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https://github.com/cis-team/datascience-squad

Data Science Squad Roadmap
https://github.com/cis-team/datascience-squad
cis-team computer-science data-science dataanalysis
Last synced: 7 days ago
JSON representation
Data Science Squad Roadmap
Host: GitHub
URL: https://github.com/cis-team/datascience-squad
Owner: CIS-Team
Created: 2021-07-25T22:44:53.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2021-09-28T17:45:52.000Z (about 3 years ago)
Last Synced: 2023-08-10T18:27:52.569Z (over 1 year ago)
Topics: cis-team, computer-science, data-science, dataanalysis
Homepage:
Size: 222 KB
Stars: 50
Watchers: 3
Forks: 5
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

README

        ![Data Science](https://user-images.githubusercontent.com/55801427/127027935-5d7d6de8-1a2d-411b-ac8d-6c07856c6f96.png)

# ▶ Data Science Squad Roadmap

**📌 “We 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”**

# **▶What is Data Science?**

📌 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.

# **▶Why Data Science is Important?**

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.

**Data without science is nothing.**

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.

**Data will help to create better customer experiences.**

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.

**Data will be used across verticals.**

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.

# **▶What are we going to learn?**

## **📌 Basic sciences you will need**

     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.

## **📌 Data Analysis**

     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

## **📌Machine Learning**

     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.

## ▶▶ This track is divided into 3 Levels

 ### 📌 Beginner: you get a basic understanding of data analysis, tools and techniques.

 ### 📌 Intermediate: dive deeper in more complex topics of ML, Math and data engineering.

 ### 📌 Advanced: where we learn more advanced Math, DL and Deployment.

## ▶ Beginner

 📌 Descriptive Stats. 


           [Intro to Descriptive Statistics](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827)


           Intro to Descriptive Statistics [Article 1](https://towardsdatascience.com/descriptive-statistics-f2beeaf7a8df) or  [Article 2](https://towardsdatascience.com/intro-to-descriptive-statistics-252e9c464ac9)


           [Arabic Course](https://www.youtube.com/watch?v=d5jh5mmwcKI&list=PLY99ZSsxRyJiu6kb4WRRpeEFqK1pAr-EO) 


           One resource is very enough


📌 Probability


            [Khan Academy](https://www.khanacademy.org/math/statistics-probability/probability-library)


            [Arabic Course](https://www.youtube.com/playlist?list=PL158D091D26F47358)


            One resource is very enough


📌 Python


            [Introduction to Python Programming](https://www.udacity.com/course/introduction-to-python--ud1110)

            

        [OOP](https://learn.datacamp.com/courses/object-oriented-programming-in-python)


           

        [Arabic Course](https://www.youtube.com/watch?v=MxYLqE3Ils8&list=PLHIfW1KZRIfnM9y0sQRwjVz2-IwvnEJep)


📌 Pandas


            [Kaggle](https://www.kaggle.com/learn/pandas)


            [Playlist-Youtube](https://www.youtube.com/watch?v=yzIMircGU5I&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=1)


            [Arabic Course](https://www.youtube.com/watch?v=3ISW655DemU&list=PLvLvlVqNQGHCb2_ygmr1DQOMOv0yXp84F)


            One resource is very enough 


   

📌 Numpy


            [Kaggle](https://www.kaggle.com/legendadnan/numpy-tutorial-for-beginners-data-science)


            [Arabic Course](https://www.youtube.com/watch?v=5-5CrLmf2vk&list=PLIA_seGogbkGDYq-dnVCsELEIq_7HK7Ca)


📌 Scipy


            [Tutorial](https://cs231n.github.io/python-numpy-tutorial/#scipy)


            [Docs](https://docs.scipy.org/doc/scipy/reference/tutorial/general.html)


        

📌 Data Cleaning


            [Read this](https://towardsdatascience.com/the-ultimate-guide-to-data-cleaning-3969843991d4) To know the importance of Data Cleaning


            [Kaggle to Cleaning data](https://www.kaggle.com/learn/data-cleaning)


            [Introduction to Data Science in Python](https://www.coursera.org/learn/python-data-analysis?specialization=data-science-python)


            [Arabic video](https://www.youtube.com/watch?v=Mrd56i_U6cM) but not enough


            [Cleaning Data in Python](https://learn.datacamp.com/courses/cleaning-data-in-python)


 

📌 Data Visualization


            [Kaggle to Data Visualization with Seaborn](https://www.kaggle.com/learn/data-visualization)


            [Intermediate Data Visualization with Seaborn](https://learn.datacamp.com/courses/intermediate-data-visualization-with-seaborn)


            [Playlist-Youtube](https://www.youtube.com/watch?v=z7ZINBk8EUk&list=PL998lXKj66MpNd0_XkEXwzTGPxY2jYM2d)


