"Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili
"Deep Learning" by Yoshua Bengio, Ian Goodfellow, and Aaron Courville
"Data Science from Scratch" by Joel Grus
"Python for Data Analysis" by Wes McKinney
"Data Science for Business" by Foster Provost and Tom Fawcett
"R for Data Science" by Hadley Wickham and Garrett Grolemund
"The Hundred-Page Machine Learning Book" by Andriy Burkov
"Data Science Handbook" by Field Cady, Carl Shanfield and Matt Kusner
"Data Wrangling with Python" by Jacqueline Kazil and Katharine Jarmul
"Big Data: A Revolution That Will Transform How We Live, Work, and Think" by Viktor Mayer-Schönberger
These books cover a range of topics in data science, including machine learning, data analysis, data visualization, and big data. They also provide a good introduction to programming languages such as Python and R, which are commonly used in data science. They provide the basis for understanding and implementing key data science concepts and techniques, including data exploration, data cleaning, feature engineering, and model evaluation. They also cover the use of tools such as Jupyter Notebook and Pandas, which are widely used in the data science community.
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