Beginning Data Science, IoT, and AI on Single Board Computers: Core Skills and Real-World Application with the BBC Micro:bit and XinaBox
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress L. P.
2020
|
| Schlagworte: | |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (323 Seiten) |
| ISBN: | 9781484257661 |
Internformat
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| 245 | 1 | 0 | |a Beginning Data Science, IoT, and AI on Single Board Computers |b Core Skills and Real-World Application with the BBC Micro:bit and XinaBox |
| 264 | 1 | |a Berkeley, CA |b Apress L. P. |c 2020 | |
| 264 | 4 | |c ©2020 | |
| 300 | |a 1 Online-Ressource (323 Seiten) | ||
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| 505 | 8 | |a Intro -- Table of Contents -- About the Authors -- Foreword -- Chapter 1: Introducing Data Science -- 1.1 Introducing Data Science -- 1.2 Using Temperature -- 1.3 Measuring Temperature -- 1.4 Controlling Data -- 1.5 Understanding the Tools -- 1.6 Data Quality -- 1.7 Data Capturing -- 1.8 Experimenting with Temperature -- 1.9 Analyzing Our Results -- Analysis of Extremities -- Analysis of Averages (Central Tendency) -- Random Insights -- Analysis of Data Quality -- Introspection -- 1.10 Summary -- Chapter 2: Data Science Goes Digital -- 2.1 Making It Digital -- 2.2 Measuring Temperature Digitally -- The Role of the Microprocessor -- 2.3 Building Digital Tools -- 2.4 Using the BBC micro:bit As a Thermometer -- 2.5 Coding Guidelines Used in This Book -- 2.6 Using the micro:bit Code Editors -- 2.7 Using the "No-Code" Option -- 2.8 Coding the micro:bit Thermometer -- 2.9 Comparing Analog and Digital Thermometers -- 2.10 Analysis -- Analysis of Extremities -- Analysis of Averages: "Central Tendency" -- Analysis of Data Quality -- Introspection -- 2.11 Why the micro:bit? -- 2.12 What Kit Do We Need? -- 2.13 Selecting Our Toolkit -- 2.14 Guide to Hardware Requirements -- 2.15 Summary -- Chapter 3: Experimenting with Weather -- 3.1 Introduction -- 3.2 Measuring Weather -- 3.3 Choosing the Data to Measure -- 3.4 Experimenting with Weather -- 3.5 Building Our Weather Station Tool -- 3.6 Coding Our Weather Station -- 3.7 Upgrading the Display -- 3.8 Experimental Design -- 3.9 Visualizing the Data We Collected -- 3.10 Analyzing the Data We Collected -- 3.11 Summary -- Chapter 4: Working with Large Data Sets -- 4.1 Experimental Design -- 4.2 Using the micro:bit As a File Storage Device -- 4.3 Accessing Files on the micro:bit -- 4.4 Transferring Files onto a Computer -- 4.5 Hardware Requirements -- 4.6 Storing Sensor Data in a File | |
| 505 | 8 | |a 4.7 Measuring How Many Data Points We Can Store -- 4.8 Replicating the Weather Station Experiment with File Storage -- 4.9 Addressing Memory Limitations -- 4.10 Expanding Data Storage Capacity -- 4.11 Summary -- Chapter 5: Introduction to Data Analysis -- 5.1 Expanding Our Analysis Tools -- 5.2 Software for Data Analysis -- 5.3 Selecting a Spreadsheet Program -- 5.4 Measuring Correlation -- 5.5 Calculating Correlation Scores -- 5.6 Understanding a Correlation Coefficient/Score -- 5.7 Calculating the Correlation Score for Weather Data -- 5.8 Using Other Analysis Functions -- 5.9 Using Visualization Tools -- 5.10 Reporting -- 5.11 Statistical Significance -- 5.12 Summary -- Chapter 6: Introducing IoT to Data Science -- 6.1 The Weakness in Our Data Science Toolkit -- 6.2 Internet of Things Overview -- 6.3 Anatomy of the Cloud -- 6.4 Transferring Data from a micro:bit -- 6.5 Wireless Communication Options for IoT -- 6.6 Transmitting Data Using a Serial Connection -- 6.7 Summary -- Chapter 7: Using Bluetooth for Data Science -- 7.1 What Is Bluetooth? -- 7.2 Why Use Bluetooth? -- 7.3 Using BLE on micro:bit -- 7.4 Building a BLE Weather Station with Bluetooth UART -- 7.5 Using the Serial Bluetooth Terminal App -- 7.6 Coding the BLE Weather Station -- 7.7 Other Options for BLE on micro:bit -- 7.8 Summary -- Chapter 8: Investigating the micro:bit Radio -- 8.1 Standards Are Important -- 8.2 Using Radio for Input/Output -- 8.3 Using Radio to Build a Network -- 8.4 Choosing MakeCode or MicroPython -- 8.5 MakeCode Radio Groups -- 8.6 Nodes and a Collector -- 8.7 Building the Nodes -- 8.8 Building the Server/Collector -- 8.9 Summary -- Chapter 9: Using Wi-Fi to Connect to the Internet -- 9.1 Defining Our IoT Weather Station -- 9.2 Building Our Wi-Fi Weather Station -- 9.3 Updating Firmware -- 9.4 Choosing an IoT Platform -- 9.5 Setting Up the IoT Platform | |
| 505 | 8 | |a 9.6 Adding Our Weather Station to the IoT Platform -- 9.7 Visualizing Data in the IoT Platform -- 9.8 Coding Our Wi-Fi Weather Station -- 9.9 Powering and Running the Weather Station -- 9.10 Viewing the Data Visualizations -- 9.11 Summary -- Chapter 10: Introduction to Machine Learning and Artificial Intelligence -- 10.1 Artificial Intelligence -- 10.2 AI/ML? -- 10.3 ML/AI and Data Science -- 10.4 Thinking Like a Machine -- 10.5 Experimental Design -- 10.6 Hardware Requirements -- 10.7 Software -- 10.8 Using the Hardware -- 10.9 Analyzing the Data -- 10.10 Comparing Humans and Machines -- 10.11 Summary -- Chapter 11: Using ML Services -- 11.1 Defining Our IoT Application -- 11.2 Choosing an IoT Service Provider -- 11.3 Setting Up Microsoft Azure: Cloud Computing Services -- 11.4 Creating an IoT Hub Using Azure Portal -- 11.5 Setting Up a Weather Prediction Model in Azure Machine Learning Studio -- 11.6 Creating a Workflow Using Azure Logic Apps -- 11.7 Setting Up the workflow -- Step 1: Sending Data from the Weather Station Instrument -- Step 2: Passing Data into the ML Service -- Step 3: Interpreting ("Parsing") Results from the ML Service ("Parse JSON") -- Step 4: Setting Up a Variable to Store the Data from the ML Service -- Step 5: Sending Data Back to the Weather Station Instrument -- 11.8 Testing the Workflow -- 11.9 Summary -- Chapter 12: Connecting an Edge Device to the IoT Application -- 12.1 Choosing the Hardware -- 12.2 The Role of the Edge Device -- 12.3 Building the Edge Device -- 12.4 Coding the Edge Device -- 12.5 Using the Edge Device -- 12.6 Improving the Edge Device -- 12.7 Peering Under the Hood of the IoT Application -- Parse JSON -- Initialize Variable -- Response -- When a HTTP Request Is Received -- 12.8 Data Analysis -- 12.9 Summary -- Chapter 13: Consolidating our Learnings -- 13.1 Am I a Data Scientist? | |
| 505 | 8 | |a 13.2 Becoming a Data Scientist -- 13.3 Debunking Some Myths -- 13.4 Extrapolating Learnings -- 13.5 Applying Our Knowledge to Different Builds -- 13.6 Ethical Considerations -- 13.7 Summary -- Index | |
| 650 | 4 | |a Internet of things | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Microprocessors | |
| 700 | 1 | |a Seneviratne, Pradeeka |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Meitiner, Philip |t Beginning Data Science, IoT, and AI on Single Board Computers |d Berkeley, CA : Apress L. P.,c2020 |z 9781484257654 |
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Datensatz im Suchindex
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| contents | Intro -- Table of Contents -- About the Authors -- Foreword -- Chapter 1: Introducing Data Science -- 1.1 Introducing Data Science -- 1.2 Using Temperature -- 1.3 Measuring Temperature -- 1.4 Controlling Data -- 1.5 Understanding the Tools -- 1.6 Data Quality -- 1.7 Data Capturing -- 1.8 Experimenting with Temperature -- 1.9 Analyzing Our Results -- Analysis of Extremities -- Analysis of Averages (Central Tendency) -- Random Insights -- Analysis of Data Quality -- Introspection -- 1.10 Summary -- Chapter 2: Data Science Goes Digital -- 2.1 Making It Digital -- 2.2 Measuring Temperature Digitally -- The Role of the Microprocessor -- 2.3 Building Digital Tools -- 2.4 Using the BBC micro:bit As a Thermometer -- 2.5 Coding Guidelines Used in This Book -- 2.6 Using the micro:bit Code Editors -- 2.7 Using the "No-Code" Option -- 2.8 Coding the micro:bit Thermometer -- 2.9 Comparing Analog and Digital Thermometers -- 2.10 Analysis -- Analysis of Extremities -- Analysis of Averages: "Central Tendency" -- Analysis of Data Quality -- Introspection -- 2.11 Why the micro:bit? -- 2.12 What Kit Do We Need? -- 2.13 Selecting Our Toolkit -- 2.14 Guide to Hardware Requirements -- 2.15 Summary -- Chapter 3: Experimenting with Weather -- 3.1 Introduction -- 3.2 Measuring Weather -- 3.3 Choosing the Data to Measure -- 3.4 Experimenting with Weather -- 3.5 Building Our Weather Station Tool -- 3.6 Coding Our Weather Station -- 3.7 Upgrading the Display -- 3.8 Experimental Design -- 3.9 Visualizing the Data We Collected -- 3.10 Analyzing the Data We Collected -- 3.11 Summary -- Chapter 4: Working with Large Data Sets -- 4.1 Experimental Design -- 4.2 Using the micro:bit As a File Storage Device -- 4.3 Accessing Files on the micro:bit -- 4.4 Transferring Files onto a Computer -- 4.5 Hardware Requirements -- 4.6 Storing Sensor Data in a File 4.7 Measuring How Many Data Points We Can Store -- 4.8 Replicating the Weather Station Experiment with File Storage -- 4.9 Addressing Memory Limitations -- 4.10 Expanding Data Storage Capacity -- 4.11 Summary -- Chapter 5: Introduction to Data Analysis -- 5.1 Expanding Our Analysis Tools -- 5.2 Software for Data Analysis -- 5.3 Selecting a Spreadsheet Program -- 5.4 Measuring Correlation -- 5.5 Calculating Correlation Scores -- 5.6 Understanding a Correlation Coefficient/Score -- 5.7 Calculating the Correlation Score for Weather Data -- 5.8 Using Other Analysis Functions -- 5.9 Using Visualization Tools -- 5.10 Reporting -- 5.11 Statistical Significance -- 5.12 Summary -- Chapter 6: Introducing IoT to Data Science -- 6.1 The Weakness in Our Data Science Toolkit -- 6.2 Internet of Things Overview -- 6.3 Anatomy of the Cloud -- 6.4 Transferring Data from a micro:bit -- 6.5 Wireless Communication Options for IoT -- 6.6 Transmitting Data Using a Serial Connection -- 6.7 Summary -- Chapter 7: Using Bluetooth for Data Science -- 7.1 What Is Bluetooth? -- 7.2 Why Use Bluetooth? -- 7.3 Using BLE on micro:bit -- 7.4 Building a BLE Weather Station with Bluetooth UART -- 7.5 Using the Serial Bluetooth Terminal App -- 7.6 Coding the BLE Weather Station -- 7.7 Other Options for BLE on micro:bit -- 7.8 Summary -- Chapter 8: