Data Science and Data Analytics

Opportunities and Challenges

Amit Kumar Tyagi

• Publisher: Chapman and Hall/CRC
• Year: 2021
• eBook ISBN: 9781000423228
• Topic: Computer Science
• Subtopic: Statistics for Business & Economics
• Index: Computer Science

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues.

Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy.

FEATURES

• Gives the concept of data science, tools, and algorithms that exist for many useful applications
• Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems
• Identifies many areas and uses of data science in the smart era
• Applies data science to agriculture, healthcare, graph mining, education, security, etc.

Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm's productivity.

Section II

Algorithms, Methods, and Tools for Data Science and Data Analytics

5

Crop Models and Decision Support Systems Using Machine Learning

B. Vignesh and G. Suganya

CONTENTS

5.1 Introduction

5.1.1 Decision Support System

5.1.2 Decision Support System for Crop Yield

5.1.3 What Is Crop Modeling?

5.1.4 Necessity of Crop Modeling

5.1.5 Recent Trends in Crop Modeling

5.2 Methodologies

5.2.1 Machine-Learning-Based Techniques

5.2.2 Deep-Learning-Based Techniques

5.2.3 Hyper-Spectral Imaging

5.2.4 Popular Band Selection Techniques

5.2.5 Leveraging Conventional Neural Network

5.3 Role of Hyper-Spectral Data

5.3.1 Farm Based

5.3.2 Crop Based

5.3.3 Advanced HSI Processing

5.4 Potential Challenges and Strategies to Overcome the Challenges

5.5 Current and Future Scope

5.6 Conclusion

References

5.1 Introduction

The human population throughout the world is estimated to reach around 9 billion by the year 2050 as per the prediction of the Food and Agriculture Organization (United Nations). Therefore, the demand for food and agriculture-based commodities will increase. The estimated food demand gradually increases at a rate of 2% per year, approximately. By the year 2050, the food demand will increase by 70% of the present-day food demand. Therefore the agricultural yield should be multiplied manifold to meet the increasing food demand (i.e., the food production should increase to 13.5 billion tonnes a year from the current food production, which is 8.4 billion tonnes a year) [1].

Such a huge target cannot be achieved using traditional farming methods because of the present-day challenges and environmental problems like climate change, biodiversity loss, erosion, pesticide resistance, fertilizers and eutrophication, water depletion, soil salinization, urban sprawl, pollution, and silt. Hence, traditional farming methods are unable to keep pace with the increasing food production demand [2]. Under such conditions, the increased food security threat will lead to global instability.

A sustainable and long-term solution to this problem is “smart farming,” especially in countries like India that has a growing population. Smart farming is a more productive and sustainable way of farming. Smart farm...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• Preface
• Editors
• Contributors
• Section I: Introduction about Data Science and Data Analytics
• Section II: Algorithms, Methods, and Tools for Data Science and Data Analytics
• Section III: Applications of Data Science and Data Analytics
• Section IV: Issue and Challenges in Data Science and Data Analytics
• Section V: Future Research Opportunities towards Data Science and Data Analytics
• Index