As the world is marching towards new advancements each day, there is a constant rise in data scientists’ demand worldwide. A data scientist helps analyze the data that can help make decisions related to business in all industries, be it infrastructure, technology, and more. Data Science is a broad field. It requires an understanding of the concepts that help in studying the data, organization, implementation, analysis, and other aspects that can help use the data for doing other processes.
And one can’t deny that data scientists often need to be well versed with statistics concepts as it helps in interpretation, analysis, collection, and even presentation of the unorganized data. It plays a crucial role in data science, and yet there are only a few scientists focus on learning this subject along with its practical applications and training of using statistics in real-time problems.
Looking at the requirement of such skills, many aspiring data scientists opt for short courses in the universities, self-help books, or tutorials that can help them learn this subject with ease.
Practical Statistics for Data Scientists
However, Practical statistics for data scientists, a book for learning statistics are grabbed the attention of a lot of people due to its claims and the topics included in the book.
So if you are an aspiring data scientist and want to learn these methods for doing your work with ease, you can opt for this book named – Practical statistics for data scientists. To help you better understand how this book can be helpful to you, here is a detailed review of the same.
What Topics Does The Book Cover?
This book is formatted to cover all the topics that can be beneficial for the data scientists to analyze and collect their data. Here are some of the following concepts covered in detail in the book:
- The methods to learn from the data by using statistical machine learning.
- Role of exploratory data analysis in the data science
- Techniques for classifying and predicting the categories for a particular record or data.
- Principles of experimental design and its applications.
- Methods to use regression for estimating the outcome and for detecting anomalies
- Ways to use random sampling methods for reducing the bias and yielding a higher quality data set.
- Techniques to extract meaning from the data that is unlabelled/uncategorized.
A lot of buyers have praised this book for the wide range of concepts it covers. The readers have also found this book engaging, and they have even liked it as it gives a broad overview of the statistics. This book is ideal for the people who are started with their careers in data science provides an excellent introduction to the statistical methods and concepts that come handy in the field. Based on all the reviews received, here is a list of pros and cons that you can consider while getting this book:
Practical statistics can be challenging for people with very little to no knowledge about the core statistics concepts, techniques, methods, calculations, and applications. This book is suitable for the people who know the subject and want to know more concepts in the statistics. It is an excellent introductory book for people starting in the field of data science. Instead of using the internet and other various sources to learn techniques, one could invest in this book to get all the information in one place and use it as a point of reference when required.
Practical Statistics for Data scientists is a book that you can opt for if you have some exposure to the basic statistical concepts and know the R programming language. However, you can also learn the basic concepts and then opt for this book to make it easy to understand the calculations, techniques, and methods mentioned in the book. Overall, the book delivers what it claims and can help data scientists understand the crucial concepts for analysis.