Hands-On Data Science with Anaconda: Utilize the right mix of tools to create high-performance data science applications

Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda

Key Features

• Use Anaconda to find solutions for clustering, classification, and linear regression
• Analyze your data efficiently with the most powerful data science stack
• Use the Anaconda cloud to store, share, and discover projects and libraries

Book Description

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.

What you will learn

• Perform cleaning, sorting, classification, clustering, regression, and dataset modeling using Anaconda
• Use the package manager conda and discover, install, and use functionally efficient and scalable packages
• Get comfortable with heterogeneous data exploration using multiple languages within a project
• Perform distributed computing and use Anaconda Accelerate to optimize computational powers
• Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud
• Tackle advanced data prediction problems

Who This Book Is For

Hands-On Data Science with Anaconda is for you if you are a developer who is looking for the best tools in the market to perform data science. It's also ideal for data analysts and data science professionals who want to improve the efficiency of their data science applications by using the best libraries in multiple languages. Basic programming knowledge with R or Python and introductory knowledge of linear algebra is expected.

Table of Contents

1. Ecosystem of Anaconda
2. Anaconda Installation
3. Data basics
4. Data visualization
5. Statistics modeling in Anaconda
6. Managing packages
7. Optimization in Anaconda
8. Unsupervised Learning in Anaconda
9. Supervised Learning in Anaconda
10. Predictive Data Analytics: Modelling and Validation
11. Anaconda Cloud
12. Distributed computing, parallel computing and HPCC

Dr. Yuxing Yan graduated from McGill University with a PhD in finance. He has taught various finance courses at eight universities in Canada, Singapore, and the U.S. He has published 23 research and teaching-related papers and is the author of 6 books. Two of his recent publications are Python for Finance and Financial Modelling using R. He is well-versed in R, Python, SAS, MATLAB, Octave, and C. In addition, he is an expert on financial data analytics.

James Yan is an undergraduate student at the University of Toronto (UofT), currently double-majoring in computer science and statistics. He has hands-on knowledge of Python, R, Java, MATLAB, and SQL. During his study at UofT, he has taken many related courses, such as Methods of Data Analysis I and II, Methods of Applied Statistics, Introduction to Databases, Introduction to Artificial Intelligence, and Numerical Methods, including a capstone course on AI in clinical medicine.