Why and how should you learn “Productive Data Science”?

What is Productive Data Science and what are some of its components?

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Efficiency in data science workflow

Data science and machine learning can be practiced with varying degrees of efficiency and productivity. Irrespective of the application area or specialization, a data scientist — beginner or seasoned professional — should strive to enhance his/her efficiency at all aspects of typical data science tasks,

• statistical analysis,
• visualization,
• model selection, feature engineering,
• code quality testing, modularization,
• parallel processing,
• easy web-app deployment

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This means performing all of these tasks,

• at higher speed
• with faster debugging
• in a synchronized manner
• by taking full advantage of any and all available hardware resources

What should you expect to learn in this…