Complete course of Data Science from Scratch with examples

Data Science:-

Study of data is called Data science.
Data Science involves developing methods of set down, to putting down, and examine data to effectively get very important and useful information.
The main goal of data science is to gain awareness of knowledge from any type of data that will be structured or unstructured.

Scratch is a language of visual programming that give permission students to create their own interactive games, to write interactive stories and animations/vigor.
Scratch is used to describe a temporary file/location in storage of memory that will be used by computer as storage place of data.
Scratch has also have own paint and sound editor.
Scratch is used as a synonym to delete.

Data Science from scratch is very important for beginners who want review and theoretical concepts on data visualization, python, data science ,neural networks and many more.
Data Science with python has a crash course on Python. Learning Data science from Scratch is very easy.
Data Science from scratch you will learn different methods of data science and evaluate the scope of data science works and projects.

Introduction and Basics of Data Science
Complete overview on Data Scientists
Complete crash course on python which include modules, functions, strings, exceptions, lists, tuples, control flow, List Comprehensions ,Generators and Iterators, Randomness ,Regular Expressions, Object-Oriented Programming , Functional Tools.
Learn about Visualizing data include bar charts, line charts, scatterplots.
Learn about Statistics which include Describing a Single Set of Data, Central Tendencies, Dispersion, Correlation, Simpson’s Paradox, Correlational Caveats, Correlation and Causation, For Further Exploration.
Learn Gradient Descent and the Idea Behind Gradient Descent, Estimating the Gradient, Using the Gradient, Choosing the Right Step Size, Putting It All Together.
Machine learning
Overfitting and Underfitting
Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Decision Trees
Clustering
Natural language process
Network Analysis
Database and SQL