeCodeCamp](https://youtu.be/HXV3zeQKqGY)
🎥[Arabic_Course](https://youtu.be/B7evUQGmN6M)
🎥[Intermediate SQL (data camp)](https://bit.ly/3J09xBx)
🎥[More advanced SQL(Coursera)](https://www.coursera.org/lecture/data-driven-astronomy/more-advanced-sql-GDmo5)
📝 Practice [SQLZOO practice & solution](https://github.com/codyloyd/sqlzoo-solutions/blob/master/SQLZOO_solutions.md#join)
📝 Practice [HackerRank](https://www.hackerrank.com/domains/sql)
📝 Practice [SQL Cookbook By Anthony Molinaro](https://learning.oreilly.com/library/view/sql-cookbook/0596009763/)
📝 Practice [DataLemur](https://datalemur.com/)
| 84 | 85 | ## 🔰 Intermediate Level 👇 86 | 87 | | Topics | Resources & Links | 88 | | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------| 89 | |       **1.Git**|🎥[Udacity course](https://classroom.udacity.com/courses/ud123)
📑[Git Tutorial for Absolute Beginners from Zero to Hero](https://youtube.com/playlist?list=PLJGDHERh23x_4MDp0m4Arswm5VErr3tbl)
🎥[Git (session)](https://drive.google.com/drive/folders/14qy8O3XsKmaSGUfd5jGyFFoAcqcSCxoZ?usp=sharing)
🎥[Arabic Youtube](https://www.youtube.com/watch?v=Q6G-J54vgKc)
| 90 | |  **2. Web Scraping & APIs**|🎥[Intro to web scraping (data camp)](https://bit.ly/3CuNyjE)
🎥[Intermediate Importing Data (data camp)](https://bit.ly/37kqrgi)
📑[Web Scraping with Python Using Beautiful Soup](https://www.dataquest.io/blog/web-scraping-tutorial-python/)
📑[Getting Started with APIs](https://www.dataquest.io/blog/python-api-tutorial/)
📑[Medium](https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-pull-data-from-an-api-using-python-requests-edcc8d6441b1)
📑[Rapidapi](https://rapidapi.com/blog/how-to-use-an-api-with-python/)
| 91 | |     **3.Time-Series**|📑[Tutorial(Prophet)](https://facebook.github.io/prophet/docs/quick_start.html)
🎥[Time Series with Python(data camp)](https://learn.datacamp.com/skill-tracks/time-series-with-python)
🎥[Arabic Course(Hesham Asem)1](https://www.youtube.com/watch?v=TvhaHPq6xLU&list=TLPQMjYwNzIwMjEPGXX6392WJA&index=1)
| 92 | |  **4. Math for Machine Learning**|🎥[Mathematics for Machine Learning Specialization](https://www.coursera.org/specializations/mathematics-machine-learning)
🎥[Mathematics for Machine Learning](https://www.youtube.com/watch?v=vLJcduC4lBM&list=PLcQCwsZDEzFmlSc6levE3UV9rZ8yY-D_7)
🎥[Probability (Khanacademy)](https://bit.ly/379gylk)
🎥[Probability MIT playlist]( https://bit.ly/3MFxaBC)
🎥[Linear algebra for ML Coursera specialization]( https://bit.ly/3pToX32)
🎥[ Multivariate Calculus ](https://www.coursera.org/learn/multivariate-calculus-machine-learning?fbclid=IwAR243aoz0jxs4iUn539pnjSQliXtr7Y5QAsvgeRTietZT_tkyoRU3b6Sq1o)
| 93 | |    **5. Machine Learning**|🎥[Machine learning andrew](https://bit.ly/3u5zfhV)
🎥[Machine learning Udacity]( https://bit.ly/3J2j7Uu)
🎥[Machine learning IBM](https://www.coursera.org/learn/machine-learning-with-python?specialization=ai-engineer&fbclid=IwAR35JlCKXk3OdCYVRFnK_pRXmiko5CHO7lKk5rLld8M3A9McbtIVPDn6AFs)
📕Hands on ML book([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 + [Notebooks](https://github.com/ageron/handson-ml?fbclid=IwAR3s31KlwkLKyrEwuEd4UMOcvHN1Q9Z2LLGzPg5vP4UKSwjriHxU0uO405c)
| 94 | |  **6. Feature Engineering**|📑[Tutorial](https://www.kaggle.com/learn/feature-engineering)
📑[Tutorial](https://www.kaggle.com/learn/feature-engineering)
📕[Feature Engineering for Machine Learning](https://drive.google.com/file/d/1BkJYO0tqMYptTWUDQ7X0vd2aygohHRm8/view?usp=sharing)
| 95 | 96 | ## 🔰 Advanced Level 👇 97 | 98 | | Topics | Resources & Links | 99 | | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------| 100 | |    **1. Deep Learning**|🎥[Deep learning specialization Andrew](https://bit.ly/3I1c396)
🎥[Deep learning book](https://bit.ly/3I3lt49)
| 101 | |   **2. Computer Vision**|🎥[Stanford playlist](https://www.youtube.com/playlist?list=PLf7L7Kg8_FNxHATtLwDceyh72QQL9pvpQ)
🎥[Stanford](https://stanford.io/3HZaJDJ)
| 102 | |      **3. NLP**|🎥[NLP Stanford](https://stanford.io/3pUcw76)
🎥[CS224n: Natural Language Processing with Deep Learning](https://web.stanford.edu/class/cs224n/)
🎥[NLP Coursera specialization](https://bit.ly/3vXkU9M)
| 103 | |     **4. Spark**|🎥[Spark (Udacity)](https://bit.ly/3sYKFUZ )
| 104 | |   **5. Data Warehouse**|🎥[Data Warehouse Concepts (Coursera)](https://www.coursera.org/learn/dwdesign)
| 105 | |  **7. Inferential Statistics**|🎥[Specialization, 2nd & 3rd courses](https://www.coursera.org/specializations/statistics-with-python)
🎥[Coursera](https://www.coursera.org/learn/statistical-inferences)
🎥[Udacity](https://bit.ly/37a34FY)
| 106 | |  **8. Model Deployment**|📑[Flask tutorial](https://towardsdatascience.com/deploying-a-deep-learning-model-using-flask-3ec166ef59fb)
🎥[TensorFlow: Data and Deployment Specialization](https://www.coursera.org/specializations/tensorflow-data-and-deployment)
🎥[Deploy Models with TensorFlow Serving and Flask](https://www.coursera.org/projects/deploy-models-tensorflow-serving-flask)
🎥[How to Deploy a Machine Learning Model to Google Cloud - Daniel Bourke](https://www.youtube.com/watch?v=fw6NMQrYc6w)
**if you`re interested in more deployment methods, search for (_FastAPI - Heroku - chitra_)
**| 107 | |**9. Probabilistic Graphical Models**|🎥[Specialization](https://www.coursera.org/specializations/probabilistic-graphical-models)
| 108 | 109 | 110 | 111 | 112 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 |

Data Science Roadmap-IEEE-2023

3 | 4 | ## Here we have two files: 5 | * Roadmap: Contains the full roadmap as headlines and each one has some suggested courses.
