Data science with Raspberry Pi real-time applications using a localized cloud
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
New York, New York :
Apress Media LLC
[2021]
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631584406719 |
Tabla de Contenidos:
- Intro
- Table of Contents
- About the Authors
- About the Technical Reviewer
- Acknowledgments
- Introduction
- Chapter 1: Introduction to Data Science
- Importance of Data Types in Data Science
- Data Science: An Overview
- Data Requirements
- Data Acquisition
- Data Preparation
- Data Processing
- Data Cleaning
- Duplicates
- Human or Machine Errors
- Missing Values
- Outliers
- Transforming the Data
- Data Visualization
- Data Analysis
- Modeling and Algorithms
- Report Generation/Decision-Making
- Recent Trends in Data Science
- Automation in Data Science
- Artificial Intelligence-Based Data Analyst
- Cloud Computing
- Edge Computing
- Natural Language Processing
- Why Data Science on the Raspberry Pi?
- Chapter 2: Basics of Python Programming
- Why Python?
- Python Installation
- Python IDEs
- PyCharm
- Spyder
- Jupyter Notebook
- Python Programming with IDLE
- Python Comments
- Python Data Types
- Numeric Data Types
- int
- float
- complex
- bool
- Numeric Operators
- Sequence Data Types
- list
- tuple
- str
- set
- dict
- Type Conversion
- Control Flow Statements
- if Statement
- if-else Statement
- if...elif...else statement
- while loop
- for loop
- Exception Handling
- Functions
- Python Libraries for Data Science
- NumPy and SciPy for Scientific Computation
- Scikit-Learn for Machine Learning
- Pandas for Data Analysis
- TensorFlow for Machine Learning
- Chapter 3: Introduction to the Raspberry Pi
- What Can You Do with the Raspberry Pi?
- Physical Computing with the Raspberry Pi
- How to Program the Raspberry Pi?
- Raspberry Pi Hardware
- System on a Chip
- Raspberry Pi RAM
- Connectivity
- Setting Up the Raspberry Pi
- microSD Memory Card
- Installing the OS
- Inserting the microSD Memory Card
- Connecting a Keyboard and Mouse
- Connecting a Monitor.
- Powering the Raspberry Pi
- Raspberry Pi Enclosure
- Raspberry Pi Versions
- Raspberry Pi 1
- Raspberry Pi 2
- Raspberry Pi 3
- Raspberry Pi Zero (W/WH)
- Raspberry Pi 4
- Recommended Raspberry Pi Version
- Interfacing the Raspberry Pi with Sensors
- GPIO Pins
- GPIO Pinout
- GPIO Outputs
- Controlling GPIO Output with Python
- GPIO Input Signals
- Reading GPIO Inputs with Python
- Digital Signals from Sensors
- Analog Signals from Sensors
- Interfacing a Ultrasonic Sensor with the Raspberry Pi
- Interfacing the Temperature and Humidity Sensor with the Raspberry Pi
- Interfacing the Soil Moisture Sensor with the Raspberry Pi
- Interfacing Cameras with the Raspberry Pi
- Raspberry Pi as an Edge Device
- Edge Computing in Self-Driving Cars
- What Is an Edge Device?
- Edge Computing with the Raspberry Pi
- Raspberry Pi as a Localized Cloud
- Cloud Computing
- Raspberry Pi as Localized Cloud
- Connecting an External Hard Drive
- Connecting USB Accelerator
- Chapter 4: Sensors and Signals
- Signals
- Analog and Digital Signals
- Continuous-Time and Discrete-Time Signals
- Deterministic and Nondeterministic Signals
- One-Dimensional, Two-Dimensional, and Multidimensional Signals
- Gathering Real-Time Data
- Data Acquisition
- Sensors
- Analog Sensors
- Digital Sensors
- What Is Real-Time Data?
- Real-Time Data Analytics
- Getting Real-Time Distance Data from an Ultrasonic Sensor
- Interfacing an Ultrasonic Sensor with the Raspberry Pi
- Getting Real-Time Image Data from a Camera
- Getting Real-Time Video from a Webcam
- Getting Real-Time Video from Pi-cam
- Data Transfer
- Serial and Parallel Communication
- Interfacing an Arduino with the Raspberry Pi
- Serial via USB
- Serial via GPIOs
- Data Transmission Between an Arduino and the Raspberry Pi
- Arduino Code
- Raspberry Pi Python Code.
- Time-Series Data
- Time-Series Analysis and Forecasting
- Memory Requirements
- More Storage
- More RAM
- Case Study: Gathering the Real-Time Industry Data
- Storing Collected Data Using Pandas
- Dataframes
- Saving Data as a CSV File
- Saving as an Excel File
- Reading Saved Data Files
- Adding the Date and Time to the Real-Time Data
- Industry Data from the Temperature and Humidity Sensor
- Chapter 5: Preparing the Data
- Pandas and Data Structures
- Installing and Using Pandas
- Pandas Data Structures
- Series
- DataFrame
- Reading Data
- Reading CSV Data
- Reading Excel Data
- Reading URL Data
- Cleaning the Data
- Handling Missing Values
- Handling Outliers
- Z-Score
- Filtering Out Inappropriate Values
- Removing Duplicates
- Chapter 6: Visualizing the Data
- Matplotlib Library
- Scatter Plot
- Line Plot
- Histogram
- Bar Chart
- Pie Chart
- Other Plots and Packages
- Chapter 7: Analyzing the Data
- Exploratory Data Analysis
- Choosing a Dataset
- Modifying the Columns in the Dataset
- Statistical Analysis
- Uniform Distribution
- Binomial Distribution
- Normal Distribution
- Statistical Analysis of Boston Housing Price Dataset
- Chapter 8: Learning from Data
- Forecasting from Data Using Regression
- Linear Regression using Scikit-Learn
- Principal Component Analysis
- Outlier Detection Using K-Means Clustering
- Chapter 9: Case Studies
- Case Study 1: Human Emotion Classification
- Methodology
- Dataset
- Interfacing the Raspberry Pi with MindWave Mobile via Bluetooth
- Data Collection Process
- Features Taken from the Brain Wave Signal
- Unstructured Data to Structured Dataset
- Exploratory Data Analysis from the EEG Data
- Classifying the Emotion Using Learning Models
- Case Study 2: Data Science for Image Data
- Exploratory Image Data Analysis.
- Preparing the Image Data for Model
- Object Detection Using a Deep Neural Network
- Case Study 3: Industry 4.0
- Raspberry Pi as a Localized Cloud for Industry 4.0
- Collecting Data from Sensors
- Preparing the Industry Data in the Raspberry Pi
- Exploratory Data Analysis for the Real-Time Sensor Data
- Visualizing the Real-Time Sensor Data
- Report Generation by Reading Bar Codes Using Vision Cameras
- Transmitting Files or Data from the Raspberry Pi to the Computer
- References
- Index.
