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. Why do we care about predicting the future?
  2. What does seasonality mean? How could it impact our predictions?
  3. How does one measure the impact of seasonality?
  4. Write an R program to use the moving average of the last five years to predict the next year's expected return. The source of the data is http://fiannce.yahoo.com. You can test a few stocks such as IBM, C, and WMT. In addition, apply the same method to the S&P500 index. What is your conclusion?
  5. Assume that we have the following true model:

Write a Python program to use linear and polynomial models to approximate the previous function and show the related graphs.

  1. Download a market index monthly data and estimate its next year's annual return. The S&P500 could be used as the index and Yahoo!Finance at finance.yahoo.com could be used as the source of data. Source of data: https...