Beginning Data Science, IoT, and AI on Single Board Computers : Core Skills and Real-World Application with the BBC Micro:bit and XinaBox

Learn to use technology to undertake data science and to leverage the Internet of Things (IoT) in your experimentation. Designed to take you on a fascinating journey, this book introduces the core concepts of modern data science. You'll start with simple applications that you can undertake on a BBC micro:bit and move to more complex experiments with additional hardware. The skills and narrative are as generic as possible and can be implemented with a range of hardware options. One of the most exciting and fastest growing topics in education is data science. Understanding how data works, and how to work with data, is a key life skill in the 21st century. In a world driven by information it is essential that students are equipped with the tools they need to make sense of it all. For instance, consider how data science was the key factor that identified the dangers of climate change -- and continues to help us identify and react to the threats it presents. This book explores the power of data and how you can apply it using hardware you have at hand. You'll learn the core concepts of data science, how to apply them in the real world and how to utilize the vast potential of IoT. By the end, you'll be able to execute sophisticated and meaningful data science experiments - why not become a citizen scientist and make a real contribution to the fight against climate change. There is something of a digital revolution going these days, especially in the classroom. With increasing access to microprocessors, classrooms are are incorporating them more and more into lessons. Close to 5 million BBC micro:bits will be in the hands of young learners by the end of the year and millions of other devices are also being used by educators to teach a range of topics and subjects. This presents an opportunity: microprocessors such as micro:bit provide the perfect tool to use to build 21st century data science skills. Beginning Data Science and IoT on the BBC micro:bit provides you with a solid foundation in applied data science. What You'll Learn · Use sensors with a microprocessor to gather or "create" data · Extract, tabulate, and utilize data it from the microprocessor · Connect a microprocessor to an IoT platform to share and then use the data we collect · Analyze and convert data into information Who This Book Is For Educators, citizen scientists, and tinkerers interested in an introduction to the concepts of IoT and data on a broad scale. Table of Contents 4 About the Authors 10 Foreword 11 Chapter 1: Introducing Data Science 14 1.1 Introducing Data Science 15 1.2 Using Temperature 16 1.3 Measuring Temperature 17 1.4 Controlling Data 19 1.5 Understanding the Tools 22 1.6 Data Quality 23 1.7 Data Capturing 24 1.8 Experimenting with Temperature 27 1.9 Analyzing Our Results 29 Analysis of Extremities 31 Analysis of Averages (Central Tendency) 31 Random Insights 31 Analysis of Data Quality 32 Introspection 33 1.10 Summary 34 Chapter 2: Data Science Goes Digital 35 2.1 Making It Digital 35 2.2 Measuring Temperature Digitally 36 The Role of the Microprocessor 37 2.3 Building Digital Tools 38 2.4 Using the BBC micro:bit As a Thermometer 39 2.5 Coding Guidelines Used in This Book 40 2.6 Using the micro:bit Code Editors 40 2.7 Using the “No-Code” Option 41 2.8 Coding the micro:bit Thermometer 41 2.9 Comparing Analog and Digital Thermometers 45 2.10 Analysis 47 Analysis of Extremities 47 Analysis of Averages: “Central Tendency” 48 Analysis of Data Quality 48 Introspection 48 2.11 Why the micro:bit? 49 2.12 What Kit Do We Need? 51 2.13 Selecting Our Toolkit 53 2.14 Guide to Hardware Requirements 57 2.15 Summary 60 Chapter 3: Experimenting with Weather 61 3.1 Introduction 61 3.2 Measuring Weather 62 3.3 Choosing the Data to Measure 63 3.4 Experimenting with Weather 66 3.5 Building Our Weather Station Tool 68 3.6 Coding Our Weather Station 71 3.7 Upgrading the Display 78 3.8 Experimental Design 80 3.9 Visualizing the Data We Collected 81 3.10 Analyzing the Data We Collected 86 3.11 Summary 89 Chapter 4: Working with Large Data Sets 90 4.1 Experimental Design 91 4.2 Using the micro:bit As a File Storage Device 92 4.3 Accessing Files on the micro:bit 95 4.4 Transferring Files onto a Computer 96 4.5 Hardware Requirements 97 4.6 Storing Sensor Data in a File 98 4.7 Measuring How Many Data Points We Can Store 102 4.8 Replicating the Weather Station Experiment with File Storage 110 4.9 Addressing Memory Limitations 113 4.10 Expanding Data Storage Capacity 114 4.11 Summary 114 Chapter 5: Introduction to Data Analysis 115 5.1 Expanding Our Analysis Tools 115 5.2 Software for Data Analysis 116 5.3 Selecting a Spreadsheet Program 117 5.4 Measuring Correlation 119 5.5 Calculating Correlation Scores 121 5.6 Understanding a Correlation Coefficient/Score 125 5.7 Calculating the Correlation Score for Weather Data 126 5.8 Using Other Analysis Functions 128 5.9 Using Visualization Tools 130 5.10 Reporting 132 5.11 Statistical Significance 133 5.12 Summary 136 Chapter 6: Introducing IoT to Data Science 137 6.1 The Weakness in Our Data Science Toolkit 137 6.2 Internet of Things Overview 138 6.3 Anatomy of the Cloud 142 6.4 Transferring Data from a micro:bit 145 6.5 Wireless Communication Options for IoT 146 6.6 Transmitting Data Using a Serial Connection 148 6.7 Summary 150 Chapter 7: Using Bluetooth for Data Science 151 7.1 What Is Bluetooth? 