└── README.md /README.md: -------------------------------------------------------------------------------- 1 | ![Data Science](https://user-images.githubusercontent.com/55801427/127027935-5d7d6de8-1a2d-411b-ac8d-6c07856c6f96.png) 2 | 3 | 4 | # β–Ά Data Science Squad Roadmap 5 | 6 | **πŸ“Œ β€œWe are in [CIS](https://www.facebook.com/cisteam15/) try to give you advice about How to start in Data Science. This Document for who are interested in Data Science”** 7 | 8 | 9 | # **β–ΆWhat is Data Science?** 10 | 11 | πŸ“Œ 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 which analysts and business users can translate into tangible business value. 12 | 13 | # **β–ΆWhy Data Science is Important?** 14 | Data is valuable, and so is the science in decoding it. Zillions of bytes of data are being generated, and now its value has surpassed oil as well. The role of a data scientist is and will be of paramount importance for organizations across many verticals. 15 | 16 | **Data without science is nothing.** 17 | Data needs to be read and analyzed. This calls out for the requirement of having a quality of data and understanding how to read it and make data-driven discoveries. 18 | 19 | **Data will help to create better customer experiences.** 20 | For goods and products, data science will be leveraging the power of machine learning to enable companies to create and produce products that customers will adore. For example, for an eCommerce company, a great recommendation system can help them discover their customer personas by looking at their purchase history. 21 | 22 | **Data will be used across verticals.** 23 | Data science is not limited to only consumer goods or tech or healthcare. There will be a high demand to optimize business processes using data science from banking and transport to manufacturing. So anyone who wants to be a data scientist will have a whole new world of opportunities open out there. The future is data. 24 | 25 | 26 | # **β–ΆWhat are we going to learn?** 27 | ## **πŸ“Œ Basic sciences you will need** 28 |     Mathematics and statistics are the heart of data science. Because this is the basis by which you will understand the data and understand how to build machine learning Algorithms and how to work with them. 29 | 30 | ## **πŸ“Œ Data Analysis** 31 |     In this part, you will start by learning the tools and techniques and applying statistics and mathematics that you have learned in order to understand the data, extract useful information from it, and communicate an impact to the owner who can understand and make important decisions 32 | 33 | ## **πŸ“ŒMachine Learning** 34 |     Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. Also Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. 35 | 36 | ## β–Άβ–Ά This track is divided into 3 Levels 37 | 38 | ### πŸ“Œ Beginner: you get a basic understanding of data analysis, tools and techniques. 39 | ### πŸ“Œ Intermediate: dive deeper in more complex topics of ML, Math and data engineering. 40 | ### πŸ“Œ Advanced: where we learn more advanced Math, DL and Deployment. 41 | 42 | 43 | ## β–Ά Beginner 44 | 45 | πŸ“Œ Descriptive Stats.
46 |     [Intro to Descriptive Statistics](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827)
47 |     Intro to Descriptive Statistics [Article 1](https://towardsdatascience.com/descriptive-statistics-f2beeaf7a8df) or [Article 2](https://towardsdatascience.com/intro-to-descriptive-statistics-252e9c464ac9)
48 |     [Arabic Course](https://www.youtube.com/watch?v=d5jh5mmwcKI&list=PLY99ZSsxRyJiu6kb4WRRpeEFqK1pAr-EO)
