Statistics Essentials for Beginners in Data Science

Are you thinking of learning Statistics fundamentals for Data Science?

If you are looking for a beginner book to master Statistics Learning fundamentals for Data Science, this book is for you.

Who Should Read this Book?

Aspiring data scientists who are looking forward to begin their journey in the vast field of data science. People who are seeking to learn and understand data analysis from its very deep-rooted basics have found the right book. Clear basic concepts make the foundation of a good knowledge base, which ultimately helps to gain sharp insights into this topic further. This book will give you the practical exposure along with its theory explained comprehensively. This book is the perfect compilation for beginners as well as intermediate learners who intend to learn statistics and data analysis techniques.

Why this book?

This book will guide you step by step from the very basics to how you can start your own data science project. The best part about this book is its structure, it's structured in such a way that integrates practicals along with its theory to make the concepts easily understandable. It will help you to understand a basic concept like mean, median, mode, scatter plot and histograms. Thus ensures no prior knowledge is required to start learning from this book. The content of this book is specially designed to encompass all the concepts that come under the domain of data science. This book will guide you through the problems and concepts of statistics.

What is statistics?

Most of the people think statistics in data science is something different and more profound than what we learnt in our mathematics classes but it’s not. It is the same concept of data collection followed by its organization, interpretation and presentation. Statistics is the key to develop a desired model in machine learning. Using statistics you can convert your raw meaningless chunk of data to a well-structured informative data.

What’s Inside This Book?

Probability & Bayes Theorem, Data Exploration and Analysis
Structured Data
Estimates Mean and Median
Estimates Variability
Exploring the data distribution
Percentiles and Boxplots
Frequency table and Histograms
Density Estimates
Mode
Correlation
Categorical and Numeric Data
Visualizing Multiple Variables
Regression Analysis
Clustering Analysis
Statistical tests and ANOVA
Classification
Naïve Bayes
Discriminant Analysis
Linear regression
Logistic Regression
Statistical Machine Learning
K_Nearest Neighbor
Trees Models
Bagging and Random Forest
Boosting algorithms
Principal Component Analysis
K_means Clustering
Hierarchical Clustering
Model Based Clustering
Sources & References
From AI Sciences Publishing

Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations.