Best Books for Data Science for all Level of Peoples
Statistical methods play a key role in data science. There are some excellent introductory and advanced level textbooks for data scientists which explains how to apply various statistical methods to data science, how to avoid their misuse and gives you advice on what's important and what's not.
Here you will get some of the best books for data science.
This is a great informative book for those who are newer, and a little more experienced. This is a good introduction to practical statistics which provided a number of excellent practical logical explanations. People who are interested in statistics and data science find this book very helpful.
Charles Wheelan clarifies key concepts such as
- Regression analysis
- Randomized experiments
- Hypothesis tests
- Issues related to confidence level and p-value.
The writer reveals how biased or careless parties can manipulate or misrepresent data. He again shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
Statistics with R is a great book for beginning data analysis. A beginner will quickly be able to use data analysis tools such as ggplot2 and dplyr etc. Students and working professionals find this book very informative. It provides an integrated treatment of statistical inference techniques in data science using the R statistical software.
So we can say that this is an awesome resource for all levels who want to reach the depth of statistics and data science.
An Introduction to Statistical Learning provides you the right amount of theory and practice. This data science book requires no prior knowledge of calculus or linear algebra though it is an outstanding introduction to statistical learning.
This book provides you
- An accessible overview of the field of statistical learning
- An essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics
- Some of the most important modeling and prediction techniques, along with relevant applications
- Linear regression
- Resampling methods
- Shrinkage approaches
- Tree-based methods
- Support vector machines, clustering, and more
Each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open-source statistical software platform.
Practical Statistics for Data Scientists is an excellent introductory textbook for data scientists which explains how to apply various statistical methods to data science, how to avoid their misuse, and gives you advice on what's important and what's not. This is a good reference book as all the explanations are very clear.
This book includes
- Python code
- The curse of dimensionality
- A discussion of neural networks.
You’ll learn from this book
- Why exploratory data analysis is a key preliminary step in data science
- How random sampling can reduce bias and yield a higher quality dataset, even with big data
- How the principles of experimental design yield definitive answers to questions
- How to use regression to estimate outcomes and detect anomalies
- Key classification techniques for predicting which categories a record belongs to
- Statistical machine learning methods that learn from data
- Unsupervised learning methods for extracting meaning from unlabeled data
Statistical Rethinking: A Bayesian Course with Examples in R and Stan is a nice and short introduction to statistical modeling. In this book, the author includes the basics of regression to multilevel models. He also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.
- The Golem of Prague
- Small worlds and large worlds
- Sampling the imaginary
- Linear models
- Multivariate linear models
- Overfitting and model comparison
- Markov chain Monte Carlo Estimation
- Big entropy and the generalized linear model
- Counting and classification
- Monsters and mixtures
- Multilevel models
- Adventures in covariance
- Missing data and other opportunities
This book is divided into four parts. These are
- Gathering and exploring data
- Probability, probability distribution, and sampling distributions
- Inferential statistics
- Analyzing association and extended statistical method
It’s important for students to learn and analyze both quantitative and categorical data. Concepts are introduced first with categorical data, and then with quantitative data.
The key terms are
- Navigating the software
- Input and output
- Data structures
- Data transformations
- Strings and date
- General statistics
- Linear regression an ANOVA
- Useful tricks
- Beyond basic numerics and statistics
- Time series analysis.
This is the best books for data science with excellent data about data analysis. It provides useful advice that applies in the real world jobs and techniques. This book is divided into five parts which are the research process and data collection, describing data, testing hypotheses, exploring relationships, and writing a research paper.
Key coverage is
- The research process
- Sampling techniques
- Questionnaire design
- An introduction to Stata
- Preparing and transforming your data
- Descriptive statistics
- The normal distributions
- Linear regression analysis and diagnostics
- Regression analysis with categorical dependent variables.