Investigating the micro:bit Radio -- 8.1 Standards Are Important -- 8.2 Using Radio for Input/Output -- 8.3 Using Radio to Build a Network -- 8.4 Choosing MakeCode or MicroPython -- 8.5 MakeCode Radio Groups -- 8.6 Nodes and a Collector -- 8.7 Building the Nodes -- 8.8 Building the Server/Collector -- 8.9 Summary -- Chapter 9: Using Wi-Fi to Connect to the Internet -- 9.1 Defining Our IoT Weather Station -- 9.2 Building Our Wi-Fi Weather Station -- 9.3 Updating Firmware -- 9.4 Choosing an IoT Platform -- 9.5 Setting Up the IoT Platform 9.6 Adding Our Weather Station to the IoT Platform -- 9.7 Visualizing Data in the IoT Platform -- 9.8 Coding Our Wi-Fi Weather Station -- 9.9 Powering and Running the Weather Station -- 9.10 Viewing the Data Visualizations -- 9.11 Summary -- Chapter 10: Introduction to Machine Learning and Artificial Intelligence -- 10.1 Artificial Intelligence -- 10.2 AI/ML? -- 10.3 ML/AI and Data Science -- 10.4 Thinking Like a Machine -- 10.5 Experimental Design -- 10.6 Hardware Requirements -- 10.7 Software -- 10.8 Using the Hardware -- 10.9 Analyzing the Data -- 10.10 Comparing Humans and Machines -- 10.11 Summary -- Chapter 11: Using ML Services -- 11.1 Defining Our IoT Application -- 11.2 Choosing an IoT Service Provider -- 11.3 Setting Up Microsoft Azure: Cloud Computing Services -- 11.4 Creating an IoT Hub Using Azure Portal -- 11.5 Setting Up a Weather Prediction Model in Azure Machine Learning Studio -- 11.6 Creating a Workflow Using Azure Logic Apps -- 11.7 Setting Up the workflow -- Step 1: Sending Data from the Weather Station Instrument -- Step 2: Passing Data into the ML Service -- Step 3: Interpreting ("Parsing") Results from the ML Service ("Parse JSON") -- Step 4: Setting Up a Variable to Store the Data from the ML Service -- Step 5: Sending Data Back to the Weather Station Instrument -- 11.8 Testing the Workflow -- 11.9 Summary -- Chapter 12: Connecting an Edge Device to the IoT Application -- 12.1 Choosing the Hardware -- 12.2 The Role of the Edge Device -- 12.3 Building the Edge Device -- 12.4 Coding the Edge Device -- 12.5 Using the Edge Device -- 12.6 Improving the Edge Device -- 12.7 Peering Under the Hood of the IoT Application -- Parse JSON -- Initialize Variable -- Response -- When a HTTP Request Is Received -- 12.8 Data Analysis -- 12.9 Summary -- Chapter 13: Consolidating our Learnings -- 13.1 Am I a Data Scientist? 13.2 Becoming a Data Scientist -- 13.3 Debunking Some Myths -- 13.4 Extrapolating Learnings -- 13.5 Applying Our Knowledge to Different Builds -- 13.6 Ethical Considerations -- 13.7 Summary -- Index |
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| id | DE-604.BV048226079 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:44Z |
| indexdate | 2024-07-10T09:32:30Z |
| institution | BVB |
| isbn | 9781484257661 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033606809 |
| oclc_num | 1178714351 |
| open_access_boolean | |
| physical | 1 Online-Ressource (323 Seiten) |
| psigel | ZDB-30-PQE |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Apress L. P. |
| record_format | marc |
