Dynamic visualization means that we can see the concept or theory vividly, just like a video or movie. For example, in the Monte Carlo simulation, we could generate a set of random numbers from certain distributions, such as a uniform or a normal distribution. If we could show how particles moved within a range, learners would understand the concept of randomness better. This is like dropping red ink into a basin of water and observing how the ink diffuses. Another example is related to the so-called bisection method or bisection search. Assume that we have a database that contains daily trading data for over 5,000 stocks. There are many ways to retrieve one specific stock's information. One is called sequential search. In a sequential search, we pick up the first stock and compare it with our target. If it doesn't match, we go to the next stock...
Hands-On Data Science with Anaconda
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Hands-On Data Science with Anaconda
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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.
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Hands-On Data Science with Anaconda
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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Customer Reviews