Arduino Data Communications. Learn how to configure databases, MQTT, REST APIs, and store data over LoRaWAN, HC-12, and GSM

Robert Thas John

In our modern, internet-connected world, where billions of devices constantly collect and send data to systems to be stored and processed, it’s surprising how the intricacies of data transmission and storage are often overlooked in the IoT domain. With Arduino Data Communications, you'll bridge the knowledge gap and become an expert in collecting data from IoT sensors, transmitting data, and configuring your own databases.This book is an exploration of IoT’s inner workings, guiding you through the process of setting up an end-to-end system that you can employ to prototype your own IoT solutions, using easy-to-follow examples. It begins with a general overview of the Arduino ecosystem, acquainting you with various sensors and shields and unveiling the art of data collection. You’ll then explore data formats and methods to store data, both locally and on database servers. As you progress through the chapters, you’ll learn how to set up REST and MQTT infrastructure to communicate with databases and get hands-on with LoRaWAN, Ethernet, cellular, HC-12, and RS-485. The final chapters are your training ground for real-world projects, imparting the essential knowledge you need to tackle complex challenges with confidence.By the end of this Arduino book, you'll have seamlessly configured an end-to-end system, all while immersing yourself in practical scenarios that bring the world of IoT to life.

The Data Science Workshop. A New, Interactive Approach to Learning Data Science

Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, ...

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results.Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding.Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book.Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.

The Data Science Workshop. Learn how you can build machine learning models and create your own real-world data science projects - Second Edition

Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, ...

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities.The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search.Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch.By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.