04646cam a2200613Mi 4500 9781003090113 FlBoTFG 20260210180808.0 m o d cr |n||||||||| 210225s2021 flu ob 001 0 eng d OCoLC-P eng OCoLC-P 9781000345483 (electronic bk.) 1000345483 (electronic bk.) 9781003090113 (electronic bk.) 1003090117 (electronic bk.) 9781000345490 (electronic bk. : Mobipocket) 1000345491 (electronic bk. : Mobipocket) 9781000345506 (electronic bk. : EPUB) 1000345505 (electronic bk. : EPUB) 9780367546793 0367546795 9780367546786 0367546787 10.1201/9781003090113 doi (OCoLC)1239748290 (OCoLC-P)1239748290 QH324.2 SCI 010000 bisacsh MED 009000 bisacsh SCI 008000 bisacsh PS bicssc 570.285 23 Hasija, Yasha, author. Hands on data science for biologists using Python [electronic resource] / Yasha Hasija and Rajkumar Chakraborty. First edition. Boca Raton : CRC Press, 2021. 1 online resource text rdacontent still image rdacontent computer rdamedia online resource rdacarrier Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day biologists. With the advent of high throughput technologies and consequent availability of omics data, biological science has become a data-intensive field. This hands-on textbook has been written with the inception of easing data analysis by providing an interactive, problem-based instructional approach in Python programming language. The book starts with an introduction to Python and steadily delves into scrupulous techniques of data handling, preprocessing, and visualization. The book concludes with machine learning algorithms and their applications in biological data science. Each topic has an intuitive explanation of concepts and is accompanied with biological examples. Features of this book: The book contains standard templates for data analysis using Python, suitable for beginners as well as advanced learners. This book shows working implementations of data handling and machine learning algorithms using real-life biological datasets and problems, such as gene expression analysis; disease prediction; image recognition; SNP association with phenotypes and diseases. Considering the importance of visualization for data interpretation, especially in biological systems, there is a dedicated chapter for the ease of data visualization and plotting. Every chapter is designed to be interactive and is accompanied with Jupyter notebook to prompt readers to practice in their local systems. Other avant-garde component of the book is the inclusion of a machine learning project, wherein various machine learning algorithms are applied for the identification of genes associated with age-related disorders. A systematic understanding of data analysis steps has always been an important element for biological research. This book is a readily accessible resource that can be used as a handbook for data analysis, as well as a platter of standard code templates for building models. Python : introduction and environment set up -- Basic Python programming -- BioPython -- Python for data analysis -- Python for data visualization -- Principal component analysis -- Hands-on projects -- Machine learning and linear regression -- Logistic regression -- K-nearest neighbors (K-NN) -- Decision trees and random forests -- Support vector machines -- Neural nets and deep learning -- The machine learning project -- Natural language processing -- K-means clustering. OCLC-licensed vendor bibliographic record. Biology Data processing. Python (Computer program language) SCIENCE / Biotechnology bisacsh MEDICAL / Biotechnology bisacsh SCIENCE / Life Sciences / Biology / General bisacsh Chakraborty, Rajkumar, author. Taylor & Francis https://www.taylorfrancis.com/books/9781003090113 OCLC metadata license agreement http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf 91279 91278