Book Image

Hands-On Data Science with Anaconda

By : Yuxing Yan, James Yan
Book Image

Hands-On Data Science with Anaconda

By: Yuxing Yan, James 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)

Review questions and exercises

  1. What does optimization mean?
  2. What is an LPP? What are its uses?
  3. What is the difference between a global solution and a local solution?
  4. In what situations would our LPP program not converge? Give a few simple examples and possible solutions.
  5. Explain why we have the following weird result:
> f<-function(x)-2*x^2+3*x+1 
> optim(13,f) 
$par 
[1] 2.352027e+75 
$value 
[1] -1.106406e+151 
$counts 
function gradient  
     502       NA  
$convergence 
[1] 1 
$message 
NULL
  1. What does quadratic equation mean?
  2. From where could we search all the R packages targeting optimization issues?
  3. What is the usage of the task view related to optimization?
  4. According to the related task view, how many R packages are associated with optimization, and how do we install them all at once?
  5. From the Prof. French Data Library, download the return data for 10 industries...