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Data Science 2.0: Live Raspberry Pi 3 and Arduino Projects using Python 3.7
How to Use This Book?
Data science is about extracting value from data in a grounded manner where one realizes that data requires a lot of treatment and work from a lot of stakeholders before becoming valuable.
The first section of this book explains Data Science Questions and Hypotheses and Data Engineering 2.0. The second section of this book explains how Artificial Intelligence And Data Science relates to each other. The third section of this book explains the complete Python 3.7 A to Z. The fourth section of this book explains how to implement Web Scraping using Python 3.7. The fifth section of this book explains how to Create a Battleship Game using Python 3.7. The last section of this book explains several live Arduino and Raspberry Pi’s Projects.
Table of Contents
1. Data Science and Data Engineering 2.0
2. Data Science Questions and Hypotheses
3. Artificial Intelligence And Data Science
4. Introducing Object-Oriented Programming: Python 3.7
5. Inheritance, Composition, and Aggregation in Python 3.7
6. Python 3.7 A to Z
7. Python Career in 2020
8. Python 3.7: Some Basics Concepts
9. Text Manipulation in Python 3.7
10. Best Ways to used Constructors in Python
11. Web Scraping using Python 3.7
12. Create a Battleship Game using Python 3.7
13. NumPy Library: The Python packages
14. Working with Arduino Sensors
15. Arduino Analog Input and Outputs
16. How to connect GPS in Arduino?
17. Working with Raspberry Pi’s GPIO Interface
18. Raspberry Pi Socket Programming
19. Generative Adversarial Network (GAN) for Games
20. Sphinx: A Documentation Tool for Python
21. Building Recurrent Neural Networks
22. Appendix: Generator Functions
Data Engineering
Data engineering involves getting your data ready for data analytics work. However, this is not an easy task because data comes in many varieties and degrees of data quality and documentation. In fact, this is the most time-consuming stage of the process, and it’s not uncommon for a data scientist to spend 70-80% of their time in this stage.
The main challenge is that most of the data streams involved in a data science project are unstructured or semi-structured data. However, most data analytics models work with structured data (aka datasets), so the raw data streams are practically useless for them.
Programming Languages for Data Science
Data science is inherently different from conventional data analytics approaches, and one of the key differentiating factors is programming. This is the process of creating a custom program that runs on a computer or a cloud server and processes data to provide an output related to this data, using a specialized coding language.
In data science, we have a set of programming languages that, because of their ease of use and variety of packages (programming libraries), lend themselves to the various methodologies we examined in the previous blog. The most important of these languages are Julia, Python, R, and Scala. In this book, we explain Python 3.7 in depth.
Create a Battleship Game using Python 3.7
In this chapter, you will learn how to implement a basic game in a quantum computer. The game is given a major facelift by showing you how to implement a cloud-based battleship game using python 3.7.
Quantum Battleship with the following features:
•\ A browser-based user interface with interactive boards to place ship or bombs. The game mechanics remain the same nonetheless.
Data science is about extracting value from data in a grounded manner where one realizes that data requires a lot of treatment and work from a lot of stakeholders before becoming valuable.
The first section of this book explains Data Science Questions and Hypotheses and Data Engineering 2.0. The second section of this book explains how Artificial Intelligence And Data Science relates to each other. The third section of this book explains the complete Python 3.7 A to Z. The fourth section of this book explains how to implement Web Scraping using Python 3.7. The fifth section of this book explains how to Create a Battleship Game using Python 3.7. The last section of this book explains several live Arduino and Raspberry Pi’s Projects.
Table of Contents
1. Data Science and Data Engineering 2.0
2. Data Science Questions and Hypotheses
3. Artificial Intelligence And Data Science
4. Introducing Object-Oriented Programming: Python 3.7
5. Inheritance, Composition, and Aggregation in Python 3.7
6. Python 3.7 A to Z
7. Python Career in 2020
8. Python 3.7: Some Basics Concepts
9. Text Manipulation in Python 3.7
10. Best Ways to used Constructors in Python
11. Web Scraping using Python 3.7
12. Create a Battleship Game using Python 3.7
13. NumPy Library: The Python packages
14. Working with Arduino Sensors
15. Arduino Analog Input and Outputs
16. How to connect GPS in Arduino?
17. Working with Raspberry Pi’s GPIO Interface
18. Raspberry Pi Socket Programming
19. Generative Adversarial Network (GAN) for Games
20. Sphinx: A Documentation Tool for Python
21. Building Recurrent Neural Networks
22. Appendix: Generator Functions
Data Engineering
Data engineering involves getting your data ready for data analytics work. However, this is not an easy task because data comes in many varieties and degrees of data quality and documentation. In fact, this is the most time-consuming stage of the process, and it’s not uncommon for a data scientist to spend 70-80% of their time in this stage.
The main challenge is that most of the data streams involved in a data science project are unstructured or semi-structured data. However, most data analytics models work with structured data (aka datasets), so the raw data streams are practically useless for them.
Programming Languages for Data Science
Data science is inherently different from conventional data analytics approaches, and one of the key differentiating factors is programming. This is the process of creating a custom program that runs on a computer or a cloud server and processes data to provide an output related to this data, using a specialized coding language.
In data science, we have a set of programming languages that, because of their ease of use and variety of packages (programming libraries), lend themselves to the various methodologies we examined in the previous blog. The most important of these languages are Julia, Python, R, and Scala. In this book, we explain Python 3.7 in depth.
Create a Battleship Game using Python 3.7
In this chapter, you will learn how to implement a basic game in a quantum computer. The game is given a major facelift by showing you how to implement a cloud-based battleship game using python 3.7.
Quantum Battleship with the following features:
•\ A browser-based user interface with interactive boards to place ship or bombs. The game mechanics remain the same nonetheless.
887 pages, Kindle Edition
Published June 12, 2019
9 people are currently reading
14 people want to read
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