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Hands-On Data Science with Anaconda: Utilize the right mix of tools to create high-performance data science applications
Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda
Key FeaturesUse Anaconda to find solutions for clustering, classification, and linear regressionAnalyze your data efficiently with the most powerful data science stackUse the Anaconda cloud to store, share, and discover projects and librariesBook DescriptionAnaconda 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, prediction, and building machine learning models and optimizing them. In addition to this, you'll learn how to visualize data using the packages available for Julia, Python, and R.
What you will learnPerform cleaning, sorting, classification, clustering, regression, and dataset modeling using AnacondaUse the package manager conda and discover, install, and use functionally efficient and scalable packagesGet comfortable with heterogeneous data exploration using multiple languages within a projectPerform distributed computing and use Anaconda Accelerate to optimize computational powersDiscover and share packages, notebooks, and environments, and use shared project drives on Anaconda CloudTackle advanced data prediction problemsWho this book is forHands-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.
Table of ContentsEcosystem of AnacondaAnaconda InstallationData basics Data visualization Statistics modeling in AnacondaManaging packagesOptimization in AnacondaUnsupervised Learning in AnacondaSupervised Learning in AnacondaPredictive Data Modelling and ValidationAnaconda CloudDistributed computing, parallel computing and HPCC
Key FeaturesUse Anaconda to find solutions for clustering, classification, and linear regressionAnalyze your data efficiently with the most powerful data science stackUse the Anaconda cloud to store, share, and discover projects and librariesBook DescriptionAnaconda 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, prediction, and building machine learning models and optimizing them. In addition to this, you'll learn how to visualize data using the packages available for Julia, Python, and R.
What you will learnPerform cleaning, sorting, classification, clustering, regression, and dataset modeling using AnacondaUse the package manager conda and discover, install, and use functionally efficient and scalable packagesGet comfortable with heterogeneous data exploration using multiple languages within a projectPerform distributed computing and use Anaconda Accelerate to optimize computational powersDiscover and share packages, notebooks, and environments, and use shared project drives on Anaconda CloudTackle advanced data prediction problemsWho this book is forHands-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.
Table of ContentsEcosystem of AnacondaAnaconda InstallationData basics Data visualization Statistics modeling in AnacondaManaging packagesOptimization in AnacondaUnsupervised Learning in AnacondaSupervised Learning in AnacondaPredictive Data Modelling and ValidationAnaconda CloudDistributed computing, parallel computing and HPCC
651 pages, Kindle Edition
Published May 31, 2018
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11 people want to read
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Displaying 1 - 2 of 2 reviews
October 5, 2020
This book tries to explain some key concepts about R, Python, Julia and Octave with the only connection being they all can be run in Anaconda. Apart from this, there is no a single section really dedicated to Anaconda (environments, troubleshooting, possibilities, etc). Anaconda is used in the title and some section titles but I still don’t understand why. The topics selected for the content are completely arbitrary with a lot of mathematical formula here and there, big chunks of coded (mostly from different internet pages and on-line tutorials) with no explanation at all of what is in the code. The table of contents is highly arbitrary and disorganized.
In summary, I would not call this a book. Just take a random list of webpages with these keywords, bind them together (unsupervised clustering ;-) and voila!
In summary, I would not call this a book. Just take a random list of webpages with these keywords, bind them together (unsupervised clustering ;-) and voila!
January 31, 2022
While there might be some valuable information here. The title of the book doesn't really hold true throughout the book. Essentially we are very light on information pertaining to Anaconda itself and far too much information on Data Science. Blending and combining these two subjects should have been the subject of this work. Rather the book consists of a very simple survey of all this information.
Displaying 1 - 2 of 2 reviews