├── README.md
├── Roadmap.md
└── Roadmap_into_weeks.md


/README.md:
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 1 | # Data Science Roadmap
 2 | 
 3 | ## Here we have two files:
 4 | * Roadmap:  Contains the full roadmap as headlines and each one has some suggested courses. <br>
 5 | * 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.
 6 | <br>
 7 | 
 8 | ## Important links
 9 | - We use *DataCamp* courses in our roadmap, so if you are not in an organization that supports the access, you still can have 3 free months from
10 | [GitHub student package](https://docs.github.com/en/education/explore-the-benefits-of-teaching-and-learning-with-github-education/github-global-campus-for-students/apply-to-github-global-campus-as-a-student)
11 | - To get access to *O'Reilly* books and a lot more books `for free` you can make these steps described in this [video](https://www.awesomescreenshot.com/video/16346202?key=891302ebf42290c51796f8da0c2ae251)
12 | 
13 | # Contact me
14 | 
15 | <a href="https://www.facebook.com/MahMoud0Hegab" title="Facebook"><img src="https://img.shields.io/badge/Facebook-%234267B2?style=flat&logo=Facebook&logoColor=white"/></a>
16 | <a href="https://www.linkedin.com/in/mahmoudhegab/" title="LinkedIn"><img src="https://img.shields.io/badge/LinkedIn-%230177B5?style=flat&logo=linkedin&logoColor=white"/></a>
17 | 
18 | 


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/Roadmap.md:
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 1 | # General Roadmap
 2 | Here you have the main topics and for each one there are some suggested courses that you should take one or more of them. <br>
 3 | <br>
 4 | ***> 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, however, you should go through all the topics.***
 5 | <br>
 6 | ## So let's start
 7 | ### 1. Python
 8 | [Python3 Specialization](https://bit.ly/3vQRRoe) <br>
 9 | [Python for Everybody specialization](https://bit.ly/3w0VrvY) <br>
10 | [Intro to cs with python MIT](https://bit.ly/3hV3rqj)  <br>
11 | 
12 | ### 2. Intro to statistics
13 | [Descriptive stats (Udacity)](https://bit.ly/3t0mZzD) <br>
14 | 
15 | ### 3. Analysis
16 | * Pandas and Numpy <br>
17 | &nbsp;&nbsp;&nbsp;&nbsp;[Google first course(data, data)](https://bit.ly/3pR0480) <br>
18 | &nbsp;&nbsp;&nbsp;&nbsp;[Pandas (Corey playlist)](https://bit.ly/3pXk8FX)  <br>
19 | &nbsp;&nbsp;&nbsp;&nbsp;[Pandas and numpy (Udacity)](https://bit.ly/3HV5ULO)  <br>
20 | * Cleaning <br>
21 | &nbsp;&nbsp;&nbsp;&nbsp;[Cleaning blog](https://bit.ly/3vXqybR)   <br>
22 | &nbsp;&nbsp;&nbsp;&nbsp;[Cleaning session](https://bit.ly/35Le3oY )  <br>
23 | &nbsp;&nbsp;&nbsp;&nbsp;[Cleaning datacamp](https://bit.ly/3w2iQ0h )  <br>
24 | &nbsp;&nbsp;&nbsp;&nbsp;[Cleaning (part of google specialization)](https://bit.ly/3vQTEts ) <br>
25 | * EDA <br>
26 | &nbsp;&nbsp;&nbsp;&nbsp;[Ask questions(from google specialization)](https://bit.ly/3CLwM01) <br> 
27 | &nbsp;&nbsp;&nbsp;&nbsp;[Visualizing intro to seaborn (datacamp)](https://bit.ly/3KprvxE ) <br>
28 | &nbsp;&nbsp;&nbsp;&nbsp;[Visualizing intro to matplotlib (datacamp)](https://bit.ly/3Cz9E4M ) <br>
29 | &nbsp;&nbsp;&nbsp;&nbsp;[Visualizing intermediate seaborn (datacamp)](https://bit.ly/3pWPQmu) <br>
30 | &nbsp;&nbsp;&nbsp;&nbsp;[EDA (datacamp)](https://bit.ly/3KChY6z ) <br>
31 | &nbsp;&nbsp;&nbsp;&nbsp;[EDA (from google specialization)](https://bit.ly/3CzSrrW ) <br>
