Teach Yourself Data Analytics in 30 Days
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Teach Yourself Data Analytics in 30 Days

Learn to use Python and Jupyter Notebooks by exploring fun, real-world data projects

About the Book

Would you like to learn how to extract useful insights from all the data around you without having to take years' worth of courses? College data science programs teach valuable skills, but sometimes all you need is access to some quick and direct tools. Welcome to Teach Yourself Data Analytics in 30 Days.

The book's curriculum is organized by eight data "stories." The stories are interesting on their own. But there's no doubt what they're really all about is Python data analytics. Each story/chapter contains all the information you would need to go out and get the raw data and then write the Python analytics code necessary to solve a specific problem. Or, in other words, each chapter teaches new Python skills.

If you're motivated and have a bit of available time, there's no reason you can't use those stories to teach yourself data analytics in 30 days. You'll find everything you need to build your own basic data analytics skills, including:

* Getting Python up and running on Jupyter Notebooks (or JupyterLab, if you prefer)

* Finding and cleaning data sources

* Plotting your data

* Understanding results through domain knowledge and tools like regression lines

* Python functions

About the Author

David Clinton
David Clinton

David Clinton has written about and published training material (including books with Manning and Wiley/Sybex and video courses with Pluralsight) for many cutting edge technical subjects including Linux systems, Cloud Computing (in particular Amazon's AWS), container technologies like LXD and Docker, and DevOps tools. Besides the technology books and guides he's written and edited, David is experienced at writing white papers and complex technical documentation, and creating, administering, and overhauling large document stores.

Table of Contents

  • Getting Started with Data Analytics and Python
    • Programming with Python
    • Installing Python
    • Virtual Python environments
    • Working with Python modules
    • Using Jupyter Notebooks
    • Installing Jupyter Notebooks
    • Storing and protecting data
    • Getting help
  • Comparing Wages With Consumer Price Index Data
    • Working With the API
    • Working with CPI and salary data
    • Merge and plot our dataframes
  • Wages and CPI: Reality Check
    • Confirm the integrity of your data
    • Establish a historical context for your data
    • Incorporate S&P 500 quotes
    • Warning: Gotchas ahead!
    • Calculate rate of increase for both CPI and S&P
  • Working With Major US Storm Data
    • What Is a Major Storm?
    • Where Does Major Storm Data Come From?
    • What Does the Historical Record Show?
    • US Hurricane Data: 1851-2019
    • US Tropical Storm Data: 1851-1965, 1983-2019
  • Property Rights and Economic Development
    • How Do You Measure Conditions and Outcomes?
    • Getting the Data
    • Visualizing Data Sets Using Scatter Plots
    • Adding “Hover” Visibility
    • Adding a Regression Line
    • Other Considerations
  • How Representative Is Your Government?
    • Scraping data from a single vote
    • Automating vote tabulation
    • What did we learn?
    • Could government be more representative?
  • Does Wealth Influence the Prevalence of Mental Illness?
    • My assumptions
    • Plotting the data
    • Thinking about our data
  • Do Birthdays Make Elite Athletes?
    • Where Does the NHL Hide Its Data?
    • How to Use Python to Scrape NHL Statistics
    • How to Visualize the Raw Data
    • What Do Histograms Really Tell Us?
    • Make Sure to Use the Right Tools For the Job
  • Do Gun Control Laws Impact Gun Violence?
    • Quantifying International Gun Laws
    • Importing Gun Violence Data
    • Plotting International Gun Violence Data
    • US Gun Laws and Gun Violence
    • What’s next?

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