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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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Review questions and exercises

  1. Why should we care about data visualization?
  2. Where can we find lists of R, Python, and Julia packages associated with data visualization?
  3. Draw a graph using R and Python for the following formula:
  1. Based on R programming, put the following two graphs together:
  2. Download the R dataset related to the Fama-French monthly factor time series at http://canisius.edu/~yany/RData/ff3monthly.RData. Then, draw the histograms for these three factors: Market, SMB, and HML.
  3. Write an R program to generate 1,000 random numbers from a uniform distribution. Then, estimate their mean and standard deviation. Finally, draw a histogram. Note that the R function for drawing n random numbers from a uniform distribution is runif(n).
  4. Repeat the previous exercise using Python and Julia.

  1. Use Python to draw both the sine and cosine functions together.
  2. From a beta distribution...

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