Data science and SDGs: challenges, opportunities and realities

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.

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 Anal…