Statistics Essentials for Beginners in Data Science

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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.

Frequently Asked Questions

Q: Does this book include everything I need to become a data analyst expert?
A: Unfortunately, no. This book is designed for readers taking their first steps in statistics and data science and further learning will be required beyond this book to master all aspects.

Q: Can I have a refund if this book doesn't fit for me?
A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service pleas

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