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Books I’m reading at the moment:
Simon Monk’s latest book follows the Cookbook format. It covers the electronics you might need to know in order to build or troubleshoot an Electronics Project - especially those based on the Raspberry Pi or Arduino.
The book starts with the basics: a little theory is followed by chapters on resistors, capacitors and inductors, diodes, transistors and ICs, switches and relays.
The next twelve chapters cover recipes for what the book describes as pretty much anything electronic that you might like to design. Topics include power sources, sensors, motors and displays.
The last two chapters cover construction and tools.
Simon Monk knows what he’s talking about, and he writes clearly and simply.
If you’re thinking of creating a project based on a Raspberry Pi or Arduino, or you want to understand how someone else’s project works, this is a great book to get you started.
The only topics I felt were missing were how to read a datasheet, and how to order components. These are essential skills for electronics makers, and they can be daunting for beginners. Maybe Simon will add them in a second edition!
These are minor shortcomings.
Overall, this is an excellent book, and I recommend it.
Anyone who has worked with real word data knows that it is nasty, brutish and long. It needs cleaning before it can be used, and before you can clean it you need to understand it.
These days, most data scientists use pandas to analyse, clean and crunch structured data.
The author of this book is the creator and BDFL of pandas. His enthusiasm and expertise shine through the book.
After a gentle introduction to preparing the environment (Ch 1), to Python IPython and Jupyter (Ch 2), and built-in Python features (Ch 3), Chapter 4 covers NumPy and Vectorised computation.
Chapter 5 introduces pandas; ch 6 covers HDF5 format. Chapters 7 and 8 are task-oriented, covering Data Cleaning and Preparation.
Chapter 9 covers plotting with matplotlib, pandas and seaborn. Chapter 10 covers Data Aggregation and Grouping. Chapter 11 looks at Time Series Data, Chapter 12 looks at advanced pandas topics, and Ch 13 looks at modelling in Python. Chapter 14 has some great real-world examples of Data Analysis.
As you can see the book covers a wide range of topics. The explanations are clear and easy to follow. The data and jupyter notebooks are available on GitHub. If you like learning from books, or need an approachable reference, this book is ideal.
I came across it while taking Coursera’s Introduction to Data Science in Python, where it’s recommended as optional reading. I’ve found the book invaluable for consolidating and extending my knowledge of Python’s data analysis tools.