6 | * Roadmap_into_weeks (recommended): The same roadmap, but a specific course was chosen in each subject and it was divided into weeks, at a studying rate of 7 hours per week. 7 |
8 | 9 | 10 | ## Contact Me :iphone:
11 | 12 | 13 | 14 | 15 | 16 | -------------------------------------------------------------------------------- /Roadmap Into Weeks.md: -------------------------------------------------------------------------------- 1 |

Roadmap Into Weeks

2 | 3 | 4 | ## It's the same roadmap, but divided into weeks with an average studying rate of 6 hours per week. 5 | 6 | ### This roadmap is divided into 4 stages: 7 | ### 1. Stage 1: you get a basic understanding of the prerequisites, data cleaning, and git. 8 | ### 2. Stage 2: learn visualizing, Tableau, SQL, and web scraping. 9 | ### 3. Stage 3: dive into ML and Math 10 | ### 4. Stage 4: where we learn DL, CV, and NLP. 11 | 12 |
13 | 14 | > You should make a task after each week, and some projects after each stage. 15 | 16 | ## Stage 1 17 | ### Week 1 : 18 | [Python-Basics (Full Course)](https://www.coursera.org/learn/python-basics)
19 | [Task](https://colab.research.google.com/drive/1N_sxUfzFwAEQmqVuOxMfaRGxFuw9DnPm?usp=sharing)
20 | ### Week 2: 21 | [Python Functions, Files, and Dictionaries](https://www.coursera.org/learn/python-functions-files-dictionaries)
22 | [Task](https://colab.research.google.com/drive/1tDl_jyCxT74xTXSwh9s8klGESs0TdfD3?usp=sharing)
23 | 24 | ### Week 3: 25 | [Python-OOP (1st week only)](https://www.coursera.org/learn/python-classes-inheritance)
26 | [Descriptive stats](https://www.youtube.com/watch?v=NyCqaxLW3p8)
27 | [Task](https://colab.research.google.com/drive/1Dfs7jlyq0cOksL42hRMzYW3cEmkaQ2Wa?usp=sharing)
28 | ### Week 4: 29 | [Finish Python-OOP](https://www.coursera.org/learn/python-classes-inheritance)
30 | [Numpy (Full course)](https://app.datacamp.com/learn/courses/introduction-to-numpy)
31 | [Numpy DOC](https://numpy.org/learn/)
32 | [Task](https://colab.research.google.com/drive/1EQtnzLJ5liX4o3FJJEcca6hk42fb-fNr?usp=sharing#ProblemSolving)
33 | 34 | ### Week 5: 35 | 6 videos of [Corey playlist](https://www.youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS )
36 | [Task](https://colab.research.google.com/drive/1CZMvyh9zqWJDTC3uJAJrXIsV7TWG4HFG?usp=sharing)
37 | 38 | ### Week 6: 39 | 7 → videos of [Corey playlist](https://www.youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS )
40 | [Git (session)](https://drive.google.com/drive/folders/14qy8O3XsKmaSGUfd5jGyFFoAcqcSCxoZ?usp=sharing)
41 | [Task](https://github.com/Nordiniv/Beginner/blob/main/Pandas%20and%20Git.md)
42 | [Try to explore this data](https://www.kaggle.com/datasets/rkiattisak/mobile-phone-price)
43 | [notebook](https://www.kaggle.com/code/mennatullahelsahy/mobile-phone-price)
44 | 45 | 46 | ### Week 7: 47 | [Cleaning (data camp)](https://app.datacamp.com/learn/courses/cleaning-data-in-python)
48 | [The Ultimate Guide to Data Cleaning](https://bit.ly/3vXqybR )
49 | [Cleaning Kaggle](https://www.kaggle.com/learn/data-cleaning)
50 | [Course Summary](https://github.com/Ahmed-Hereiz/My-Summmary/tree/main/Data%20cleaning)
51 | [Task(Try to clean this data)](https://drive.google.com/drive/folders/1Nxi3h7cxIFDJ1Zr8LajnHYDA2cuLDAK2)
52 | [useful repo for cleaning](https://github.com/7MustafaAdelIbrahim/Data-cleaning)
53 | 54 | 55 | ### Week 8: 56 | [Exploratory Data Analysis in Python (DataCamp Course)](https://app.datacamp.com/learn/courses/exploratory-data-analysis-in-python)
57 | [Visualizing (data camp Matplotlib)](https://app.datacamp.com/learn/courses/introduction-to-data-visualization-with-matplotlib)