32 | * Dashboards <br>
33 | &nbsp;&nbsp; 1. Tableau: <br>
34 | &nbsp;&nbsp;&nbsp;&nbsp;[Tableau (from google specialization)]( https://bit.ly/3pY9LBB) <br> 
35 | &nbsp;&nbsp;&nbsp;&nbsp;[Tableau (Udacity)](https://bit.ly/3sZyJm9 ) <br>
36 | &nbsp;&nbsp;&nbsp;&nbsp;[Tableau learning](https://tabsoft.co/3tOvtJk) <br>
37 | &nbsp;&nbsp; 2. Power BI: <br>
38 | &nbsp;&nbsp;&nbsp;&nbsp;[Power BI (Coursera)](https://www.coursera.org/projects/power-bi-desktop) <br>
39 | &nbsp;&nbsp;&nbsp;&nbsp;[Power BI learning](https://powerbi.microsoft.com/en-us/learning/) <br> 
40 | * More Statistics <br>
41 | &nbsp;&nbsp;&nbsp;&nbsp;[Inferential stats (Udacity)](https://bit.ly/37a34FY) <br>
42 | &nbsp;&nbsp;&nbsp;&nbsp;[Think stats (book)](https://bit.ly/3KA6nEO) <br>
43 | 
44 | ### 4. SQL
45 | [Intro to sql (datacamp)](https://bit.ly/3i1g7M0) <br>
46 | [Intro to sql and db (datacamp)](https://bit.ly/36ak52f) <br> 
47 | [Intermediate sql (datacamp)](https://bit.ly/3J09xBx ) <br>
48 | 
49 | ### 5. Tools 
50 | [Git (Udacity)](https://bit.ly/3I1h8yc) <br> 
51 | [Intro to web scraping (datacamp)](https://bit.ly/3CuNyjE) <br> 
52 | [Web scraping (datacamp)]( https://bit.ly/37kqrgi) <br>
53 | 
54 | ### 6. Machine Learning
55 | * Probability and linear algebra <br>
56 | &nbsp;[Probability (Khanacademy)](https://bit.ly/379gylk) <br> 
57 | &nbsp;[Probability MIT playlist]( https://bit.ly/3MFxaBC) <br> 
58 | &nbsp;[Linear algebra for ML Coursera specialization]( https://bit.ly/3pToX32) <br> 
59 | * Machine learning <br>
60 | &nbsp;[Machine learning andrew](https://bit.ly/3u5zfhV) <br>
61 | &nbsp;[Machine learning IBM](https://www.coursera.org/learn/machine-learning-with-python?specialization=ai-engineer&fbclid=IwAR35JlCKXk3OdCYVRFnK_pRXmiko5CHO7lKk5rLld8M3A9McbtIVPDn6AFs) <br>
62 | &nbsp;[Hands on ML 2nd edition book](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1098125975) <br>
63 | &nbsp;[Videos of Hands on ML 2nd edition](https://www.youtube.com/playlist?list=PL1YWN9bMt3ODJnCNW1WqJ46tXVMCgdwTI) <br>
64 | &nbsp;[Feature engineering book]( https://bit.ly/3vWNubo) <br>
65 | 
66 | ### 7. Deep Learning
67 | [Deep learning specialization Andrew](https://bit.ly/3I1c396) <br> 
68 | [Deep learning book](https://www.manning.com/books/deep-learning-with-python-second-edition) <br>
69 | 
70 | ### 8. Computer Vision
71 | [Computer Vision Stanford](https://stanford.io/3HZaJDJ) <br>
72 | 
73 | ### 9. NLP
74 | [NLP Stanford](https://stanford.io/3pUcw76) <br>
75 | [NLP Coursera specialization](https://bit.ly/3vXkU9M) <br> 
76 | 
77 | ### 10. Spark
78 | [Spark (Udacity)](https://bit.ly/3sYKFUZ ) <br>
79 | 
80 | ### 11. Data Warehouse
81 | [Data Warehouse Concepts (Coursera)](https://www.coursera.org/learn/dwdesign) <br>
82 | 
83 | 
84 | 
85 | 
86 | 
87 | 
88 | 
89 | 


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/Roadmap_into_weeks.md:
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  1 | # Roadmap into weeks
  2 | 
  3 | ## It's the same roadmap, but divided into weeks with an average studying rate of 7 hours per week.
  4 | 
  5 | ### This roadmap is divided into 4 stages:
  6 | ### 1. Stage 1: you get a basic understanding of the prerequisites, data cleaning and git.
  7 | ### 2. Stage 2: learn visualizing, Tableau, SQL and web scraping.
  8 | ### 3. Stage 3: dive into ML and Math
  9 | ### 4. Stage 4: where we learn DL, CV and NLP.