| spelling | Meitiner, Philip Verfasser aut Beginning Data Science, IoT, and AI on Single Board Computers Core Skills and Real-World Application with the BBC Micro:bit and XinaBox Berkeley, CA Apress L. P. 2020 ©2020 1 Online-Ressource (323 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Intro -- Table of Contents -- About the Authors -- Foreword -- Chapter 1: Introducing Data Science -- 1.1 Introducing Data Science -- 1.2 Using Temperature -- 1.3 Measuring Temperature -- 1.4 Controlling Data -- 1.5 Understanding the Tools -- 1.6 Data Quality -- 1.7 Data Capturing -- 1.8 Experimenting with Temperature -- 1.9 Analyzing Our Results -- Analysis of Extremities -- Analysis of Averages (Central Tendency) -- Random Insights -- Analysis of Data Quality -- Introspection -- 1.10 Summary -- Chapter 2: Data Science Goes Digital -- 2.1 Making It Digital -- 2.2 Measuring Temperature Digitally -- The Role of the Microprocessor -- 2.3 Building Digital Tools -- 2.4 Using the BBC micro:bit As a Thermometer -- 2.5 Coding Guidelines Used in This Book -- 2.6 Using the micro:bit Code Editors -- 2.7 Using the "No-Code" Option -- 2.8 Coding the micro:bit Thermometer -- 2.9 Comparing Analog and Digital Thermometers -- 2.10 Analysis -- Analysis of Extremities -- Analysis of Averages: "Central Tendency" -- Analysis of Data Quality -- Introspection -- 2.11 Why the micro:bit? -- 2.12 What Kit Do We Need? -- 2.13 Selecting Our Toolkit -- 2.14 Guide to Hardware Requirements -- 2.15 Summary -- Chapter 3: Experimenting with Weather -- 3.1 Introduction -- 3.2 Measuring Weather -- 3.3 Choosing the Data to Measure -- 3.4 Experimenting with Weather -- 3.5 Building Our Weather Station Tool -- 3.6 Coding Our Weather Station -- 3.7 Upgrading the Display -- 3.8 Experimental Design -- 3.9 Visualizing the Data We Collected -- 3.10 Analyzing the Data We Collected -- 3.11 Summary -- Chapter 4: Working with Large Data Sets -- 4.1 Experimental Design -- 4.2 Using the micro:bit As a File Storage Device -- 4.3 Accessing Files on the micro:bit -- 4.4 Transferring Files onto a Computer -- 4.5 Hardware Requirements -- 4.6 Storing Sensor Data in a File 4.7 Measuring How Many Data Points We Can Store -- 4.8 Replicating the Weather Station Experiment with File Storage -- 4.9 Addressing Memory Limitations -- 4.10 Expanding Data Storage Capacity -- 4.11 Summary -- Chapter 5: Introduction to Data Analysis -- 5.1 Expanding Our Analysis Tools -- 5.2 Software for Data Analysis -- 5.3 Selecting a Spreadsheet Program -- 5.4 Measuring Correlation -- 5.5 Calculating Correlation Scores -- 5.6 Understanding a Correlation Coefficient/Score -- 5.7 Calculating the Correlation Score for Weather Data -- 5.8 Using Other Analysis Functions -- 5.9 Using Visualization Tools -- 5.10 Reporting -- 5.11 Statistical Significance -- 5.12 Summary -- Chapter 6: Introducing IoT to Data Science -- 6.1 The Weakness in Our Data Science Toolkit -- 6.2 Internet of Things Overview -- 6.3 Anatomy of the Cloud -- 6.4 Transferring Data from a micro:bit -- 6.5 Wireless Communication Options for IoT -- 6.6 Transmitting Data Using a Serial Connection -- 6.7 Summary -- Chapter 7: Using Bluetooth for Data Science -- 7.1 What Is Bluetooth? -- 7.2 Why Use Bluetooth? -- 7.3 Using BLE on micro:bit -- 7.4 Building a BLE Weather Station with Bluetooth UART -- 7.5 Using the Serial Bluetooth Terminal App -- 7.6 Coding the BLE Weather Station -- 7.7 Other Options for BLE on micro:bit -- 7.8 Summary -- Chapter 8: Investigating the micro:bit Radio -- 8.1 Standards Are Important -- 8.2 Using Radio for Input/Output -- 8.3 Using Radio to Build a Network -- 8.4 Choosing MakeCode or MicroPython -- 8.5 MakeCode Radio Groups -- 8.6 Nodes and a Collector -- 8.7 Building