58 | [Matplotlib Tutorial](https://matplotlib.org/stable/tutorials/index.html)
59 | [Task(Try to clean and visualize this data)](https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand)
60 | [notebook](https://www.kaggle.com/code/mennatullahelsahy/hotel-booking-analysis)
61 | 62 | ## Stage 2 63 | ### Week 9: 64 | [Visualizing Intro to seaborn (data camp)](https://app.datacamp.com/learn/courses/introduction-to-data-visualization-with-seaborn )
65 | [Intermediate in seaborn(data camp)](https://app.datacamp.com/learn/courses/intermediate-data-visualization-with-seaborn )
66 | [Improving Your Data Visualizations in Python](https://app.datacamp.com/learn/courses/improving-your-data-visualizations-in-python)
67 | [Task(Try to clean and visualize this data)](https://www.kaggle.com/datasets/disham993/9000-movies-dataset)
68 | [notebook](https://www.kaggle.com/code/mennatullahelsahy/movies-data-analysis)
69 | 70 | ### Week 10: 71 | [Ask Questions (from Google specialization)](https://www.coursera.org/learn/ask-questions-make-decisions?specialization=google-data-analytics)
72 | [Task](https://colab.research.google.com/drive/1oTUmufyuUW-GViilsfWCB-1sigYMl3Wh?usp=sharing)
73 | 74 | ### Week 11: 75 | [Tableau and visualizing (from Google specialization)](https://www.coursera.org/learn/visualize-data?specialization=google-data-analytics)
76 | [Task(Create a Dashboard with tableau )](https://public.tableau.com/s/sites/default/files/media/airbnb.xlsx)
77 | ### Week 12: 78 | [Intro to DB](https://app.datacamp.com/learn/courses/introduction-to-relational-databases-in-sql)
79 | [Intro to SQL](https://app.datacamp.com/learn/courses/introduction-to-sql)
80 | [Notebook](https://www.kaggle.com/code/dimarudov/data-analysis-using-sql)
81 | Task (Try to solve the following queries)
82 | 1 -https://www.hackerrank.com/challenges/select-all-sql?isFullScreen=true
83 | 2- https://www.hackerrank.com/challenges/japanese-cities-attributes/problem?isFullScreen=true
84 | 3- https://www.hackerrank.com/challenges/weather-observation-station-3/problem?isFullScreen=true
85 | 4- https://www.hackerrank.com/challenges/weather-observation-station-12?isFullScreen=true
86 | 5- https://www.hackerrank.com/challenges/binary-search-tree-1/problem?isFullScreen=true
87 | ### Week 13: 88 | [Intro to web scraping](https://drive.google.com/file/d/1kV0iewMJt0RHSYWAjJCaTNoD41wpfRXM/view?usp=sharing)
89 | [Web scraping](https://app.datacamp.com/learn/courses/web-scraping-with-python)
90 | [Task(Sacrap the following website and collect, Job title, Job skills, Job type (Full Time / Part Time), Company name, Company location and Post time )](https://wuzzuf.net/search/jobs/?a=spbg&q=data%20science)
91 | 92 | 93 | ### Week 14: 94 | [Linear Algebra](https://www.coursera.org/specializations/mathematics-machine-learning) [Or](https://www.coursera.org/learn/linear-algebra-machine-learning)
95 | [preprocessing 1](https://medium.com/python-in-plain-english/the-power-of-data-preprocessing-in-unlocking-the-true-potential-of-raw-data-176aa6dd64f0)
96 | [preprocessing 2](https://medium.com/gitconnected/the-importance-of-data-preprocessing-in-python-pandas-bfbc112ae28c?source=search_post---------6----------------------------)
97 | [preprocessing 3](https://medium.com/mlearning-ai/feature-selection-techniques-in-machine-learning-82c2123bd548)
98 | 99 | ## Stage 3 100 | ### Week 15 : 101 | [Inferential Stats](https://classroom.udacity.com/courses/ud201 )
102 | 103 | ### Week 16 : 104 | [Intro for Machine Udacity Course](https://www.coursera.org/learn/machine-learning-with-python?specialization=ai-engineer)
