Hands-On Data Science with Anaconda - Couverture souple

Yan, Dr Yuxing; Yan, James

Couverture souple

ISBN 10 : 1788831195 ISBN 13 : 9781788831192

Editeur : Packt Publishing, 2018

Synopsis

Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda

Key Features

• 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

Book Description

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, 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 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

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.

Table of Contents

1. Ecosystem of Anaconda
2. Anaconda Installation
3. Data basics
4. Data visualization
5. Statistics modeling in Anaconda
6. Managing packages
7. Optimization in Anaconda
8. Unsupervised Learning in Anaconda
9. Supervised Learning in Anaconda
10. Predictive Data Analytics: Modelling and Validation
11. Anaconda Cloud
12. Distributed computing, parallel computing and HPCC

A propos du livre

Présentation de l'éditeur

Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda

Key Features

• 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

Book Description

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, 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 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

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.

Table of Contents

1. Ecosystem of Anaconda
2. Anaconda Installation
3. Data basics
4. Data visualization
5. Statistics modeling in Anaconda
6. Managing packages
7. Optimization in Anaconda
8. Unsupervised Learning in Anaconda
9. Supervised Learning in Anaconda
10. Predictive Data Analytics: Modelling and Validation
11. Anaconda Cloud
12. Distributed computing, parallel computing and HPCC

Biographie de l'auteur

Dr. Yuxing Yan

Since graduated from McGill with a Ph.D. in finance, Dr. Yan has been teaching various finance courses at 8 universities (2 in Canada, 1 in Singapore and 5 in the US). He has 23 publications including papers published in Journal of Accounting and Finance, Journal of Banking and Finance, Journal of Empirical Finance, Real Estate Review, Pacific Basin Finance Journal, Applied Financial Economics and Annals of Operations Research. He is good at several computer languages such as R, Python, SAS, Matlab and C. From 2003 to 2010, he worked at Wharton School as a consultant helping researchers with their programs and data issues. He has published 5 books: "Python for Finance" (2014, 2nd edition 2017, both Chinese and Korean editions 2017), "Financial Modeling using R" (2016), "Financial Databases" (2008, with S.W. Zhu, in Chinese). He is an expert on financial data analytics.

James Yan

A senior at Univresity of Toronto with a mojon in Computer Science. He is very good at Python, R, Java, Matlab and SQL. During his study at UT, he took many related courses such as Methods of Data Analysis I and II, Methods of Applied Statistics, Introduction to Databases, Introduction to Artificial Intelligence, Numerical Methods, Data Structures and Algorithms and Computer Organization.

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2018
• Langue: anglais
• ISBN 10: 1788831195
• ISBN 13: 9781788831192
• Reliure: Broché
• Nombre de pages: 364
• Coordonnées du fabricant: non disponible