1st Edition

Scientific Data Analysis with R Biostatistical Applications

    336 Pages 86 Color & 5 B/W Illustrations
    by Chapman & Hall

    In an era marked by exponential growth in data generation and an unprecedented convergence of technology and healthcare, the intersection of biostatistics and data science has become a pivotal domain. This book is the ideal companion in navigating the convergence of statistical methodologies and data science techniques with diverse applications implemented in the open-source environment of R. It is designed to be a comprehensive guide, marrying the principles of biostatistics with the practical implementation of statistics and data science in R, thereby empowering learners, researchers, and practitioners with the tools necessary to extract meaningful knowledge from biological, health and medical datasets.

    This book is intended for students, researchers, and professionals eager to harness the combined power of biostatistics, data science, and the R programming language while gathering vital statistical knowledge needed for cutting-edge scientists in all fields. It is useful for those seeking to understand the basics of data science and statistical analysis, or looking to enhance their skills in handling any simple or complex data including biological, health, medical, and industry data.

    Key Features:

    • Presents contemporary concepts of data science and biostatistics with real life data analysis examples.
    • Promotes the evolution of fundamental and advanced methods applying to real-life problem-solving cases.
    • Explores computational statistical data science techniques from initial conception to recent developments of biostatistics.
    • Provides all R codes and real-world datasets to practice and competently apply into reader’s own domains. 


    1. Introductory Data Sciences 

    2. Contemporary Concepts of Biostatistics 

    3. Summary Statistics and Presentation of Data 

    4. Advanced Graphical Presentation of Data 

    5. Measures of Centre and Dispersion 

    6. Probability, Random Variables and Distributions 

    7. Statistical Inferences 

    8. Normality Testing 

    9. Nonparametric Tests and Applications 

    10. Statistical Association and Correlation 

    11. Regression Analysis 

    12. Survival Analysis and Factor Analysis


    Azizur Rahman is an associate professor at the School of Computing, Mathematics and Engineering and the 'Data Mining Research Group' leader at Charles Sturt University, Australia. He earned a BSc (Honours) in Statistical Science, an MSc (Thesis) in Biostatistics, and a PhD in Economics and Statistics from the University of Canberra under the supervision of Professor Ann Harding, AO FASSA. He worked as a biostatistical research fellow in the Faculty of Health and Medical Sciences at the University of Adelaide. Professor Rahman is a statistician and data scientist with expertise in developing and applying novel methodologies, models and technologies. He designs projects to understand multi-disciplinary research issues within various fields with the interaction or adaptation of statistics, data science, AI, and ML. Professor Rahman develops data-centric "alternative computational methods in microsimulation modelling technologies", which are handy tools for decision-making processes in government and non-government organizations, precision estimation, policy analysis and evaluation. He founded and runs the 'Data Analytics Lab' at Charles Sturt. Professor Rahman has accrued more than $3.4 million of external research funding and over 200 scholarly publications and received several awards, including the 2023 Charles Sturt Excellence Awards and the ANZRSAI's 2023 Outstanding Service Award.

    Faruq Abdulla is an outstanding graduate researcher, statistician, and data scientist, adeptly practicing in academia and industry. His expertise includes applying and developing sophisticated statistical, data science, and machine learning methodologies, models, and techniques in biological and medical sciences. With a keen focus on high-dimensional simulation and real-world data, he tackles pressing public health challenges, thereby contributing to evidence-based policy formulation. He has completed an MSc (Thesis) and a BSc (Honors) in Statistics from the Islamic University, Kushtia, Bangladesh. His academic excellence is evident through his first place in his class in order of merit at both the BSc and MSc levels, earning him the prestigious Presidential Gold Medal for achieving the highest marks in the Faculty of Applied Science & Technology in the MSc final examination. Moreover, Abdulla actively contributes to the scientific community by advancing scientific knowledge through his research findings published in renowned international peer-reviewed and high-impact journals indexed in SCOPUS and SCI. Additionally, he serves as a discerning reviewer for esteemed peer-reviewed journals published by world-class publishers.

    Md. Moyazzem Hossain is an applied statistician and data scientist specializing in developing and applying contemporary statistical and data science methodologies, models, and techniques and currently holding the position of Professor in the Department of Statistics and Data Science at Jahangirnagar University, Bangladesh. Hossain earned his PhD from the School of Mathematics, Statistics, and Physics at Newcastle University, UK. He also obtained his BSc (Honors), MSc (Thesis), and MPhil from the Department of Statistics, Jahangirnagar University, Bangladesh. Hossain's outstanding contributions have been recognized through accolades such as the "Best Conference Paper" award at the Australia and New Zealand Regional Science Association International 45th Annual Conference, held at Charles Sturt University, Wagga Wagga, Australia, on 1-2 December 2022. His research findings have been disseminated through numerous peer-reviewed publications in esteemed journals. Additionally, Hossain has served as an academic editor for PloS ONE and contributed as a reviewer for various international journals.