Learn and Download complete course of Probability and Statistics for Data Science From Scratch. Data science is a mixture of math’s, tools, algorithms, business acumen and machine learning techniques that all of which help us in finding out the invisible patterns from primary data which can be used as in the development of big business commitment. You can learn python for data science from scratch by learning the basics of programming with Python, By learning basic statistics and mathematics, By learning python for data analysis, learning machine learning, practice with projects. This practical book is very useful for students, researchers and developers. You can easily understand its concepts by real time examples. You can download this book free from here.
Download introduction of Probability and Statistics for Data Science From Scratch free in PDF. In this PDF notes you will learn about its Data Science, Probability and techniques of Statistics. This book is very useful and helpful for beginners. In this book you will learn fundamentals of Data Science From Scratch. This Data science language is very easy to learn and read. You will learn data science from scratch by learning statistics, by collection of data, cleaning of data, deep learning and machine learning. In this book you will learn basic introduction of Data Science from Scratch.
By this book you will cover this topic:
Fundamentals of Probability
- Theoretical Approaches
- A More Intuitive Approach
- Our Definitions
- Bus Ridership Model
- Aloha Network
- Aloha Network Model Summary
- Aloha Network Computations
- Aloha in the Notebook Context
- A Simple Board Game
Functions
- Discrete Random Variables
- Random Variables
- Discrete Random Variables
- The Monty Hall Problem
- Expected Value
- Misnomer
- Bus Ridership
- Predicting Product Demand
- Expected Values via Simulation
The Gamma Family of Distributions
Density and properties
Network Buffer
Importance in modeling
The Beta Family of Distributions
- Density etc
- Importance in Modeling
Mathematical Complements
- Hazard Functions
- Duality of the Exponential Family with the Poisson
- Family
Computational Complements
- R’s integrate() Function
- Inverse Method for Sampling from a Density
- Sampling from a Poisson Distribution
Cumulative Distribution Functions
- CDFs
- Non-Discrete, Non-Continuous Distributions
Density Functions
- Properties of Densities
- Intuitive Meaning of Densities
- Expected Values
- A First Example
Famous Parametric Families of Continuous Distributions
- The Uniform Distributions
- Density and Properties
- R Functions
- Modeling of Disk Performance
- Modeling of Denial-of-Service
The Normal Family of Continuous Distributions
- Importance in Modeling
- The Exponential Family of Distributions
Density and Properties - R Functions
- Garage Parking Fees
- Memoryless Property of Exponential Distributions
- Importance in Modeling
