What are the upcoming data science books?

Data science is the study and analysis of data using modern computerised and programming techniques and then take advantage of that study for a company or industry for decision making and also making business predictions. It is a relatively newer field but is getting attention of the industries for enhancing their capability to survive and sustain in the tough business environment today.
A person who is interested in making a carrying data science must have some basic knowledge of computer programming especially in some modern coding practices under 'Python' or 'R'. A knowledge in cloud computing would also be advantageous.
The job of a data scientist involves logical analysis of data which can help in the progress and improvement of a particular business entity. He has to find out what type of data analysis is going to give some results please can be used for enhancement of the business of the company or taking decisions for diversification etc. Today's business has become very dynamic and it is imperative that by doing data analysis companies have to take quick decisions and sustain in the market.
Data science is a good carrier and there is a good demand of data scientist as on today and many companies are looking for people having skills in this particular area.
I can suggest some of the basic books which will help a person to understand about data science and how to go about it and then take a decision to go for certain courses through which one can learn the intricacies of this subject. Some basic books are as follows -
(1) Introducing Data Science by Davy Cielen.
(2) Introducing Data Science for Beginners 2022 by A. Ali.

Data Science books should be treated as bible for the aspirants to grasp the basics of data science. This domain would provide the basics of machine learning, statistics, programming R, python, deep learning etc.
I am listing a few books in this area which could be helpful to the newbies for acquisition of knowledge in this branch. Such books in this line are as follows-
1) Python Data Science Handbook - By Jake Vander Plas
It deals with Python with the inclusion of valuable tips in Jupyter, I python, Numpy, Pandas, Matplotib, etc with the detailed explanations of the different algorithms.
2) Introspection to Machine Learning- By Andrees Muller
This book could be the right choice for the aspirants interested to have deep understanding of machine learning algorithms and its applicability in the practical domain.
3) Machine Learning with Python- By Sebastian Raschka and Vahid Mirjalili
This book could be helpful to the readers for understanding the Machine Learning process with the easy assimilation of the different tools indicated in the book. Sometimes the readers would be required minor adjustments to accommodate the latest code versions.
4) Smarter Data Science- By Neal Fishman, Cole Stryker and Grady Bood
This book would be immensely helpful for the managers,IT professionals and analysts to extend their data science programmes to be more realistic, predictable, reproducible and ultimately beneficial for the entire organisation. This book could be a road map for the users to make faster but precise and insightful decision making.
Other books in this line are as follows-
1) Big Data - By Victor Mayer and Kenneth Cukier
2) Hands on Machine Learning with Sckit Learning- By Keras Tensor Flow

Data Science is an emerging field and many opportunities are visible in this line. This subject involves the processing of vast data using various scientific methods, algorithms, and processes. This will be helpful to understand the hidden patterns in the raw data. This analysis is very crucial for industries and other business houses to make a correct decision and to go forward.
Like in any other subject, here also you will find many books. But the following may be useful.
1. Data Science from Scratch: This book is authored by Joel Grus. Published by ?O'Reilly. This book is being followed by many engineering graduates who wanted to make data science their career. This book will help you in learning mathematics, Statistics and hacking skills that are useful for a Data Scientist.
2. Data Science For Dummies: This book is written by Lillian Pierson and published by ?John Wiley & Sons. This book is very useful for IT professionals to have a primary idea of this subject.
3. Storytelling with Data: This book is authored by Cole Nussbaumer Knaflic and published by Wiley. The fundamentals of data visualization and how to communicate effectively with data are dealt with in detail in this book, This book mostly discusses theoretical aspects with many real-world examples.
4. Data Science and Big Data Analytics. The name of the author is EMC Education Services and published by
Wiley. It covers the activities and methods and tools that are being used by data scientists. concepts, principles, and practical applications etc are discussed in detail in this book.
5. R for Data Science: Hadley Wickham is the author and published by O'Reilly. This book gives you the steps of importing, exploring, and modelling your data and communicating the results. Each chapter will have some exercises that will help in understanding the subject well.

Data science is a field that involves using statistical and computational techniques to extract insights and knowledge from data. It encompasses a wide range of techniques, including data cleaning and preparation, statistical modeling, machine learning, and data visualization.
Here are some steps you can take to get started with learning data science:

1. Learn the basics of programming: Data science requires a strong foundation in programming, especially in Python or R. Start with learning the basics of programming concepts and syntax, and then move on to more advanced topics such as data structures and algorithms.
2. Learn statistics and probability: Understanding basic statistical concepts and probability is essential for data science. You can start with learning the basics of descriptive statistics, probability distributions, and inferential statistics.
3. Learn data visualization and data wrangling: Data visualization is an important part of data science, and it will help you to understand data and communicate insights effectively. Data wrangling is also an important step in data science, and will help you to clean and prepare data for analysis.
4. Learn machine learning: Machine learning is a key component of data science, and it will help you to train models that can learn from data, and make predictions and decisions.
5. Practice: The best way to learn data science is by doing. You can start with working through tutorials, exercises and then building your own projects.

Books: Here are a few popular books that can help beginners learn data science:

1. "Python for Data Analysis" by Wes McKinney
2. "Data Science from Scratch" by Joel Grus
3. "Introduction to Machine Learning with Python" by Andreas Müller and Sarah Guido
4. "R for Data Science" by Hadley Wickham and Garrett Grolemund
5. "Think Stats" by Allen Downey

Other than books:

1. Online courses and tutorials: Websites like Coursera, edX, DataCamp, and DataQuest offer a wide range of data science courses and tutorials.
2. Data Science Bootcamps: Data science bootcamps are intensive, in-person or online programs that teach the skills needed to become a data scientist.
3. Participating in data science competitions and hackathons: Kaggle, HackerRank, and similar platforms offer data science competitions and hackathons where you can practice your skills and learn from other participants.
4. Joining online communities: Joining online data science communities like Data Science Central, Kaggle, and DataTau can be a great way to connect with other data scientists and learn from their experience.

Great to hear that you're interested in learning Data Science! Data Science is an interdisciplinary field that involves extracting knowledge and insights from structured and unstructured data. It combines elements of statistics, computer science, and domain-specific knowledge to extract insights from data. As a beginner, there are several ways to learn Data Science.

First, learn the basics:
Start with the basics of programming languages like Python or R, which are commonly used in Data Science. You can take online courses or read books to learn these programming languages.

Learn data manipulation:
Once you have a grasp on the basics, you can learn how to manipulate and clean data using libraries like Pandas or dplyr.

Learn data visualization:
Data visualization is an important aspect of Data Science. You can learn how to create visualizations using libraries like Matplotlib or ggplot2.

Learn Machine Learning:
Machine Learning is a subset of Data Science and involves training algorithms to make predictions or decisions based on data. You can learn Machine Learning algorithms and concepts like supervised and unsupervised learning.

Work on Projects:
Once you have the basics down, start working on projects to apply your knowledge. This will help you gain practical experience and build your portfolio.

Some good books to learn Data Science are "Python for Data Analysis" by Wes McKinney, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurelien Geron, and "Data Science from Scratch" by Joel Grus.

Other than books, there are several ways to learn Data Science, including online courses, workshops, and bootcamps. There are also many online communities where you can connect with other Data Science learners and professionals, such as Kaggle, Stack Overflow, and Data Science Central.

In summary, to learn Data Science, start with the basics of programming, learn data manipulation and visualization, delve into Machine Learning, work on projects, and continue learning through books, online courses, and community involvement.