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Data Science for Beginners and Layman:
Who this book is written for
We presume that the data scientists reading this book are knowledgeable about data science, common machine learning methods, and popular data science tools, and have in the course of their work run proof of concept studies, and built prototypes. We offer a book that introduces advanced techniques and methods for building data science solutions to this audience, showing them how to construct commercial grade data products for layman and beginners.
** The Big Data Science Ecosystem **
As a data scientist, you'll no doubt be very familiar with handling files and processing perhaps even large amounts of data. However, as I'm sure you will agree, doing anything more than a simple analysis over a single type of data requires a method of organizing and cataloging data so that it can be managed effectively.
Perspectives on Data Science from a Developer
In this introductory chapter, I'll start the conversation by attempting to answer a few fundamental questions that will hopefully provide context and clarity for the rest of this book:
* What is data science and why it's on the rise
* Why is data science here to stay
* Why do developers need to get involved in data science
Inside the Book
1. Perspectives on Data Science from a Developer
2. Data Science Applications Formats and Terms
3. Machine learning applied to Data Science
4. Machine Learning Algorithms
5. Agility, Machine Learning, and Analytics
6. Using Neural Networks in Data Science
7. Deep Learning Opportunity
8. New Research and Future Directions
9. Why Data Scientists use Machine Learning
10. Unsupervised Learning - Clustering and Dimensionality Reduction
11. Natural Language Processing and Information Retrieval
12. Industrial Applications of Machine Learning and Data Science
Why is Data Science on the rise?
There are multiple factors involved in the meteoric rise of data science.
First, the amount of data being collected keeps growing at an exponential rate. According to recent market research from the IBM Marketing Cloud something like 2.5 quintillion bytes are created every day (to give you an idea of how big that is, that's 2.5 billion of billion bytes), but yet only a tiny fraction of this data is ever analyzed, leaving tons of missed opportunities on the table.
Second, we're in the midst of a cognitive revolution that started a few years ago; almost every industry is jumping on the AI bandwagon, which includes natural language processing (NLP) and machine learning.
**Even though these fields existed for a long time,** they have recently enjoyed the renewed attention to the point that they are now among the most popular courses in colleges as well as getting the lion's share of open source activities.
And In the last chapter discussed machine learning analytics in detail. It broadly discussed the uses, applications, challenges, and use cases of machine learning analytics in areas like manufacturing, retail, marketing, sales, banking, and finance.
We presume that the data scientists reading this book are knowledgeable about data science, common machine learning methods, and popular data science tools, and have in the course of their work run proof of concept studies, and built prototypes. We offer a book that introduces advanced techniques and methods for building data science solutions to this audience, showing them how to construct commercial grade data products for layman and beginners.
** The Big Data Science Ecosystem **
As a data scientist, you'll no doubt be very familiar with handling files and processing perhaps even large amounts of data. However, as I'm sure you will agree, doing anything more than a simple analysis over a single type of data requires a method of organizing and cataloging data so that it can be managed effectively.
Perspectives on Data Science from a Developer
In this introductory chapter, I'll start the conversation by attempting to answer a few fundamental questions that will hopefully provide context and clarity for the rest of this book:
* What is data science and why it's on the rise
* Why is data science here to stay
* Why do developers need to get involved in data science
Inside the Book
1. Perspectives on Data Science from a Developer
2. Data Science Applications Formats and Terms
3. Machine learning applied to Data Science
4. Machine Learning Algorithms
5. Agility, Machine Learning, and Analytics
6. Using Neural Networks in Data Science
7. Deep Learning Opportunity
8. New Research and Future Directions
9. Why Data Scientists use Machine Learning
10. Unsupervised Learning - Clustering and Dimensionality Reduction
11. Natural Language Processing and Information Retrieval
12. Industrial Applications of Machine Learning and Data Science
Why is Data Science on the rise?
There are multiple factors involved in the meteoric rise of data science.
First, the amount of data being collected keeps growing at an exponential rate. According to recent market research from the IBM Marketing Cloud something like 2.5 quintillion bytes are created every day (to give you an idea of how big that is, that's 2.5 billion of billion bytes), but yet only a tiny fraction of this data is ever analyzed, leaving tons of missed opportunities on the table.
Second, we're in the midst of a cognitive revolution that started a few years ago; almost every industry is jumping on the AI bandwagon, which includes natural language processing (NLP) and machine learning.
**Even though these fields existed for a long time,** they have recently enjoyed the renewed attention to the point that they are now among the most popular courses in colleges as well as getting the lion's share of open source activities.
And In the last chapter discussed machine learning analytics in detail. It broadly discussed the uses, applications, challenges, and use cases of machine learning analytics in areas like manufacturing, retail, marketing, sales, banking, and finance.
393 pages, Kindle Edition
Published December 21, 2018
11 people want to read
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