▶ Data Science Squad Roadmap
▶ What is Data Science?
▶ 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
Intro to Descriptive Statistics
Intro to Descriptive Statistics Article 1 or Article 2
Arabic Course
One resource is very enough
Khan Academy
Arabic Course
One resource is very enough
Introduction to Python Programming
OOP
Arabic Course
Kaggle
Playlist-Youtube
Arabic Course
One resource is very enough
Kaggle
Arabic Course
Tutorial
Docs
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
Kaggle to Data Visualization with Seaborn
Intermediate Data Visualization with Seaborn
Playlist-Youtube
IBM
Intro to SQL or IBM
Intro to Relational Databases in SQL
Arabric Course
Track
Book
fbprohet
Arabic Source Video1 & Video2
Do not forget to apply what you have learned periodically.
▶ Intermediate.
Mathematics for Machine Learning Specialization
Andrew Ng
IBM ML with Python
Hands on ML book
Arabic Course
Kaggle or Article
Book
Playlist-Youtube
Tutorial
Specialization
▶ ▶ Other topics related to all of the above
course
intro2
Tutorial
book for both topics
Tutorial
Article
Tutorial
This stats. book
Think Bayes
course
joins
After finishing this level apply to 2 or 3 good-sized projects.
▶ Advanced
we will improve and add more!
Specialization (Andrew Ng)
Book
Arabic Course
Specialization
Arabic Course
Specialization
Specialization
more to be added here..