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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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Data visualization in Julia

For the following Julia program, we use a package called Plots. The command used to install the package is Pkg.add("Plots"). Here, we run Julia programs via a Jupyter notebook. The Julia program is presented in the following screenshot:

After clicking Kernel on the menu bar, and then Restart and Run All, we get the following:

Again, the srand(123) command guarantees that any user who applies the same random seed will get the same set of random numbers. Because of this, he/she would get the same graph shown previously. The next example is a scatter plot using a Julia package called PyPlot:

using PyPlot 
n=50 
srand(333) 
x = 100*rand(n) 
y = 100*rand(n) 
areas = 800*rand(n) 
fig = figure("pyplot_scatterplot",figsize=(10,10)) 
ax = axes() 
scatter(x,y,s=areas,alpha=0.5) 
title("using PyPlot: Scatter Plot") 
xlabel(&quot...
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