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Hands-On Data Science with Anaconda

Hands-On Data Science with Anaconda

By : Yuxing Yan, Yan
2.6 (5)
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Hands-On Data Science with Anaconda

Hands-On Data Science with Anaconda

2.6 (5)
By: Yuxing Yan, Yan

Overview of this book

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.
Table of Contents (15 chapters)
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Sources of data

For users in the area of data science and business analytics, one important issue is the source of data, or simply where to get data. When working at a company, the obvious source of data is one's own company, such as sales, cost of raw materials, the salary of managers and other employees, the related information of suppliers and clients, estimations of future sales, the cost of raw materials, and so on. It is a good idea to find some data for learning purposes, and this is especially true for full-time students.

Generally speaking, there are two types of data: public and private. Private or proprietary databases are quite expensive. A typical example is the Center for Research in Security Prices (CRSP) database, a financial database generated and maintained by the University of Chicago. This database has daily, weekly, monthly, and annual trading data for...

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