105 | [Calculus Course (week 1,2,3)](https://www.coursera.org/learn/multivariate-calculus-machine-learning)
106 | 107 | ### Week 17 : 108 | [Supervised Machine Learning Andrew (week 1)](https://www.coursera.org/learn/machine-learning)
109 | [Calculus Course (week 4,5)](https://www.coursera.org/learn/multivariate-calculus-machine-learning)
110 | [**optional** MIT Lectures](https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/pages/syllabus/)
111 | 112 | ### Week 18 : 113 | [Supervised Machine Learning Andrew (week 2)](https://www.coursera.org/learn/machine-learning)
114 | [Calculus Course (week 6)](https://www.coursera.org/learn/multivariate-calculus-machine-learning)
115 | [**optional** Machine Learning (Dr Hamed Tizhoosh)](https://www.youtube.com/watch?v=tExPpuk-UQ8&list=PLvan4zSb2RaoRGHbSP15RYrUycboAmmLL&index=2)
116 | 117 | ### Week 19 : 118 | [Supervised Machine Learning Andrew (week 3)](https://www.coursera.org/learn/machine-learning)
119 | 120 | ### Week 20 : 121 | [Advanced Learning Algorithms Andrew (week 1)](https://www.coursera.org/learn/advanced-learning-algorithms)
122 | 123 | ### Week 21 : 124 | [Advanced Learning Algorithms Andrew (week 2)](https://www.coursera.org/learn/advanced-learning-algorithms)
125 | 126 | ### Week 22 : 127 | [Advanced Learning Algorithms Andrew (week 3,4)](https://www.coursera.org/learn/advanced-learning-algorithms)
128 | 129 | ### Week 23 : 130 | First 2 weeks in[Unsupervised Learning, Recommenders, Reinforcement Learning Andrew](https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning)
131 | 132 | ### Week 24 : 133 | Finish [Unsupervised Learning, Recommenders, Reinforcement Learning Andrew](https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning)
134 | 135 | ### Week 25 : 136 | First 2 chapters in [Hands on ML book](https://learning-oreilly-com.montgomery.idm.oclc.org/library/view/hands-on-machine-learning/9781098125967/) use the following to get the book (56098000000101358 card number VQQy!Ng5DhR8j5i password )
137 | [Hegab Playlist](https://youtube.com/playlist?list=PL1YWN9bMt3ODJnCNW1WqJ46tXVMCgdwTI)
138 | 139 | ### Week 26 : 140 | Chapters 3,4 in [Hands on ML book](https://learning-oreilly-com.montgomery.idm.oclc.org/library/view/hands-on-machine-learning/9781098125967/) use the following to get the book (56098000000101358 card number VQQy!Ng5DhR8j5i password )
141 | [Hegab Playlist](https://youtube.com/playlist?list=PL1YWN9bMt3ODJnCNW1WqJ46tXVMCgdwTI)
142 | 143 | ### Week 27 : 144 | Chapters 5,6 in [Hands on ML book](https://learning-oreilly-com.montgomery.idm.oclc.org/library/view/hands-on-machine-learning/9781098125967/) use the following to get the book (56098000000101358 card number VQQy!Ng5DhR8j5i password )
145 | [Hegab Playlist](https://youtube.com/playlist?list=PL1YWN9bMt3ODJnCNW1WqJ46tXVMCgdwTI)
146 | 147 | ### Week 28 : 148 | Chapters 7,8 in [Hands on ML book](https://learning-oreilly-com.montgomery.idm.oclc.org/library/view/hands-on-machine-learning/9781098125967/) use the following to get the book (56098000000101358 card number VQQy!Ng5DhR8j5i password )
149 | [Hegab Playlist](https://youtube.com/playlist?list=PL1YWN9bMt3ODJnCNW1WqJ46tXVMCgdwTI)
150 | 151 | ### Week 29 : 152 | Chapter 9 in [Hands-on ML book](https://learning-oreilly-com.montgomery.idm.oclc.org/library/view/hands-on-machine-learning/9781098125967/) use the following to get the book (56098000000101358 card number VQQy!Ng5DhR8j5i password )
153 | [Hegab Playlist](https://youtube.com/playlist?list=PL1YWN9bMt3ODJnCNW1WqJ46tXVMCgdwTI)