 10 | 
 11 | 
 12 | <br>
 13 | > You should make a task after each week, and some projects after each stage
 14 | 
 15 | ## Stage 1
 16 | ### Week 1 :
 17 | [Python (first course in the specialization)](https://www.coursera.org/learn/python?specialization=python) <br> 
 18 | Task 1 : [Notebook link](https://colab.research.google.com/drive/1N_sxUfzFwAEQmqVuOxMfaRGxFuw9DnPm?usp=sharing) <br> 
 19 | 
 20 | ### Week 2:
 21 | [Python (second course in the specialization)]( https://www.coursera.org/learn/python-data?specialization=python) <br> 
 22 | [Descriptive stats](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827) till lesson4-variability (included) <br>
 23 | Task 2 : [Notebook link](https://colab.research.google.com/drive/1JCuKVSZPRKMOG4xzD36Oo0oSHq9628w8?fbclid=IwAR2QiEDl6vzgsERONTARCt2GWgDb-apTwZOjrcJRTOIVMrmT4dfiMj-KyXo) <br> 
 24 | 
 25 | ### Week 3:
 26 | Finish [Descriptive stats](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827) <br>
 27 | [Google first course](https://www.coursera.org/learn/foundations-data?specialization=google-data-analytics) <br>
 28 | Task 3 : [Notebook link](https://colab.research.google.com/drive/1tA5Ryyoj1TKyNO2_NoH2M_MB9oNAuTob?usp=sharing) <br> 
 29 | 
 30 | ### Week 4:
 31 | [Numpy video](https://www.youtube.com/watch?v=QUT1VHiLmmI) <br>
 32 | 6 videos of [Corey playlist](https://www.youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS ) <br>
 33 | Task 4 p1 : [Notebook link](https://colab.research.google.com/drive/1bAS39rOstc4bf3vdaTvD8PEjlHyIB8wp?usp=sharing#scrollTo=MwzekJmUstWR) <br> 
 34 | Task 4 p2 : [Notebook link](https://colab.research.google.com/drive/1jGpdatYPiQv5VLPtQutRPtGsi6Bpm3U4?usp=sharing) <br> 
 35 | 
 36 | 
 37 | ### Week 5:
 38 | [Git (udacity)](https://bit.ly/3I1h8yc) <br>
 39 | 2 lessons of [Cleaning (datacamp)](https://app.datacamp.com/learn/courses/cleaning-data-in-python) <br> 
 40 | [Cleaning blog](https://bit.ly/3vXqybR ) <br>
 41 | Task 5 : [Notebook link](https://colab.research.google.com/drive/1w_sO_9GcQbDsnbHl85XvUYwNOVA0vsE5?usp=sharing) <br> 
 42 | 
 43 | 
 44 | ### Week 6:
 45 | Finish [Cleaning (datacamp)](https://app.datacamp.com/learn/courses/cleaning-data-in-python) <br>
 46 | [Visualizing (datacamp)](https://app.datacamp.com/learn/courses/introduction-to-data-visualization-with-seaborn ) <br>
 47 | Task 6 : [Link](https://drive.google.com/drive/folders/1Nxi3h7cxIFDJ1Zr8LajnHYDA2cuLDAK2?usp=sharing) <br> 
 48 | 
 49 | ## Stage 2
 50 | ### Week 7:
 51 | [Ask Questions (from google specialization)](https://www.coursera.org/learn/ask-questions-make-decisions?specialization=google-data-analytics) <br>
 52 | [More Visualizing (datacamp)](https://app.datacamp.com/learn/courses/intermediate-data-visualization-with-seaborn ) <br>
 53 | Task 7 : [Link](https://docs.google.com/document/d/1bg3uHz1BGwuj_LuBr_V9dxJAviMapITidSvjgswiAbw/edit?usp=sharing)
 54 | 
 55 | 
 56 | ### Week 8:
 57 | [EDA (datacamp)](https://app.datacamp.com/learn/courses/exploratory-data-analysis-in-python) <br>
 58 | Half of [Tableau (udacity)](https://www.udacity.com/course/data-visualization-in-tableau--ud1006 ) <br>
 59 | [Excel data analysis](https://youtube.com/playlist?list=PLUaB-1hjhk8Hyd5NiPQ9CND82vNodlFF5&si=Kdy-f0nCqEwPEj-N) <br>
 60 | Task 8 : Investigate the following dataset [Data](https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand)
 61 | 
 62 | 
 63 | ### Week 9:
 64 | Finish [Tableau (udacity)](https://www.udacity.com/course/data-visualization-in-tableau--ud1006 ) <br>
 65 | [Tableau and visualizing (from google specialization)](https://www.coursera.org/learn/visualize-data?specialization=google-data-analytics) <br>
 66 | Task 9 : [Tableau Task](https://docs.google.com/document/d/1TamjhCdFRgyPi6ZRiYFGRs5KyECbcN6a_vpimEK-aP8/edit?usp=sharing)
 67 | 
 68 | 
 69 | ### Week 10:
 70 | [Intro to DB](https://app.datacamp.com/learn/courses/introduction-to-relational-databases-in-sql) <br>
