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Let us explore each book with some of its main learning and lessons –
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The book came out in 2019 and is a detailed guide on using data science techniques and programming languages Python, R, and data mining.
Data science is a booming subject in today’s times, and this book covers its entirety, from basics to advanced level knowledge. The book will surely be a one-stop solution to readers’ queries and doubts. It has a total number of 500 exercises for readers to indulge in. In addition, the book provides hands-on information on various analyses and data science calculations.
This book was published in 2019 and consisted of data science libraries, modules, toolkits, and frameworks.
The book starts with the basics and lets the reader absorb every bit of information before moving to advanced levels of learning. Next, the author provides core data science tools and hacking skills one needs to know to become a data scientist. Finally, he helps dig out answers and get hold of all the data an individual comes across in data mining.
A Case Study Approach To Gaining Valuable Insights From Real Data With Machine Learning
The book came out in 2021 and is written in a case study format to cover all the arguments and opinions. The author shares some valuable insights on machine learning as well.
The book is a project in itself with predictive models and data sets. It progresses with practical exercises and includes many processes and calculations concerning data science projects, machine learning, data exploration, etc. Using a case study approach; the author shares many machine learning models, algorithmic fairness, XGBoost, and SHAP interaction values.
The book was released in 2019 and compiled analytical insights from the four authors on machine learning and data science.
The book is a product of all the hard work and experience gained by the authors over time. It is a detailed account of machine learning techniques, which is good for graduate-level and undergraduate-level students. The authors present major theorem proofs and derivations. They have kept the writing very clear, explaining the statistics to denote the variations in machine learning algorithms.
The Ultimate Beginners’ Guide To Learning Python Data Science Step By Step
The book was published in 2019 and is treated as a beginner’s guide to learning python programming and the basics of data science.
The book is good for anyone learning the Python programming language, irrespective of the level they operate. The author states that to master the language, one must keep practicing and access relevant study materials. He also believes that the only limitation is aspiration; other possibilities can be explored with consistency.
The book was published in 2019 and dictated how to build projects to streamline large datasets.
The book makes readers use their abilities to explore massive data sets and work with them. If a reader wants to create machine learning models with the help of Dask-ML, this book is the perfect pick. In addition, the author talks about the use of AWS and Docker to build interactive visualizations and clusters.
The book came out in 2017 and is prominently focused on statistics and mathematical training for data science analysis.
The book is quite popular for featuring Python examples with a perspective of applying several methods and techniques involving practical guidance. As a result, the readers can learn how to avoid data misuse and only consider relevant and important information in data science analysis. Those acquainted with Python and R can easily relate, and the book can bridge the gaps between doubts and queries.
The book was released in 2016 and is a handbook for people who constantly need to acquire knowledge and references regarding data science techniques and strategies.
The author wrote the book as a comprehensive guide so that readers willing to learn Python can have access to it anytime with straight on-point examples and techniques. People interested in the Python language come across several difficulties and errors while programming, this book help eliminate such issues.
Learning To Program With AI, Big Data, And The Cloud
The book was published in 2019. It provides a flexible approach to learning Python programming and context to machine learning, cloud space, and AI.
The book holds real-world data sets and AI technologies. This one aspect helps students have an in-depth understanding of business projects belonging to different industries. In addition, the book covers new topics and applications associated with data science. Finally, it contains a collection of over one hundred exercises, examples, and projects.
4 Books In 1: Python Programming, Data Analysis, Machine Learning. A Complete Overview To Master The Art Of Data Science From Scratch Using Python For Business
The book released in 2020 is a collection of four books in one. The key topics include Python programming, machine learning, and data analysis.
Data science is becoming more exciting and inviting to people from different fields; data scientists require a lot of knowledge and therefore are expensive to hire and difficult to retain. This book is the bundle for programmers, project managers, and software engineers who always want to walk in parallel with the technology. In addition, Python programming is becoming a must-have language skill, and this book can help readers start their journey from scratch.
This has been a guide to the List of Top 10 Data Science With Python Books. Here we briefly discuss them, review them, & explain why they are the best reads. You may also have a look at these suggested books below –