154 | First part in [Probability](https://www.khanacademy.org/math/statistics-probability/probability-library)
155 | 156 | ### Week 30 : 157 | Finish [Probability](https://www.khanacademy.org/math/statistics-probability/probability-library)
158 | 159 | 160 | ## Stage 4 161 | ### Week 31 : 162 | [First 2 Weeks Neural Networks course](https://www.coursera.org/learn/neural-networks-deep-learning)
163 | 164 | ### Week 32 : 165 | [Finsh Neural Networks course](https://www.coursera.org/learn/neural-networks-deep-learning)
166 | 167 | ### Week 33 : 168 | [First week Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization](https://www.coursera.org/learn/deep-neural-network?specialization=deep-learning)
169 | 170 | ### Week 34 : 171 | [Second week Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization](https://www.coursera.org/learn/deep-neural-network?specialization=deep-learning)
172 | [Introduction to Deep Learning in Python](https://app.datacamp.com/learn/courses/introduction-to-deep-learning-in-python)
173 | 174 | ### Week 35 : 175 | [Finish week Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization](https://www.coursera.org/learn/deep-neural-network?specialization=deep-learning)
176 | 177 | ### Week 36 : 178 | [Structuring Machine Learning Projects Course](https://www.coursera.org/learn/machine-learning-projects)
179 | 180 | ### Week 37 : 181 | Chapter 1,2,3 in [Deep Learning with python](https://learning-oreilly-com.montgomery.idm.oclc.org/library/view/deep-learning-with/9781484227664/ACoverHTML.html) use the following to get the book (56098000000101358 card number VQQy!Ng5DhR8j5i password )
182 | 183 | ### Week 38 : 184 | Chapter 4,5 in [Deep Learning with python](https://learning-oreilly-com.montgomery.idm.oclc.org/library/view/deep-learning-with/9781484227664/ACoverHTML.html) use the following to get the book (56098000000101358 card number VQQy!Ng5DhR8j5i password )
185 | 186 | ### Week 39 : 187 | Chapter 6,7 in [Deep Learning with python](https://learning-oreilly-com.montgomery.idm.oclc.org/library/view/deep-learning-with/9781484227664/ACoverHTML.html) use the following to get the book (56098000000101358 card number VQQy!Ng5DhR8j5i password )
188 | 189 | ### Week 40 : 190 | [Convolutional Neural Networks Week 3, Week 4](https://www.coursera.org/learn/convolutional-neural-networks)
191 | 192 | ### Week 41 : 193 | Chapter 8,9 in [Deep Learning with python](https://learning-oreilly-com.montgomery.idm.oclc.org/library/view/deep-learning-with/9781484227664/ACoverHTML.html) use the following to get the book (56098000000101358 card number VQQy!Ng5DhR8j5i password )
194 | 195 | ### Week 42 : 196 | [Sequence Models Week 1, Week 2](https://www.coursera.org/learn/nlp-sequence-models)
197 | 198 | ### Week 44 : 199 | [Sequence Models Week 3, Week 4](https://www.coursera.org/learn/nlp-sequence-models)
200 | 201 | ### Week 43 : 202 | Chapter 10,11 in [Deep Learning with python](https://learning-oreilly-com.montgomery.idm.oclc.org/library/view/deep-learning-with/9781484227664/ACoverHTML.html) use the following to get the book (56098000000101358 card number VQQy!Ng5DhR8j5i password )
203 | 204 | ### Week 44: 205 | Chapter 12,13 in [Deep Learning with python](https://learning-oreilly-com.montgomery.idm.oclc.org/library/view/deep-learning-with/9781484227664/ACoverHTML.html) use the following to get the book (56098000000101358 card number VQQy!Ng5DhR8j5i password )
206 | 207 | > More to be added... 208 | 209 | --------------------------------------------------------------------------------