- Why is understanding various packages important?
- What are package dependencies?
- For R, Python, Julia, and Octave, find out how many packages are available for each of them, today.
- How do we install a package in R, Python, and Julia?
- How do we update a package in R, Python, and Julia?
- What is the task view for R?
- How do we install all R packages included in a task view?
- After an R package is installed, how do you find its related directory? What is the usage to find its related directory? You could use the R package called healthcare as an example. Note that the package is about tools for healthcare machine learning.
- Find out more details about the task views related to the subject of Econometrics. Then install all related R packages. How many are there?
- How do we update one R package? How would we do it for Octave?
- How do we find the manual for...
Hands-On Data Science with Anaconda
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Hands-On Data Science with Anaconda
By:
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)
Preface
Free Chapter
Ecosystem of Anaconda
Anaconda Installation
Data Basics
Data Visualization
Statistical Modeling in Anaconda
Managing Packages
Optimization in Anaconda
Unsupervised Learning in Anaconda
Supervised Learning in Anaconda
Predictive Data Analytics – Modeling and Validation
Anaconda Cloud
Distributed Computing, Parallel Computing, and HPCC
References
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