the Nodes -- 8.8 Building the Server/Collector -- 8.9 Summary -- Chapter 9: Using Wi-Fi to Connect to the Internet -- 9.1 Defining Our IoT Weather Station -- 9.2 Building Our Wi-Fi Weather Station -- 9.3 Updating Firmware -- 9.4 Choosing an IoT Platform -- 9.5 Setting Up the IoT Platform 9.6 Adding Our Weather Station to the IoT Platform -- 9.7 Visualizing Data in the IoT Platform -- 9.8 Coding Our Wi-Fi Weather Station -- 9.9 Powering and Running the Weather Station -- 9.10 Viewing the Data Visualizations -- 9.11 Summary -- Chapter 10: Introduction to Machine Learning and Artificial Intelligence -- 10.1 Artificial Intelligence -- 10.2 AI/ML? -- 10.3 ML/AI and Data Science -- 10.4 Thinking Like a Machine -- 10.5 Experimental Design -- 10.6 Hardware Requirements -- 10.7 Software -- 10.8 Using the Hardware -- 10.9 Analyzing the Data -- 10.10 Comparing Humans and Machines -- 10.11 Summary -- Chapter 11: Using ML Services -- 11.1 Defining Our IoT Application -- 11.2 Choosing an IoT Service Provider -- 11.3 Setting Up Microsoft Azure: Cloud Computing Services -- 11.4 Creating an IoT Hub Using Azure Portal -- 11.5 Setting Up a Weather Prediction Model in Azure Machine Learning Studio -- 11.6 Creating a Workflow Using Azure Logic Apps -- 11.7 Setting Up the workflow -- Step 1: Sending Data from the Weather Station Instrument -- Step 2: Passing Data into the ML Service -- Step 3: Interpreting ("Parsing") Results from the ML Service ("Parse JSON") -- Step 4: Setting Up a Variable to Store the Data from the ML Service -- Step 5: Sending Data Back to the Weather Station Instrument -- 11.8 Testing the Workflow -- 11.9 Summary -- Chapter 12: Connecting an Edge Device to the IoT Application -- 12.1 Choosing the Hardware -- 12.2 The Role of the Edge Device -- 12.3 Building the Edge Device -- 12.4 Coding the Edge Device -- 12.5 Using the Edge Device -- 12.6 Improving the Edge Device -- 12.7 Peering Under the Hood of the IoT Application -- Parse JSON -- Initialize Variable -- Response -- When a HTTP Request Is Received -- 12.8 Data Analysis -- 12.9 Summary -- Chapter 13: Consolidating our Learnings -- 13.1 Am I a Data Scientist? 13.2 Becoming a Data Scientist -- 13.3 Debunking Some Myths -- 13.4 Extrapolating Learnings -- 13.5 Applying Our Knowledge to Different Builds -- 13.6 Ethical Considerations -- 13.7 Summary -- Index Internet of things Artificial intelligence Microprocessors Seneviratne, Pradeeka Sonstige oth Erscheint auch als Druck-Ausgabe Meitiner, Philip Beginning Data Science, IoT, and AI on Single Board Computers Berkeley, CA : Apress L. P.,c2020 9781484257654 |
| spellingShingle | Meitiner, Philip Beginning Data Science, IoT, and AI on Single Board Computers Core Skills and Real-World Application with the BBC Micro:bit and XinaBox Intro -- Table of Contents -- About the Authors -- Foreword -- Chapter 1: Introducing Data Science -- 1.1 Introducing Data Science -- 1.2 Using Temperature -- 1.3 Measuring Temperature -- 1.4 Controlling Data -- 1.5 Understanding the Tools -- 1.6 Data Quality -- 1.7 Data Capturing -- 1.8 Experimenting with Temperature -- 1.9 Analyzing Our Results -- Analysis of Extremities -- Analysis of Averages (Central Tendency) -- Random Insights -- Analysis of Data Quality -- Introspection -- 1.10 Summary -- Chapter 2: Data Science Goes Digital -- 2.1 Making It Digital -- 2.2 Measuring Temperature Digitally -- The Role of the Microprocessor -- 2.3 Building Digital Tools -- 2.4 Using the BBC micro:bit As a Thermometer -- 2.5 Coding Guidelines Used in This Book -- 2.6 Using the micro:bit Code Editors -- 2.7 Using the "No-Code" Option -- 2.8 Coding the micro:bit Thermometer -- 2.9 Comparing Analog and Digital Thermometers -- 2.10 Analysis -- Analysis of Extremities -- Analysis of Averages: "Central Tendency" -- Analysis of Data Quality -- Introspection -- 2.11 Why the micro:bit? -- 2.12 What Kit Do We Need? -- 2.13 Selecting Our Toolkit -- 2.14 Guide to Hardware Requirements -- 2.15 Summary -- Chapter 3: Experimenting with Weather -- 3.1 Introduction -- 3.2 Measuring Weather -- 3.3 Choosing the Data to Measure -- 3.4 Experimenting with Weather -- 3.5 Building Our Weather Station Tool -- 3.6 Coding Our Weather Station -- 3.7 Upgrading the Display -- 3.8 Experimental Design -- 3.9 Visualizing the Data We Collected -- 3.10 Analyzing the Data We Collected -- 3.11 Summary -- Chapter 4: Working with Large Data Sets -- 4.1 Experimental Design -- 4.2 Using the micro:bit As a File Storage Device -- 4.3 Accessing Files on the micro:bit -- 4.4 Transferring Files onto a Computer -- 4.5 Hardware Requirements -- 4.6 Storing Sensor Data in a File 4.7 Measuring How Many Data Points We Can Store -- 4.8 Replicating the Weather Station Experiment with File Storage -- 4.9 Addressing Memory Limitations -- 4.10 Expanding Data Storage Capacity -- 4.11 Summary -- Chapter 5: Introduction to Data Analysis -- 5.1 Expanding Our Analysis Tools -- 5.2 Software for Data Analysis -- 5.3 Selecting a Spreadsheet Program -- 5.4 Measuring Correlation -- 5.5 Calculating Correlation Scores -- 5.6 Understanding a Correlation Coefficient/Score -- 5.7 Calculating the Correlation Score for Weather Data -- 5.8 Using Other Analysis Functions -- 5.9 Using Visualization Tools -- 5.10 Reporting -- 5.11 Statistical Significance -- 5.12 Summary -- Chapter 6: Introducing IoT to Data Science -- 6.1 The Weakness in Our Data Science Toolkit -- 6.2 Internet of Things Overview -- 6.3 Anatomy of the Cloud -- 6.4 Transferring Data from a micro:bit -- 6.5 Wireless Communication Options for IoT -- 6.6 Transmitting Data Using a Serial Connection -- 6.7 Summary -- Chapter 7: Using Bluetooth for Data Science -- 7.1 What Is Bluetooth? -- 7.2 Why Use Bluetooth? -- 7.3 Using BLE on micro:bit -- 7.4 Building a BLE Weather Station with Bluetooth UART -- 7.5 Using the Serial Bluetooth Terminal App -- 7.6 Coding the BLE Weather Station -- 7.7 Other Options for BLE on micro:bit -- 7.8 Summary -- Chapter 8: Investigating the micro:bit Radio -- 8.1 Standards Are Important -- 8.2 Using Radio for Input/Output -- 8.3 Using Radio to Build a Network -- 8.4 Choosing MakeCode or MicroPython -- 8.5 MakeCode Radio Groups -- 8.6 Nodes and a Collector -- 8.7 Building the Nodes -- 8.8 Building the Server/Collector -- 8.9 Summary -- Chapter 9: Using Wi-Fi to Connect to the Internet -- 9.1 Defining Our IoT Weather Station -- 9.2 Building Our Wi-Fi Weather Station -- 9.3 Updating Firmware -- 9.4 Choosing an IoT Platform -- 9.5 Setting Up the IoT Platform 9.6 Adding Our Weather Station to the IoT Platform -- 9.7 Visualizing Data in the IoT Platform -- 9.8 Coding Our Wi-Fi Weather Station -- 9.9 Powering and Running the Weather Station -- 9.10 Viewing the Data Visualizations -- 9.11 Summary -- Chapter 10: Introduction to Machine Learning and Artificial Intelligence -- 10.1 