Introducing data science

Davy Cielen, Arno D. B. Meysman, and Mohamed Ali, Introducing data science: Big data, machine learning, and more, using Python tools, Manning, May 2016.

Introducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You'll explore data visualization, graph databases, the use of NoSQL, and the data science process. You'll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you'll have the solid foundation you need to start a career in data science.

Data Science in Production: Building Scalable Model Pipelines with Python

Ben Weber, Data Science in Production: Building Scalable Model Pipelines with Python, Independently published, 2020.

Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and deploy scalable model pipelines, data scientists can own more of the model production process and more rapidly deliver data products.

Decisive actions to emerge stronger in the next normal

Kevin Sneader, Shubham Singhal, and Bob Sternfels, What now? Decisive actions to emerge stronger in the next normal, McKinsey & Company, September 2020 (pdf)

1. Think of the return as a muscle
2. Focus on high-impact actions
3. Rebuild for speed
4. Reimagine the workforce from the top down
5. Make bold portfolio moves
6. Reset technology plans
7. Rethink the global footprint
8. Take the lead on climate and sustainability
9. Think about the role of regulation and government
10. Make purpose part of everything

台灣引興怎麼堅持豐田管理的零庫存

曾如瑩、管婺媛，工具機天王遇斷鏈潮，怎麼堅持豐田管理的零庫存，商業周刊，2020 年 08 月 31 日

邱奕嘉問（以下簡稱邱）：每次有重大天災，精實管理就會被拿出來檢討。因為低庫存者斷鏈，造成損失，反倒有庫存者，業績成長。經過這次疫情，你認為零庫存概念是否應該調整？

王慶華答（以下簡稱王）：豐田汽車當然（曾經）因為天災而斷鏈，但它也是全世界恢復最快的。企業講究長期利益，不是講短期利益的。斷貨（鏈）是事實沒錯，有存貨者，可能在疫情爆發這 3 個月中活得比別人好，但就是贏這 3 個月，之後呢？即便賺到比別人多一倍的利潤，也只有短期，庫存總會用完。

Linear Algebra and Optimization for Machine Learning: A Textbook

Charu C. Aggarwal, Linear Algebra and Optimization for Machine Learning: A Textbook, Springer, 1st ed, 2020.

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: