Data Science is an interdisciplinary field that combines statistical analysis, machine learning, and domain expertise to extract insights from data. With the exponential growth of data in recent years, data science has become an increasingly important field for businesses and organizations.
If you’re looking to learn more about data science, here are 8 books to consider:
- Data Science For Beginners
- Data Science From Scratch: First Principles With Python
- Python Data Science Handbook
- Practical Statistics for Data Scientists
- R for Data Science
- Storytelling With Data
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Data Science on AWS
1. Data Science For Beginners by Andrew Park
This book is a great introduction to data science for those with little to no programming experience. It covers the basics of data analysis, statistical inference, and machine learning.
2. Data Science From Scratch: First Principles With Python by Joel Grus
This book is a comprehensive guide to data science using the Python programming language. It covers topics such as data visualization, probability, and deep learning.
3. Python Data Science Handbook by Jake VanderPlas
This book is a must-read for anyone interested in data science with Python. It covers topics such as data manipulation, visualization, and machine learning with popular Python libraries like NumPy, Pandas, and Scikit-Learn.
4. Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce
This book is an excellent resource for anyone looking to brush up on their statistics skills. It covers topics such as probability, hypothesis testing, and regression analysis, all in the context of data science.
5. R for Data Science by Hadley Wickham and Garrett Grolemund
This book is a comprehensive guide to using the R programming language for data science. It covers topics such as data manipulation, visualization, and machine learning with popular R packages like ggplot2, dplyr, and tidyr.
6. Storytelling With Data by Cole Nussbaumer Knaflic
This book is a great resource for anyone looking to improve their data visualization skills. It covers topics such as chart selection, design principles, and storytelling techniques.
7. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
This book is a practical guide to machine learning with Python. It covers topics such as classification, regression, and deep learning using popular Python libraries like Scikit-Learn, Keras, and TensorFlow.
8. Data Science on AWS by Chris Fregly and Antje Barth
This book is a great resource for anyone interested in using Amazon Web Services (AWS) for data science. It covers topics such as data storage, processing, and analysis using AWS tools like S3, EMR, and Athena.
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