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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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Jupyter Notebook in depth

In this section, many examples are borrowed from the following web page: https://github.com/ipython/ipython-in-depth. If you are interested, you can explore more by going to the web page in order to download a ZIP file. The following screenshot shows the content of examples:

After launching Jupyter Notebook, we can search the example subdirectory. For example, we can upload a notebook called factoring.ipynb under the Interactive Widgets subdirectory(see the following screenshot):

After clicking Run, we can change the value of n (see the following result when we choose 8 for the variable):

After increasing n to 20, we have the corresponding output:

Sometimes, after running a Jupyter Notebook and logging out, we need a token or password to login again. We can run the following code to locate our token:

Jupyter notebook list 

Alternatively, we can...

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