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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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Importance of data visualization

For learners, users, or researchers in the areas of data science and business analytics, using various types of graphs, pie charts, bar charts, and other visual means to show some underlying trend or pattern implied by data is vital to understand data and to help researchers present their data to their audience or clients better. There are several reasons for this. First, it is sometimes difficult to describe our findings, especially when we have several patterns or influencing factors. With several separate graphs and a joint one, complex relationships can be understood or explained better.

We can use graphs or pictures to explain certain algorithms, such as the Bisection method (see the section related to dynamic visual presentation, Dynamic visualization).

We can also use relative sizes to represent different meanings. In finance, a basic concept...

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