11 Books Every Data Scientist Must Read In 2023

Data science is a field that is constantly evolving and it’s important for data scientists to stay up-to-date with the latest developments. Books Every Data Scientist Must Read – One way to do this is by reading books that provide insights, guidance, and inspiration.

Here are some must-read books for every data scientist:

  1. “Data Science from Scratch: First Principles with Python” by Joel Grus
  2. “Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython” by Wes McKinney
  3. “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
  4. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  5. “The Elements of Statistical Learning: Data Mining, Inference, and Prediction” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
  6. “Storytelling with Data: A Data Visualization Guide for Business Professionals” by Cole Nussbaumer Knaflic
  7. “Data Smart: Using Data Science to Transform Information into Insight” by John W. Foreman
  8. “Data Mining: Practical Machine Learning Tools and Techniques” by Ian H. Witten, Eibe Frank, and Mark A. Hall
  9. “The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t” by Nate Silver
  10. “Machine Learning Yearning” by Andrew Ng
  11. “Applied Predictive Modeling” by Max Kuhn and Kjell Johnson

    Bonus Book: “Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow” by Sebastian Raschka

These books cover various aspects of data science, including programming languages, data analysis and visualization, machine learning, deep learning, and predictive modeling. Reading them can help data scientists stay informed, improve their skills, and keep up with the latest trends and developments in the field.

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