Productive and Efficient Data Science with Python: Best Practices Guide to Implementing Aiops
暫譯: 高效且富有成效的數據科學與 Python：實施 Aiops 的最佳實踐指南

Chapter 1: What is Productive and Efficient Data Science?Chapter Goal: To introduce the readers with the concept of doing data science tasks efficiently and more productively and illustrating potential pitfalls in their everyday work.No of pages - 10Subtopics- Typical data science pipeline- Short examples of inefficient programming in data science- Some pitfalls to avoid- Efficiency and productivity go hand in hand- Overview of tools and techniques for a productive data science pipeline- Skills and attitude for productive data science
Chapter 2: Better Programming Principles for Efficient Data ScienceChapter Goal: Help readers grasp the idea of efficient programming techniques and how they can be applied to a typical data science task flow.No of pages - 15Subtopics- The concept of time and space complexity, Big-O notation- Why complexity matters for data science- Examples of inefficient programming in data science tasks- What you can do instead- Measuring code execution timing
Chapter 3: How to Use Python Data Science Packages more ProductivelyChapter Goal: Illustrate handful of tricks and techniques to use the most well-known Python data science packages - Numpy, Pandas, Matplotlib, Seaborn, Scipy - more productively.No of pages - 20Subtopics- Why Numpy is faster than regular Python code and how much- Using Numpy efficiently- Using Pandas productively- Matplotlib and Seaborn code for and productive EDA- Using SciPy for common data science tasks
Chapter 4: Writing Machine Learning Code More ProductivelyChapter Goal: Teach the reader about writing efficient and modular machine learning code for productive data science pipeline with hands-on examples using Scikit-learn.No of pages - 15Subtopics- Why modular code for machine learning and deep learning- Scikit-learn tools and techniques- Systematic evaluation of Scikit-learn ML algorithms in automated fashion- Decision boundary visualization with custom function- Hyperparameter search in Scikit-learn
Chapter 5: Modular and Productive Deep Learning CodeChapter Goal: Teach the reader about mixing modular programming style in deep learning code with hands-on examples using Keras/TensorFlow.No of pages - 25Subtopics- Why modular code and object-oriented style for deep learning- Wrapper functions with Keras for faster deep learning experimentations- A single function to streamline image classification task flow- Visualize activation functions of neural networks- Custom callback functions in Keras and their utilities- Using Scikit-learn wrapper for hyperparameter search in Keras
Chapter 6: Build Your Own Machine Learning Estimator/PackageChapter Goal: Illustrate how to build a new Python machine learning module/package from scratch.No of pages - 15Subtopics- Why write your own ML package/module?- A simple example vs. a data scientist's example- A good, old Linear Regression estimator - with a twist- How do you start building?- Add utility functions- Do more with object-oriented approach
Chapter 7: Some Cool Utility PackagesChapter Goal: Introduce the readers to the idea of executing data science tasks efficiently by going beyond traditional stack and utilizing exciting, new libraries.No of pages - 20Subtopics- The great Python