Practitioner’s Guide to Data Science (Chapman & Hall/CRC Data Science Series)

The Book provides a practical look at data science in real-world industry settings, covering both technical and soft skills not always found in traditional academic materials. Key features include a focus on big data cloud platforms like Spark using R and Python with provided code examples for hands-on learning, as well as discussions on data pre-processing, data wrangling, and various modeling techniques such as regression, regularization methods, and tree-based models like random forest and boosted trees. The text also offers insights into the history of data science, typical data science project cycles, and the different roles within a data science team. Finally, it touches upon deep learning, covering Feedforward, Convolutional, and Recurrent Neural Networks, along with discussions on databases and SQL.

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