Data Science and Data Analytics

Opportunities and Challenges
1. Auflage 2021
ISBN: 978-0-367-62882-6
Verlag: Taylor & Francis Ltd

Buch, Englisch, 482 Seiten, Format (B × H): 261 mm x 182 mm, Gewicht: 1066 g

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.

Zielgruppe

Academic, Postgraduate, Professional, and Undergraduate Advanced

Autoren/Hrsg.

Tyagi, Amit Kumar

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

Section 1. Introduction about Data Science and Data Analytics
Chapter 1. Data Science and Data Analytics: Artificial Intelligence and Machine Learning Integrated based approachSumika Chauhan, Manmohan Singh, Ashwani Kumar Aggarwal
Chapter 2 IoT Analytics/ Data Science for IoTT. Perarasi, R. Gayathri, M. Leeban Moses, B. Vinoth
Chapter 3. A model to identify agriculture production using Data Science techniquesD. Anantha Reddy, Sanjay Kumar, Rakesh Tripathi
Chapter 4. Identification and Classification of Paddy Crop Diseases using Big Data Machine Learning TechniquesAnisha P Rodrigues, Joyston Menezes, Roshan Fernandes,Aishwarya, Niranjan N Chiplunkar, Vijaya Padmanabha
Section 2. Algorithms, Methods, Tools for Data Science and Data Analytics
Chapter 5. Crop Models and Decision Support Systems using Machine LearningB.Vignesh, G.Suganya
Chapter 6. An Ameliorated Methodology to Predict Diabetes Mellitus using Random ForestArunakumari B. N, Aman Rai, Shashidhar R
Chapter 7. High Dimensionality Dataset Reduction Methodologies in Applied Machine LearningFarhan Hai Khan, Tannistha Pal
Chapter 8. Hybrid Cellular Automata Models For Discrete Dynamical SystemsSreeya Ghosh, Sumita Basu
Chapter 9. An Efficient Imputation Strategy Based On Adaptive Filter For Large Missing Value Data SetsS Radhika, A Chandrasekar, Felix Albu
Chapter 10. An Analysis of Derivative based Optimizers on Deep Neural Network ModelsAruna Pavate, Rajesh Bansode
Section 3. Applications of Data Science and Data Analytics
Chapter 11. Wheat Rust Disease Detection using Deep LearningSudhir Kumar Mohapatra, Srinivas Prasad, Sarat Chandra Nayak
Chapter 12. A Novel Data Analytics and Machine Learning Model towards Prediction and Classification of Chronic Obstructive Pulmonary DiseaseSridevi U.K., Sophia S., Boselin Prabhu S.R., Zubair Baig, P.Thamaraiselvi
Chapter 13. A Novel Multimodal risk disease prediction of Coronavirus by using Hierarchical LSTM methodsV. Kakulapati, Kanchipuram BasavaRaju, Appiah Prince, P. Shiva Kalyan
Chapter 14. Analytics in Education: An Educational Analysis Framework for Enhanced Learning OutcomesNazura Javed, Paul Anand
Chapter 15. Breast Invasive Ductal Carcinoma Classification Based on Deep Transfer Learning Models with Histopathology ImagesMd. Saikat Islam Khana, Pulak Kanti Bhowmicka, Nazrul Islama, Mostofa Kamal Nasira, Jia Uddinb
Chapter 16. Prediction of Acoustic Performance using Machine learning TechniquesRatnavel Rajalakshmi, S. Jeyanthi, Yuvaraj L, Pradeep M, Jeyakrishna S, Abhishek KrishnaswamiSection 4. Issue and Challenges in Data Science and Data Analytics
Chapter 17. Feedforward Multi-Layer Perceptron Training by Hybridized Method between Genetic Algorithm and Artificial Bee ColonyAleksa Cuk, Timea Bezdan, Nebojsa Bacanin, Miodrag Zivkovic, K Venkatachalam, Tarik A. Rashid, Kanchana Devi V
Chapter 18. Algorithmic Trading using Trend Following Strategy: Evidence from Indian Information Technology StocksMolla Ramizur Rahman
Chapter 19. A Novel Data Science Approach for Business and Decision Making for Prediction of Stock Market Movement using Twitter Data and News SentimentsS. Kumar Chandar, Hitesh Punjabi, Mahesh Kumar Sharda, Jehan Murugadhas
Chapter 20. Churn Prediction in Banking SectorShreyas Hingmire,Jawwad Khan, Ashutosh Pandey, Aruna Pavate
Chapter 21. Machine and Deep Learning Techniques for Internet of Things based Cloud SystemsRaswitha Bandi, K.Tejaswini
Section 5. Future Research Opportunities towards Data Science and Data Analytics
Chapter 22. Dialect Identification of Bengali LanguageElizabeth Behrman, Arijit Santra, Siladitya Sarkar, Prantik Roy, Ritika Yadav, Soumi Dutta, Arijit Ghosal
Chapter 23. Real Time Security Using Computer VisionBijoy Kumar Mandal, Niloy Sarkar
Chapter 24. Data Analytics for Detecting DDoS Attacks in Network TrafficCiza Thomas, Rejimol Robinson R R
Chapter 25. Detection of Patterns in Attributed Graph Using Graph MiningBapuji Rao
Chapter 26. Analysis and Prediction of the Update of Mobile Android VersionAparna Mohan, Maheswari. R

Über Autor(innen)

Amit Kumar Tyagi is Assistant Professor (Senior Grade), and Senior Researcher at Vellore Institute of Technology (VIT), Chennai Campus, India.

He earned his PhoD. in 2018 from Pondicherry Central University, India. He joined the Lord Krishna College of Engineering, Ghaziabad (LKCE) from 2009-2010, and 2012-2013. He was an Assistant Professor and Head - Research, Lingaya’s Vidyapeeth (formerly known as Lingaya’s University), Faridabad, Haryana, India in 2018-2019. His current research focuses on Machine Learning with Big data, Blockchain Technology, Data Science, Cyber Physical Systems, Smart and Secure Computing and Privacy. He has contributed to several projects such as "AARIN" and "P3- Block" to address some of the open issues related to the privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber Physical Systems (MCPS). He has published more than 8 patents in the area of Deep Learning, Internet of Things, Cyber Physical Systems and Computer Vision. He was recently awarded best paper award for paper titled "A Novel Feature Extractor Based on the Modified Approach of Histogram of oriented Gradient", ICCSA 2020, Italy (Europe). He is a regular member of the ACM, IEEE, MIRLabs, Ramanujan Mathematical Society, Cryptology Research Society, and Universal Scientific Education and Research Network, CSI and ISTE.