49 |     One resource is very enough
50 | πŸ“Œ Probability
51 |     [Khan Academy](https://www.khanacademy.org/math/statistics-probability/probability-library)
52 |     [Arabic Course](https://www.youtube.com/playlist?list=PL158D091D26F47358)
53 |     One resource is very enough
54 | πŸ“Œ Python
55 |     [Introduction to Python Programming](https://www.udacity.com/course/introduction-to-python--ud1110) 56 |     57 | [OOP](https://learn.datacamp.com/courses/object-oriented-programming-in-python)
58 |     59 | [Arabic Course](https://www.youtube.com/watch?v=MxYLqE3Ils8&list=PLHIfW1KZRIfnM9y0sQRwjVz2-IwvnEJep)
60 | πŸ“Œ Pandas
61 |     [Kaggle](https://www.kaggle.com/learn/pandas)
62 |     [Playlist-Youtube](https://www.youtube.com/watch?v=yzIMircGU5I&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=1)
63 |     [Arabic Course](https://www.youtube.com/watch?v=3ISW655DemU&list=PLvLvlVqNQGHCb2_ygmr1DQOMOv0yXp84F)
64 |     One resource is very enough
65 | 66 | πŸ“Œ Numpy
67 |     [Kaggle](https://www.kaggle.com/legendadnan/numpy-tutorial-for-beginners-data-science)
68 |     [Arabic Course](https://www.youtube.com/watch?v=5-5CrLmf2vk&list=PLIA_seGogbkGDYq-dnVCsELEIq_7HK7Ca)
69 | πŸ“Œ Scipy
70 |     [Tutorial](https://cs231n.github.io/python-numpy-tutorial/#scipy)
71 |     [Docs](https://docs.scipy.org/doc/scipy/reference/tutorial/general.html)
72 | 73 | πŸ“Œ Data Cleaning
74 |     [Read this](https://towardsdatascience.com/the-ultimate-guide-to-data-cleaning-3969843991d4) To know the importance of Data Cleaning
75 |     [Kaggle to Cleaning data](https://www.kaggle.com/learn/data-cleaning)
76 |     [Introduction to Data Science in Python](https://www.coursera.org/learn/python-data-analysis?specialization=data-science-python)
77 |     [Arabic video](https://www.youtube.com/watch?v=Mrd56i_U6cM) but not enough
78 |     [Cleaning Data in Python](https://learn.datacamp.com/courses/cleaning-data-in-python)
79 | 80 | 81 | πŸ“Œ Data Visualization
82 |     [Kaggle to Data Visualization with Seaborn](https://www.kaggle.com/learn/data-visualization)
83 |     [Intermediate Data Visualization with Seaborn](https://learn.datacamp.com/courses/intermediate-data-visualization-with-seaborn)
84 |     [Playlist-Youtube](https://www.youtube.com/watch?v=z7ZINBk8EUk&list=PL998lXKj66MpNd0_XkEXwzTGPxY2jYM2d)
85 | 86 | πŸ“Œ EDA
87 |     [IBM](https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning)
88 | πŸ“ŒSQL and DataBase
89 |     [Intro to SQL](https://learn.datacamp.com/courses/introduction-to-sql) **or** [IBM](https://www.coursera.org/learn/sql-data-science)
90 |     [Intro to Relational Databases in SQL](https://learn.datacamp.com/courses/introduction-to-relational-databases-in-sql)
91 |     [Arabric Course](https://www.youtube.com/watch?v=B7evUQGmN6M&list=PLfM2wZNebA2zROxUcAbGxNrpVZncsF3oD) 92 | 93 | πŸ“Œ Time Series Analysis
94 |     [Track](https://learn.datacamp.com/skill-tracks/time-series-with-python)
95 |     [Book](https://www.oreilly.com/library/view/practical-time-series/9781492041641/?fbclid=IwAR20cq7hAdWf6voOd61u-pNzZCHvB0rZhT_BUoGTAXxPBhhi82p8BhxLEsI)
96 |     [fbprohet](https://facebook.github.io/prophet/docs/quick_start.html)
97 |     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) 98 | 99 | Do not forget to apply what you have learned periodically. 100 | -------------------------------------------------------------------------------------------------------- 101 | ## β–ΆIntermediate. 102 | 103 | πŸ“Œ Math for Machine Learning
104 |     [Mathematics for Machine Learning Specialization](https://www.coursera.org/specializations/mathematics-machine-learning)
