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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.
The unifying concept of Data Science integrates statistics, data analysis, Machine Learning, and related methodologies to comprehend and analyze real-­world phenomena through data. Drawing on applied mathematics, statistics, Computer Science, and information and communication technologies, Data Science represents the empirical synthesis
of actionable knowledge throughout the entire data lifecycle process.
R, a freely available statistical programming language, serves as cost-­free statistical software. It was developed with a user-­friendly graphical interface (GUI), facilitating the teaching and learning of statistics or data science courses. By simplifying software complexity, it eliminates barriers to understanding content. R boasts numerous packages, such as R Commander, some equipped with drop-­down menus for streamlined statistical data analysis. As a leading alternative to commercial statistical packages like SPSS, the R package is particularly useful for beginners, as it reveals the underlying R code for each analysis performed.
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.
Written in an exclusive state-of-the-art deductive approach without any theoretical hitches to support all contemporary readers.
Preface
Introductory Data Sciences
Contemporary Concepts of Biostatistics
Summary Statistics and Presentation of Data
Advanced Graphical Presentation of Data
Measures of Centre and Dispersion
Probability, Random Variables and Distributions
Statistical Inferences
Normality Testing
Nonparametric Tests and Applications
Statistical Association and Correlation
Regression Analysis
Survival Analysis and Factor Analysis