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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. What is Anaconda and how do we use its platform?
  2. How many open source packages are accompanied with Anaconda?
  3. What is the home page for Anaconda?
  4. How do we install Anaconda? After Anaconda is installed, should we install Python separately? What about R?
  5. What is the size of a full Anaconda installation?
  6. Why should we care about Miniconda?
  7. What is Jupyter? How do we launch it without installation?
  8. What are the advantages and disadvantages of using https://jupyter.org/try?
  9. Where could a new learner find more useful information about Anaconda?
  10. Get more information about the Julia programming language.

  1. How do we write a simple program in Julia via Jupyter?
  2. How do we write a simple program in R via Jupyter?
  3. How do we find help for Jupyter?
  4. What is the conda Cheat Sheet and where can we download it?
  5. Could we run a simple R program without installing Anaconda?
  6. Could we run Anaconda without pre-installing it?
  7. Try the following two lines of Python code:
import numpy as np
print(np.sqrt(2))
  1. Try the following simple code for R:
x<-1:500
mean(x)
sd(x)
  1. Try the following code for Julia:
x=1:500
mean(x)
  1. Try the following code for R:
dd<-Sys.Date()
dd+40

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