Hands-On Data Science for Biologists Using Python

Name: Hands-On Data Science for Biologists Using Python
Brand: Taylor & Francis Ltd
SKU: 9780367546793
Price: 100652 HUF
Availability: InStock

Szerző: Hasija, Yasha (Delhi Technological University, Delhi, India), Chakraborty, Rajkumar (Delhi Technological University, Delhi, India)

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Kötés: Kemény kötésű

Kiadó: Taylor & Francis Ltd

Elérhetőség: 50 % esély

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Hands-on Data Science for Biologists using Python has been conceptualized to address the massive dat...

Információk a könyvről

Szerző

Hasija, Yasha (Delhi Technological University, Delhi, India), Chakraborty, Rajkumar (Delhi Technological University, Delhi, India)

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2021

oldal

298

EAN

9780367546793

ISBN

0367546795

Enbook ID

33388385

Kiadó

Taylor & Francis Ltd

Súly

710

Méretek

178 x 254

Teljes leírás

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.

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Hands-On Data Science for Biologists Using Python

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