Our utmost objective in conducting various data analyses is trying to find patterns in order to predict what might happen in the future. For the stock market, researchers and professionals are conducting various tests to understand market mechanisms. In this case, many questions could be asked. What will the market index level be in the next five years? What will IBM's price range be next year? Will the market volatility increase or decrease in the future? What might be the impact if governments change their tax policies? What is the potential gain and loss if one country launches a trade war with another one? How do we predict a consumer's behavior by analyzing some related variables? Could we predict the probability that an undergraduate student will successfully graduate? Could we find an association between...
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Book Overview & Buying
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Table Of Contents
Hands-On Data Science with Anaconda
By :
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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