Artificial Intelligence -- 10.2 AI/ML? -- 10.3 ML/AI and Data Science -- 10.4 Thinking Like a Machine -- 10.5 Experimental Design -- 10.6 Hardware Requirements -- 10.7 Software -- 10.8 Using the Hardware -- 10.9 Analyzing the Data -- 10.10 Comparing Humans and Machines -- 10.11 Summary -- Chapter 11: Using ML Services -- 11.1 Defining Our IoT Application -- 11.2 Choosing an IoT Service Provider -- 11.3 Setting Up Microsoft Azure: Cloud Computing Services -- 11.4 Creating an IoT Hub Using Azure Portal -- 11.5 Setting Up a Weather Prediction Model in Azure Machine Learning Studio -- 11.6 Creating a Workflow Using Azure Logic Apps -- 11.7 Setting Up the workflow -- Step 1: Sending Data from the Weather Station Instrument -- Step 2: Passing Data into the ML Service -- Step 3: Interpreting ("Parsing") Results from the ML Service ("Parse JSON") -- Step 4: Setting Up a Variable to Store the Data from the ML Service -- Step 5: Sending Data Back to the Weather Station Instrument -- 11.8 Testing the Workflow -- 11.9 Summary -- Chapter 12: Connecting an Edge Device to the IoT Application -- 12.1 Choosing the Hardware -- 12.2 The Role of the Edge Device -- 12.3 Building the Edge Device -- 12.4 Coding the Edge Device -- 12.5 Using the Edge Device -- 12.6 Improving the Edge Device -- 12.7 Peering Under the Hood of the IoT Application -- Parse JSON -- Initialize Variable -- Response -- When a HTTP Request Is Received -- 12.8 Data Analysis -- 12.9 Summary -- Chapter 13: Consolidating our Learnings -- 13.1 Am I a Data Scientist? 13.2 Becoming a Data Scientist -- 13.3 Debunking Some Myths -- 13.4 Extrapolating Learnings -- 13.5 Applying Our Knowledge to Different Builds -- 13.6 Ethical Considerations -- 13.7 Summary -- Index Internet of things Artificial intelligence Microprocessors |
| title | Beginning Data Science, IoT, and AI on Single Board Computers Core Skills and Real-World Application with the BBC Micro:bit and XinaBox |
| title_auth | Beginning Data Science, IoT, and AI on Single Board Computers Core Skills and Real-World Application with the BBC Micro:bit and XinaBox |
| title_exact_search | Beginning Data Science, IoT, and AI on Single Board Computers Core Skills and Real-World Application with the BBC Micro:bit and XinaBox |
| title_exact_search_txtP | Beginning Data Science, IoT, and AI on Single Board Computers Core Skills and Real-World Application with the BBC Micro:bit and XinaBox |
| title_full | Beginning Data Science, IoT, and AI on Single Board Computers Core Skills and Real-World Application with the BBC Micro:bit and XinaBox |
| title_fullStr | Beginning Data Science, IoT, and AI on Single Board Computers Core Skills and Real-World Application with the BBC Micro:bit and XinaBox |
| title_full_unstemmed | Beginning Data Science, IoT, and AI on Single Board Computers Core Skills and Real-World Application with the BBC Micro:bit and XinaBox |
| title_short | Beginning Data Science, IoT, and AI on Single Board Computers |
| title_sort | beginning data science iot and ai on single board computers core skills and real world application with the bbc micro bit and xinabox |
| title_sub | Core Skills and Real-World Application with the BBC Micro:bit and XinaBox |
| topic | Internet of things Artificial intelligence Microprocessors |
| topic_facet | Internet of things Artificial intelligence Microprocessors |
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