105 | 106 | πŸ“Œ Machine Learning
107 |     [Andrew Ng](https://www.coursera.org/learn/machine-learning)
108 |     [IBM ML with Python](https://www.coursera.org/learn/machine-learning-with-python)
109 |     [Hands on ML book](https://drive.google.com/file/d/15J7YoyRcmwQE2mgW5yVs-MrPL3YtmuSz/view?usp=sharing&fbclid=IwAR1RVi90sfrggEaZnc1roXW9H8AGECyHcsQnZw22FORq-HSaP0VlBU5CAiM)
110 |     [Arabic Course](https://www.youtube.com/c/HeshamAsem/playlists)
111 | 112 | πŸ“Œ Feature Engineering
113 |     [Kaggle](https://www.kaggle.com/learn/feature-engineering) or [Article](https://www.medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Ffeature-engineering-for-machine-learning-3a5e293a5114)
114 |     [Book](https://b-ok.cc/book/3583182/056a36)
115 |     [Playlist-Youtube](https://www.youtube.com/watch?v=pYVScuY-GPk&list=PLeo1K3hjS3ut5olrDIeVXk9N3Q7mKhDxO)
116 | 117 | πŸ“Œ Tableau
118 |     [Tutorial](https://www.datacamp.com/community/tutorials/data-visualisation-tableau)
119 |     [Specialization](https://www.coursera.org/specializations/data-visualization)
120 | 121 | ### β–Άβ–Ά Other topics related to all of the above 122 | πŸ“Œ Web Scraping&APIs
123 |     [course](https://learn.datacamp.com/courses/web-scraping-with-python)
124 |     [intro2](https://www.dataquest.io/blog/web-scraping-tutorial-python/)
125 |     [Tutorial](https://realpython.com/beautiful-soup-web-scraper-python/)
126 |     [book for both topics](https://b-ok.africa/book/3515980/5d50aa)
127 | πŸ“Œ APIs
128 |     [Tutorial](https://www.dataquest.io/blog/python-api-tutorial/)
129 |     [Article](https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-pull-data-from-an-api-using-python-requests-edcc8d6441b1)
130 |     [Tutorial](https://rapidapi.com/blog/how-to-use-an-api-with-python/)
131 | πŸ“Œ Stats.
132 |     [This stats. book](https://b-ok.africa/book/2737548/7659e9)
133 |     [Think Bayes](https://b-ok.africa/book/2737587/ab97d5)
134 | πŸ“Œ Advanced SQL
135 |     [course](https://www.coursera.org/lecture/data-driven-astronomy/more-advanced-sql-GDmo5)
136 |     [joins](https://learn.datacamp.com/courses/joining-data-in-postgresql)
137 | 138 | 139 | ### After finishing this level apply to 2 or 3 good-sized projects. 140 | -------------------------------------------------------------------------------------------------------- 141 | ## β–Ά Advanced 142 | **we will improve and add more!** 143 | 144 | πŸ“Œ Deep Learning
145 |     [Specialization (Andrew Ng)](https://www.coursera.org/specializations/deep-learning)
146 |     [Book](https://d2l.ai/d2l-en.pdf?fbclid=IwAR0sVdA8VFYpNZCpYZHgo_kl_HYrjcjDfjEka26D8xRWAhbhh6mmSNIXg3U)
147 |     [Arabic Course](https://www.youtube.com/watch?v=UKk3K0g7cP8&list=PL6-3IRz2XF5UiBoBDgeu5T3TyOIrgQ3r9) 148 | 149 | πŸ“Œ Tensorflow & Keras
150 |     [Specialization](https://www.coursera.org/specializations/tensorflow-in-practice)
151 |     [Arabic Course](https://www.youtube.com/watch?v=ohyn_MzS_hE&list=PL6-3IRz2XF5VbuU2T0gS_mFhCpKmLxvCP)
152 | 153 | 154 | πŸ“Œ Machine Learning Engineering for Production (MLOps)
155 |     [Specialization](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops?)
156 | 157 | πŸ“Œ Practical Data Science
158 |     [Specialization](https://www.coursera.org/specializations/practical-data-science)
159 | 160 | 161 | > more to be added here.. 162 | 163 | *** 164 | 165 | ## ...More yet to come in this section.. 166 | 167 | *** 168 | 169 | ## **β–ΆPersonal Contact** 170 | # 171 | # 172 | --------------------------------------------------------------------------------