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 is the definition of a single-factor linear model?
  2. How many independent variables are there in a single-factor model?
  3. What does it mean for something to be statically different from zero?
  4. What are the critical T-values and P-values to tell whether an estimate is statistically significant?
  5. When the significant level is 1%, what is the critical T-value when there are 30 degrees of freedom?

  1. What is the difference between a one-sided test and a two-sided test?
  2. What are the corresponding missing codes for missing data items in R, Python, and Julia?
  3. How do we treat missing variables if our sample is big? How about if our sample is small?
  4. How do we generally detect outliners and deal with them?
  5. How do we generate a correlated return series? For example, write an R program to generate 5-year monthly returns for two stocks with a fixed correlation...