In our lives, people face all kinds of choices. In a sense, we implement, either consciously or subconsciously, various kinds of implied optimization procedures. For example, when a high school junior or senior is looking for a college, they might have many choices, such as good schools, local schools, public schools, or private schools. When making a decision with a couple of offers, these high school students usually have some objectives in their mind. These objectives might include the ranking and cost of attending a school, scholarships, the reputation, and name recognition of the program, or even the fame of the football team. For corporations, they have to make all kinds of optimal or reasonable decisions. For instance, what kinds of products they should produce, what are the quantities and at what prices, and to whom they should target. Since...
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