Data Science Book | practical statistics for data scientists | python data science handbook
Here are 10 highly regarded data science books along with their respective authors.
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The Elements of Statistical Learning, Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
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Python for Data Analysis by Wes McKinney.
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Data Science for Business What You Need to Know About Data Mining and Data Analytic Thinking by Foster Provost and Tom Fossett.
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Hands on Machine Learning was sick at Learn Keras and Tensorflow concepts, Tools and Techniques to Build Intelligent Systems by Aurelian Jaren.
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Pattern Recognition and Machine Learning by Christopher M Bishop.
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Big Data A Revolution That Will Transform How We Live, work, and Think.
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By Victor Mayer Schonberger and Kenneth Cukier.
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Data Science from Scratch First Principles with Python by Joel Grass.
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Deep Learning by Ian Goodfellow, Yoshua Banjo and Aaron Courville.
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R for data science Import, tidy, transform, visualize, and model Data by Hadley Wickham and Garrett Groland.
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The Data Science Handbook by Field Katie.
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These books cover a range of topics in data science, including statistical learning, data analysis with Python, machine learning, big data, deep learning, and data visualization.
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They are highly recommended resources for individuals interested in expanding their knowledge and skills in the field of data science.
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