http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda About This Book Use Anaconda to find solutions for clustering, classification, and linear regression Analyze your data efficiently with the most powerful data science stack Use the Anaconda cloud to store, share, and discover projects and libraries Who This Book Is For Hands-On Data Science with Anaconda is for you if you are a developer who is looking for the best tools in the market to perform data science. It's also ideal for data analysts and data science professionals who want to improve the efficiency of their data science applications by using the best libraries in multiple languages. Basic programming knowledge with R or Python and introductory knowledge of linear algebra is expected. What You Will Learn Perform cleaning, sorting, classification, clustering, regression, and dataset modeling using Anaconda Use the package manager conda and discover, install, and use functionally efficient and scalable packages Get comfortable with heterogeneous data exploration using multiple languages within a project Perform distributed computing and use Anaconda Accelerate to optimize computational powers Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud Tackle advanced data prediction problems In Detail Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You'll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You'll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you're accustomed to all this, you'll start with operations in data science such as cleaning, sorting, and data classification. You'll move on to learning how to perform tasks such as clustering, regression, pr...
Electronic reproduction. Boston, MA : Safari, Available via World Wide Web. 2018.