151 7.2 Why Use Bluetooth? 153 7.3 Using BLE on micro:bit 154 7.4 Building a BLE Weather Station with Bluetooth UART 156 7.5 Using the Serial Bluetooth Terminal App 158 7.6 Coding the BLE Weather Station 159 7.7 Other Options for BLE on micro:bit 163 7.8 Summary 163 Chapter 8: Investigating the micro:bit Radio 164 8.1 Standards Are Important 164 8.2 Using Radio for Input/Output 165 8.3 Using Radio to Build a Network 166 8.4 Choosing MakeCode or MicroPython 167 8.5 MakeCode Radio Groups 168 8.6 Nodes and a Collector 170 8.7 Building the Nodes 171 8.8 Building the Server/Collector 175 8.9 Summary 183 Chapter 9: Using Wi-Fi to Connect to the Internet 184 9.1 Defining Our IoT Weather Station 185 9.2 Building Our Wi-Fi Weather Station 185 9.3 Updating Firmware 188 9.4 Choosing an IoT Platform 189 9.5 Setting Up the IoT Platform 193 9.6 Adding Our Weather Station to the IoT Platform 195 9.7 Visualizing Data in the IoT Platform 200 9.8 Coding Our Wi-Fi Weather Station 205 9.9 Powering and Running the Weather Station 213 9.10 Viewing the Data Visualizations 216 9.11 Summary 218 Chapter 10: Introduction to Machine Learning and Artificial Intelligence 220 10.1 Artificial Intelligence 221 10.2 AI/ML? 223 10.3 ML/AI and Data Science 224 10.4 Thinking Like a Machine 227 10.5 Experimental Design 227 10.6 Hardware Requirements 229 10.7 Software 232 10.8 Using the Hardware 233 10.9 Analyzing the Data 235 10.10 Comparing Humans and Machines 239 10.11 Summary 241 Chapter 11: Using ML Services 242 11.1 Defining Our IoT Application 242 11.2 Choosing an IoT Service Provider 243 11.3 Setting Up Microsoft Azure: Cloud Computing Services 244 11.4 Creating an IoT Hub Using Azure Portal 245 11.5 Setting Up a Weather Prediction Model in Azure Machine Learning Studio 251 11.6 Creating a Workflow Using Azure Logic Apps 260 11.7 Setting Up the workflow 263 Step 1: Sending Data from the Weather Station Instrument 264 Step 2: Passing Data into the ML Service 267 Step 3: Interpreting (“Parsing”) Results from the ML Service (“Parse JSON”) 273 Step 4: Setting Up a Variable to Store the Data from the ML Service 276 Step 5: Sending Data Back to the Weather Station Instrument 278 11.8 Testing the Workflow 280 11.9 Summary 282 Chapter 12: Connecting an Edge Device to the IoT Application 283 12.1 Choosing the Hardware 283 12.2 The Role of the Edge Device 285 12.3 Building the Edge Device 286 12.4 Coding the Edge Device 287 12.5 Using the Edge Device 292 12.6 Improving the Edge Device 293 12.7 Peering Under the Hood of the IoT Application 293 Parse JSON 295 Initialize Variable 295 Response 296 When a HTTP Request Is Received 297 12.8 Data Analysis 298 12.9 Summary 300 Chapter 13: Consolidating our Learnings 302 13.1 Am I a Data Scientist? 302 13.2 Becoming a Data Scientist 303 13.3 Debunking Some Myths 304 13.4 Extrapolating Learnings 307 13.5 Applying Our Knowledge to Different Builds 311 13.6 Ethical Considerations 313 13.7 Summary 315 Index 316 Learn to use technology to undertake data science and to leverage the Internet of Things (IoT) in your experimentation. Designed to take you on a fascinating journey, this book introduces the core concepts of modern data science. You'll start with simple applications that you can undertake on a BBC micro:bit and move to more complex experiments with additional hardware. The skills and narrative are as generic as possible and can be implemented with a range of hardware options. One of the most exciting and fastest growing topics in education is data science. Understanding how data works, and how to work with data, is a key life skill in the 21st century. In a world driven by information it is essential that students are equipped with the tools they need to make sense of it all. For instance, consider how data science was the key factor that identified the dangers of climate change -- and continues to help us identify and react to the threats it presents. This book explores the power of data and how you can apply it using hardware you have at hand. You'll learn the core concepts of data science, how to apply them in the real world and how to utilize the vast potential of IoT. By the end, you'll be able to execute sophisticated and meaningful data science experiments - why not become a citizen scientist and make a real contribution to the fight against climate change. There is something of a digital revolution going these days, especially in the classroom. With increasing access to microprocessors, classrooms are are incorporating them more and more into lessons. Close to 5 million BBC micro:bits will be in the hands of young learners by the end of the year and millions of other devices are also being used by educators to teach a range of topics and subjects. This presents an opportunity: microprocessors such as micro:bit provide the perfect tool to use to build 21st century data science skills. Beginning Data Science and IoT on the BBC micro:bit provides you with a so lid foundation in applied data science. You will: Use sensors with a microprocessor to gather or "create" data ; Extract, tabulate, and utilize data it from the microprocessor ; Connect a microprocessor to an IoT platform to share and then use the data we collect ; Analyze and convert data into information ; Utilize the skills learned to create experiments that can contribute to the fight against climate change