 71 | [Intro to SQL](https://app.datacamp.com/learn/courses/introduction-to-sql) <br>
 72 | Task 10 : [SQL Task](https://docs.google.com/document/d/17i9YNOBgyrKJ8cNAA43zDMcUJ7yj3xwrImcZ-SjoHVY/edit?usp=sharing)
 73 | 
 74 | 
 75 | ### Week 11:
 76 | [Intro to web scraping](https://app.datacamp.com/learn/courses/intermediate-importing-data-in-python) <br>
 77 | [Web scraping](https://app.datacamp.com/learn/courses/web-scraping-with-python) <br> 
 78 | Task 11 : [Scraping Task](https://docs.google.com/document/d/1TmhlH5gOV-glWIMgq6P51sPr3GezLnpaiEqDlptsVDE/edit?usp=sharing)
 79 |  
 80 | 
 81 | ### Week 12:
 82 | [Inferential Stats](https://classroom.udacity.com/courses/ud201 ) <br>
 83 | Task 12 : [Notebook Link](https://colab.research.google.com/drive/1MbniueZJ4ixefVUNKtX19JZo2FDDj-jQ?usp=sharing)
 84 | 
 85 | 
 86 | 
 87 | ## Stage 3
 88 | ### Week 13 :
 89 | [Prof. H.Tizhoosh](https://www.youtube.com/watch?v=tExPpuk-UQ8&list=PLvan4zSb2RaoRGHbSP15RYrUycboAmmLL&index=1) <br>
 90 | First 3 weeks in [Mathematics for Machine Learning: Linear Algebra](https://www.coursera.org/learn/linear-algebra-machine-learning?specialization=mathematics-machine-learning) <br> 
 91 | First week in [Supervised Machine Learning](https://www.coursera.org/learn/machine-learning) <br> 
 92 | Task 13 : [Notebook Link](https://colab.research.google.com/drive/1OMe4HlaLDP1kCVzS1uhTsIYSQfwurKAk?usp=sharing)
 93 | 
 94 | 
 95 | ### Week 14 :
 96 | Finish [Mathematics for Machine Learning: Linear Algebra](https://www.coursera.org/learn/linear-algebra-machine-learning?specialization=mathematics-machine-learning) <br>
 97 | Finish [Supervised Machine Learning](https://www.coursera.org/learn/machine-learning) <br> 
 98 | Task 14 : [Notebook Link](https://colab.research.google.com/drive/1rB7ApBCg5yOzfmM3x142OG4_wFYI2bOy?usp=sharing)
 99 | 
100 | 
101 | ### Week 15 :
102 | First 3 weeks in [Mathematics for Machine Learning: Multivariate Calculus](https://www.coursera.org/learn/multivariate-calculus-machine-learning) <br>
103 | First 2 weeks in [Advanced Learning Algorithms](https://www.coursera.org/learn/advanced-learning-algorithms) <br> 
104 | Task 15 : [Notebook Link](https://colab.research.google.com/drive/1F11lgEXGlw9UvlPGfIKHJqIQrgkNhXnL?usp=sharing)
105 | 
106 | 
107 | ### Week 16 :
108 | Finish [Mathematics for Machine Learning: Multivariate Calculus](https://www.coursera.org/learn/multivariate-calculus-machine-learning) <br>
109 | Finish [Advanced Learning Algorithms](https://www.coursera.org/learn/advanced-learning-algorithms) <br> 
110 | Task 16 : [Notebook Link](https://colab.research.google.com/drive/1ROo0doUySbAcTLxAY0FXVnWGNwpBYISM?usp=sharing)
111 | 
112 | 
113 | ### Week 17 :
114 | [Unsupervised Learning, Recommenders, Reinforcement Learning](https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning) <br>
115 | 
116 | 
117 | ### Week 18 :
118 | First 2 chapters in [Hands on ML book](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1098125975) <br>
119 | 
120 | 
121 | ### Week 19 :
122 | Chapters 3,4 in [Hands on ML book](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1098125975) <br>
123 | 
124 | 
125 | ### Week 20 :
126 | Chapters 5,6 in [Hands on ML book](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1098125975) <br>
127 | 
128 | 
129 | ### Week 21 :
130 | Chapters 7,8 in [Hands on ML book](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1098125975) <br>
131 | 
132 | 
133 | ### Week 22 :
134 | Chapter 9 in [Hands on ML book](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1098125975) <br>
135 | First part in [Probability](https://www.khanacademy.org/math/statistics-probability/probability-library) <br>
136 | 
137 | 
138 | ### Week 23 :
139 | Finish [Probability](https://www.khanacademy.org/math/statistics-probability/probability-library) <br>
140 | 
141 | > More to be added...
142 | 
143 | 
144 | 


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