📌 EDA


            [IBM](https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning) 


 📌SQL and DataBase


              [Intro to SQL](https://learn.datacamp.com/courses/introduction-to-sql) **or** [IBM](https://www.coursera.org/learn/sql-data-science)


              [Intro to Relational Databases in SQL](https://learn.datacamp.com/courses/introduction-to-relational-databases-in-sql)


              [Arabric Course](https://www.youtube.com/watch?v=B7evUQGmN6M&list=PLfM2wZNebA2zROxUcAbGxNrpVZncsF3oD)

📌 Time Series Analysis


              [Track](https://learn.datacamp.com/skill-tracks/time-series-with-python)


              [Book](https://www.oreilly.com/library/view/practical-time-series/9781492041641/?fbclid=IwAR20cq7hAdWf6voOd61u-pNzZCHvB0rZhT_BUoGTAXxPBhhi82p8BhxLEsI)


              [fbprohet](https://facebook.github.io/prophet/docs/quick_start.html)


              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)

Do not forget to apply what you have learned periodically.

--------------------------------------------------------------------------------------------------------

## ▶Intermediate.

📌 Math for Machine Learning 


              [Mathematics for Machine Learning Specialization](https://www.coursera.org/specializations/mathematics-machine-learning)


📌 Machine Learning


          [Andrew Ng](https://www.coursera.org/learn/machine-learning)


          [IBM ML with Python](https://www.coursera.org/learn/machine-learning-with-python)


          [Hands on ML book](https://drive.google.com/file/d/15J7YoyRcmwQE2mgW5yVs-MrPL3YtmuSz/view?usp=sharing&fbclid=IwAR1RVi90sfrggEaZnc1roXW9H8AGECyHcsQnZw22FORq-HSaP0VlBU5CAiM)


          [Arabic Course](https://www.youtube.com/c/HeshamAsem/playlists)


📌 Feature Engineering


          [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)


          [Book](https://b-ok.cc/book/3583182/056a36)


          [Playlist-Youtube](https://www.youtube.com/watch?v=pYVScuY-GPk&list=PLeo1K3hjS3ut5olrDIeVXk9N3Q7mKhDxO)


📌 Tableau 


         [Tutorial](https://www.datacamp.com/community/tutorials/data-visualisation-tableau)


         [Specialization](https://www.coursera.org/specializations/data-visualization)


### ▶▶ Other topics related to all of the above

📌 Web Scraping&APIs


           [course](https://learn.datacamp.com/courses/web-scraping-with-python)


           [intro2](https://www.dataquest.io/blog/web-scraping-tutorial-python/)


           [Tutorial](https://realpython.com/beautiful-soup-web-scraper-python/)


           [book for both topics](https://b-ok.africa/book/3515980/5d50aa)


📌 APIs 


           [Tutorial](https://www.dataquest.io/blog/python-api-tutorial/)


           [Article](https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-pull-data-from-an-api-using-python-requests-edcc8d6441b1)


           [Tutorial](https://rapidapi.com/blog/how-to-use-an-api-with-python/)


📌 Stats.


          [This stats. book](https://b-ok.africa/book/2737548/7659e9)


          [Think Bayes](https://b-ok.africa/book/2737587/ab97d5)


📌 Advanced SQL


           [course](https://www.coursera.org/lecture/data-driven-astronomy/more-advanced-sql-GDmo5)


           [joins](https://learn.datacamp.com/courses/joining-data-in-postgresql)


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

--------------------------------------------------------------------------------------------------------

## ▶ Advanced

**we will improve and add more!**

📌 Deep Learning 


          [Specialization (Andrew Ng)](https://www.coursera.org/specializations/deep-learning)


          [Book](https://d2l.ai/d2l-en.pdf?fbclid=IwAR0sVdA8VFYpNZCpYZHgo_kl_HYrjcjDfjEka26D8xRWAhbhh6mmSNIXg3U)


          [Arabic Course](https://www.youtube.com/watch?v=UKk3K0g7cP8&list=PL6-3IRz2XF5UiBoBDgeu5T3TyOIrgQ3r9)

📌 Tensorflow & Keras 


           [Specialization](https://www.coursera.org/specializations/tensorflow-in-practice)


           [Arabic Course](https://www.youtube.com/watch?v=ohyn_MzS_hE&list=PL6-3IRz2XF5VbuU2T0gS_mFhCpKmLxvCP)


📌 Machine Learning Engineering for Production (MLOps)  


          [Specialization](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops?)


📌 Practical Data Science  


          [Specialization](https://www.coursera.org/specializations/practical-data-science)


> more to be added here..

***

## ...More yet to come in this section..

***

## **▶Personal Contact**

# 

#