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ADSA 2021 : Advances in Data Science and Analytics: Concept and Paradigm

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Link: https://sites.google.com/view/adscandda
 
When N/A
Where WILEY-Scrivener
Abstract Registration Due Sep 25, 2020
Submission Deadline Nov 10, 2020
Notification Due Nov 20, 2020
Categories    data analytics   machine learning   deep learning   data science
 

Call For Papers

Book Title:
Advances in Data Science and Analytics: Concept and Paradigm

List of Topics (not limited to):
 Components of Data Science
 Data Visualization techniques
 Decision Making and Predictive Analysis
 Data Modeling and Optimization
 Machine Learning -Supervised Learning and Unsupervised Learning
 Deep Learning, Big Data Analytics
 Data Science with R
 Python for Data Science
 Mathematical methods for Data Science
 Advanced Tools to support Data Science and Analytics

About Book: This book will provide the contents of Advances in Data Science and Analytics Concept and Paradigm with practical approach. Topics to be covered - Components of Data Science - Machine Learning, Big Data, Business Intelligence, Types of Analytics - Descriptive Analytics. Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics, Mathematics for Data Science, Data Visualization techniques, Data Visualization in Tableau, Decision Making and Predictive Analysis - Implementing Scientific Decision Making, Using Predictive Data Analysis, Data Modeling and Optimization, Machine Learning, Regression Techniques, Data exploration, Evaluation methods, Classification Techniques, Clustering Techniques, Anomaly Detection, Dimensionality Reduction, Association Rule Learning, Deep Learning, Neural Networks, Big Data Analytics, Data Science with R, Python for Data Science, Building a Data Team, Data Processing, Data Storage, Data Privacy and security, Bayesian Networks, and Case studies.

How to Submit Your Chapter:
Send your 500-word abstract by the designated deadline to callforchaptersnm@gmail.com
Note that all chapters will be put through similarity software and publisher’s guidelines are an overall similarity index of less than 15% (with a maximum 3% from any single source).

Reviewing Policy: The editor(s) follows the double-blind review process to assess originality, clarity, usefulness and adherence to the scope of the project. Editor(s) are responsible for the final decision regarding acceptance or rejection of chapters.

Editor(s):
Dr. Niranjanamurthy M, M S Ramaiah Institute of Technology, India
Dr. Hemant Kumar Gianey, Thapar Institute of Engineering & Technology, India
Prof. Amir H. Gandomi, University of Technology Sydney, Australia

For more details, visit this website: https://sites.google.com/view/adscandda

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