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Data science and SDGs = challenges, ...

Sinha, Bikas Kumar.

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Data science and SDGs = challenges, opportunities and realities /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science and SDGs/ edited by Bikas Kumar Sinha, Md. Nurul Haque Mollah.
Reminder of title:	challenges, opportunities and realities /
other author:	Sinha, Bikas Kumar.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	xxii, 197 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: SDGs in Bangladesh: Implementation Challenges & Way Forward -- Chapter 2: Some Models and Their Extensions for Longitudinal Analyses -- Chapter 3: Association of IL-6 Gene rs1800796 Polymorphism with Cancer Risk: A Meta-Analysis -- Chapter 4: Two Level Logistic Regression Analysis of Factors Influencing Dual form of Malnutrition in Mother-child Pairs: A Household Study in Bangladesh -- Chapter 5: Divide and Recombine Approach for Analysis of Failure Data Using Parametric Regression Model -- Chapter 6: Performance of different data mining methods for predicting rainfall of Rajshahi district, Bangladesh -- Chapter 7: Generalized Vector Auto-regression Controlling Intervention and Volatility for Climatic Variables -- Chapter 8: Experimental Designs for fMRI Studies in Small Samples -- Chapter 9: Bioinformatic Analysis of Differentially Expressed Genes (DEGs) Detected from RNA-Sequencing Profiles of Mouse Striatum -- Chapter 10: Level of Serum High-sensitivity C-reactive protein Predicts Atherosclerosis and Coronary Artery Disease in Hyperglycemic Patients -- Chapter 11: Identification of Outliers in Gene Expression Data -- Chapter 12: Selecting Covariance Structure to Analyze Longitudinal Data: A Study to Model the Body Mass Index of Primary School Going Children in Bangladesh -- Chapter 13: Statistical Analysis of Various Optimal Latin Hypercube Designs -- Chapter 14: Erlang Loss Formulas: An Elementary Derivation -- Chapter 15: Machine Learning, Regression and Numerical Optimization.
Contained By:	Springer Nature eBook
Subject:	Economics - Congresses. - Statistical methods -
Online resource:	https://doi.org/10.1007/978-981-16-1919-9
ISBN:	9789811619199

Data science and SDGs = challenges, opportunities and realities /
Data science and SDGschallenges, opportunities and realities /[electronic resource] :edited by Bikas Kumar Sinha, Md. Nurul Haque Mollah. - Singapore :Springer Singapore :2021. - xxii, 197 p. :ill., digital ;24 cm.

Chapter 1: SDGs in Bangladesh: Implementation Challenges & Way Forward -- Chapter 2: Some Models and Their Extensions for Longitudinal Analyses -- Chapter 3: Association of IL-6 Gene rs1800796 Polymorphism with Cancer Risk: A Meta-Analysis -- Chapter 4: Two Level Logistic Regression Analysis of Factors Influencing Dual form of Malnutrition in Mother-child Pairs: A Household Study in Bangladesh -- Chapter 5: Divide and Recombine Approach for Analysis of Failure Data Using Parametric Regression Model -- Chapter 6: Performance of different data mining methods for predicting rainfall of Rajshahi district, Bangladesh -- Chapter 7: Generalized Vector Auto-regression Controlling Intervention and Volatility for Climatic Variables -- Chapter 8: Experimental Designs for fMRI Studies in Small Samples -- Chapter 9: Bioinformatic Analysis of Differentially Expressed Genes (DEGs) Detected from RNA-Sequencing Profiles of Mouse Striatum -- Chapter 10: Level of Serum High-sensitivity C-reactive protein Predicts Atherosclerosis and Coronary Artery Disease in Hyperglycemic Patients -- Chapter 11: Identification of Outliers in Gene Expression Data -- Chapter 12: Selecting Covariance Structure to Analyze Longitudinal Data: A Study to Model the Body Mass Index of Primary School Going Children in Bangladesh -- Chapter 13: Statistical Analysis of Various Optimal Latin Hypercube Designs -- Chapter 14: Erlang Loss Formulas: An Elementary Derivation -- Chapter 15: Machine Learning, Regression and Numerical Optimization.

The book presents contributions on statistical models and methods applied, for both data science and SDGs, in one place. Measuring and controlling data of SDGs, data driven measurement of progress needs to be distributed to stakeholders. In this situation, the techniques used in data science, specially, in the big data analytics, play an important role rather than the traditional data gathering and manipulation techniques. This book fills this space through its twenty contributions. The contributions have been selected from those presented during the 7th International Conference on Data Science and Sustainable Development Goals organized by the Department of Statistics, University of Rajshahi, Bangladesh; and cover topics mainly on SDGs, bioinformatics, public health, medical informatics, environmental statistics, data science and machine learning. The contents of the volume would be useful to policymakers, researchers, government entities, civil society, and nonprofit organizations for monitoring and accelerating the progress of SDGs.

ISBN: 9789811619199

Standard No.: 10.1007/978-981-16-1919-9doiSubjects--Topical Terms:

3506160
Economics
--Statistical methods--Congresses.

LC Class. No.: HB137 / .D37 2021

Dewey Class. No.: 330.015195

Data science and SDGs = challenges, opportunities and realities /
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520 $a The book presents contributions on statistical models and methods applied, for both data science and SDGs, in one place. Measuring and controlling data of SDGs, data driven measurement of progress needs to be distributed to stakeholders. In this situation, the techniques used in data science, specially, in the big data analytics, play an important role rather than the traditional data gathering and manipulation techniques. This book fills this space through its twenty contributions. The contributions have been selected from those presented during the 7th International Conference on Data Science and Sustainable Development Goals organized by the Department of Statistics, University of Rajshahi, Bangladesh; and cover topics mainly on SDGs, bioinformatics, public health, medical informatics, environmental statistics, data science and machine learning. The contents of the volume would be useful to policymakers, researchers, government entities, civil society, and nonprofit organizations for monitoring and accelerating the progress of SDGs.
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