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Data Science and Big Data 2.0: : With Live Robotics Projects using Python 3.7
How to Use This Book
As a data scientist, you have probably already experienced that “data science” has become a very overloaded term indeed. This book explains the Data Science Process with best examples. And also explains several Data Science transform Utilities and functions using Python 3.7. A major goal of this book is providing big data engineers with the intellectual tools to think like big data scientists. This book also explains Python Programming with the best examples and build an advanced robot that has the ability to seek out, recognize, and follow a coloured ball.
Table of Contents
1. Data Science and Big Data 2.0
2. Data Science Tools and Technologies
3. Data Science and Machine Learning Glossary
4. What Is New in Big Data 2.0?
5. Trending Big Data Applications
6. Industrial Revolution using Big data and Cloud Computing
7. Artificial Intelligence and eLearning
8. Artificial Intelligence, Data Science and Social Equity
9. Data Science and Data-to-Learning-to-Action Chain
10. What Is Feature Engineering?
11. Big Data and Hadoop
12. The Importance of Ethics in Data Science
13. Connecting Big Data to Big Cities
14. Python 3.7 Tutorial with Examples
15. Create Game using PyGame and Raspberry Pi
16. Python OS Automation
17. Build Computer Vision based Robot using Raspberry Pi
18. Wi-Fi and Arduino
19. Working with Robotics Motors
20. Version Control and GIT
21. Working with Data Management Platforms (DMPs)
22. Working with Memory and Threads
23. Particle Swarm Optimization Algorithm
Data Science 2.0 and Big Data
The interdisciplinary field undertaking data analytics work on all kinds of data, with a focus on big data, for the purpose of mining insights and/or building data products. Data science as lying at the intersection of computer science, statistics, and substantive application domains. From computer science comes to machine learning and high-performance computing technologies for dealing with scale.
Big Data Technology
Big data is a popular term that describes the exponential growth, availability, and use of information, both structured and unstructured. Big data continues to gain attention from the high-performance computing niche of the information technology market.
Big data provides both challenges and opportunities for data miners to develop improved models. Today’s massively parallel in-memory analytical computing appliances no longer hinder the size of data you can analyze.
Big Data technology aims to minimize the need for hardware and reduce processing costs. Conventional data technologies, such as databases and data warehouses, are becoming inadequate for the amount of data to analyze.
What Is Feature Engineering?
Feature engineering is your core technique to determine the important data characteristics in the data lake and ensure they get the correct treatment through the steps of processing. Make sure that any featuring extraction process technique is documented in the data transformation matrix and the data lineage.
This book explains What Is Feature Engineering and how it works. And also explains Common Feature Extraction Techniques used in Feature Engineering.
Build Computer Vision based Robot using Raspberry Pi
Computer vision is an advanced field of computer science and engineering that aims to enable computers and machines to see and understand their surroundings at least as well as humans, if not better.
As a data scientist, you have probably already experienced that “data science” has become a very overloaded term indeed. This book explains the Data Science Process with best examples. And also explains several Data Science transform Utilities and functions using Python 3.7. A major goal of this book is providing big data engineers with the intellectual tools to think like big data scientists. This book also explains Python Programming with the best examples and build an advanced robot that has the ability to seek out, recognize, and follow a coloured ball.
Table of Contents
1. Data Science and Big Data 2.0
2. Data Science Tools and Technologies
3. Data Science and Machine Learning Glossary
4. What Is New in Big Data 2.0?
5. Trending Big Data Applications
6. Industrial Revolution using Big data and Cloud Computing
7. Artificial Intelligence and eLearning
8. Artificial Intelligence, Data Science and Social Equity
9. Data Science and Data-to-Learning-to-Action Chain
10. What Is Feature Engineering?
11. Big Data and Hadoop
12. The Importance of Ethics in Data Science
13. Connecting Big Data to Big Cities
14. Python 3.7 Tutorial with Examples
15. Create Game using PyGame and Raspberry Pi
16. Python OS Automation
17. Build Computer Vision based Robot using Raspberry Pi
18. Wi-Fi and Arduino
19. Working with Robotics Motors
20. Version Control and GIT
21. Working with Data Management Platforms (DMPs)
22. Working with Memory and Threads
23. Particle Swarm Optimization Algorithm
Data Science 2.0 and Big Data
The interdisciplinary field undertaking data analytics work on all kinds of data, with a focus on big data, for the purpose of mining insights and/or building data products. Data science as lying at the intersection of computer science, statistics, and substantive application domains. From computer science comes to machine learning and high-performance computing technologies for dealing with scale.
Big Data Technology
Big data is a popular term that describes the exponential growth, availability, and use of information, both structured and unstructured. Big data continues to gain attention from the high-performance computing niche of the information technology market.
Big data provides both challenges and opportunities for data miners to develop improved models. Today’s massively parallel in-memory analytical computing appliances no longer hinder the size of data you can analyze.
Big Data technology aims to minimize the need for hardware and reduce processing costs. Conventional data technologies, such as databases and data warehouses, are becoming inadequate for the amount of data to analyze.
What Is Feature Engineering?
Feature engineering is your core technique to determine the important data characteristics in the data lake and ensure they get the correct treatment through the steps of processing. Make sure that any featuring extraction process technique is documented in the data transformation matrix and the data lineage.
This book explains What Is Feature Engineering and how it works. And also explains Common Feature Extraction Techniques used in Feature Engineering.
Build Computer Vision based Robot using Raspberry Pi
Computer vision is an advanced field of computer science and engineering that aims to enable computers and machines to see and understand their surroundings at least as well as humans, if not better.
1158 pages, Kindle Edition
Published June 27, 2019
2 people are currently reading
15 people want to read
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