📌 “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..
