📌 “We are in CIS try to give you advice about How to start in Data Science. This Document for who are interested in 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.

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.

    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.

    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

    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.

📌 Descriptive Stats.
    Intro to Descriptive Statistics
    Intro to Descriptive Statistics Article 1 or Article 2
    Arabic Course
    One resource is very enough
📌 Probability
    Khan Academy
    Arabic Course
    One resource is very enough
📌 Python
    Introduction to Python Programming     OOP
    Arabic Course
📌 Pandas
    Kaggle
    Playlist-Youtube
    Arabic Course
    One resource is very enough

📌 Numpy
    Kaggle
    Arabic Course
📌 Scipy
    Tutorial
    Docs

📌 Data Cleaning
    Read this To know the importance of Data Cleaning
    Kaggle to Cleaning data
    Introduction to Data Science in Python
    Arabic video but not enough
    Cleaning Data in Python

📌 Data Visualization
    Kaggle to Data Visualization with Seaborn
    Intermediate Data Visualization with Seaborn
    Playlist-Youtube

📌 EDA
    IBM
📌SQL and DataBase
    Intro to SQL or IBM
    Intro to Relational Databases in SQL
    Arabric Course

📌 Time Series Analysis
    Track
    Book
    fbprohet
    Arabic Source Video1 & Video2

📌 Math for Machine Learning
    Mathematics for Machine Learning Specialization

📌 Machine Learning
    Andrew Ng
    IBM ML with Python
    Hands on ML book
    Arabic Course

📌 Feature Engineering
    Kaggle or Article
    Book
    Playlist-Youtube

📌 Tableau
    Tutorial
    Specialization

📌 Web Scraping&APIs
    course
    intro2
    Tutorial
    book for both topics
📌 APIs
    Tutorial
    Article
    Tutorial
📌 Stats.
    This stats. book
    Think Bayes
📌 Advanced SQL
    course
    joins

we will improve and add more!

📌 Deep Learning
    Specialization (Andrew Ng)
    Book
    Arabic Course

📌 Tensorflow & Keras
    Specialization
    Arabic Course

📌 Machine Learning Engineering for Production (MLOps)
    Specialization

📌 Practical Data Science
    Specialization

more to be added here..