Hands-On Data Science with Anaconda

Utilize the right mix of tools to create high-performance data science applications

Dr. Yuxing Yan, James Yan

• Publisher: Packt Publishing
• Year: 2018
• ISBN:
  9781788834735
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Modelling & Design
• Index: Computer Science

Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, AnacondaAbout This Book• 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 librariesWho This Book Is ForHands-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.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 problemsIn DetailAnaconda 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.Style and approachThis book is your step-by-step guide full of use cases, examples and illustrations to get you well-versed with the concepts of Anaconda.

Data Basics

In this chapter, we'll first discuss sources of open data, which includes the University of California at Irvine (UCI) Machine Learning Depository, the Bureau of Labor Statistics, the Census Bureau, Professor French's Data Library, and the Federal Reserve's Data Library. Then, we will show you several ways of inputting data, how to deal with missing values, sorting, choosing a subset, merging different datasets, and data output. For different languages, such as Python, R, and Julia, several relevant packages for data manipulation will be introduced as well. In particular, the Python pandas package will be discussed.

In this chapter, the following topics will be covered:

• Sources of data
• Introduction to the Python pandas package
• Several ways to inputting packages
• Introduction to the Quandl data delivery platform
• Dealing with missing data
• Sorting data, as well as how to slice, dice, and merge various datasets
• Introduction to Python packages: cbsodata and datadotword
• Introduction to R packages: dslabs, haven, and foreign
• Generating Python datasets
• Generating R datasets

Sources of data

For users in the area of data science and business analytics, one important issue is the source of data, or simply where to get data. When working at a company, the obvious source of data is one's own company, such as sales, cost of raw materials, the salary of managers and other employees, the related information of suppliers and clients, estimations of future sales, the cost of raw materials, and so on. It is a good idea to find some data for learning purposes, and this is especially true for full-time students.

Generally speaking, there are two types of data: public and private. Private or proprietary databases are quite expensive. A typical example is the Center for Research in Security Prices (CRSP) database, a financial database generated and maintained by the University of Chicago. This database has daily, weekly, monthly, and annual trading data for all stocks listed on stock exchanges in the US from 1926 onward.

The second type of data is public or free data. For users in various data science or business analytics programs, this type of data is more than enough. For example, the UCI offers many useful datasets for machine learning that can be used for testing and learning purposes. This offers great benefits to new learners in the area of data science. Later in the chapter, several lists of free data will be offered for learners in data science, economics, and finance and accounting.

UCI machine learning

The UCI maintains 413 datasets, as of 1/10/2018, for machine learning: http://archive.ics.uci.edu/ml/index.php. The following screenshot shows the top three downloaded datasets:

For the number one downloaded dataset called Iris, we have the following information:

The beauty of these datasets is that they give quite detailed information such as the source, the creator or donator, a description, and even citations.

The following table shows several potential public data sources for users in the area of data science and business analytics:

Name	Web page	Data types
UCI	http://archive.ics.uci.edu/ml/index.php	Data for machine learning
World Health Organization	http://www.who.int/en/	Healthcare data
Amazon Web Services	https://aws.amazon.com/cn/datasets/?nc1=h_ls	Web usage
Data.gov (US Government Open Data)	https://www.data.gov/	Agriculture, climate, consumer, and so on
GitHub	https://github.com/caesar0301/awesome-public-datasets	Many datasets offered by individuals
Open data network	https://www.opendatanetwork.com/	Many useful datasets
Government health data	https://www.healthdata.gov/	Healthcare-related datasets
Google public data	https://www.google.com/publicdata/directory	World development indicators

Table 3.1: Potential sources of open data for data science and business analytics

After we go to https://www.data.gov/, we can see the following choices related to Agriculture, Climate, Consumer, Ecosystems, Education, and th...

Table of contents

• Title Page
• Copyright and Credits
• Dedication
• Packt Upsell
• Contributors
• Preface
• 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
• Other Books You May Enjoy