Data Science For Dummies book cover

Data Science For Dummies

Author:
Lillian Pierson
Published: September 15, 2021

Overview

Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help

What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is.

Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects.

Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book.

Data Science For Dummies demonstrates:

  • The only process you’ll ever need to lead profitable data science projects
  • Secret, reverse-engineered data monetization tactics that no one’s talking about
  • The shocking truth about how simple natural language processing can be
  • How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise 

Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today.

Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help

What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is.

Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects.

Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science

expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book.

Data Science For Dummies demonstrates:

  • The only process you’ll ever need to lead profitable data science projects
  • Secret, reverse-engineered data monetization tactics that no one’s talking about
  • The shocking truth about how simple natural language processing can be
  • How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise 

Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today.

Data Science For Dummies Cheat Sheet

"Data science" is the big buzzword these days, and most folks who have come across the term realize that data science is a powerful force that is in the process of revolutionizing scores of major industries. Not many folks, however, are aware of the range of tools currently available that are designed to help big businesses and small take advantage of the data science revolution. Take a peek at these tools and see how they fit in to the broader context of data science.

Articles From The Book

29 results

General Macs Articles

Data Journalism: Collecting Data for Your Story

A data-journalism piece is only as good as the data that supports it. To publish a compelling story, you must find compelling data on which to build. That isn't always easy, but it's easier if you know how to use scraping and autofeeds to your advantage.

Scraping data

Web-scraping involves setting up automated programs to scour and extract the exact and custom datasets that you need straight from the Internet so you don't have to do it yourself. The data you generate from this process is commonly called scraped data. Most data journalists scrape source data for their stories because it's the most efficient way to get datasets for unique stories. Datasets that are easily accessible have usually already been exploited and mined by teams of data journalists who were looking for stories. To generate unique data sources for your data-driven story, scrape the data yourself.

If you find easy-to-access data, beware that most of the stories in that dataset have probably been told by a journalist who discovered that data before you.

To illustrate how you'd use data scraping in data journalism, imagine the following example: You're a data journalist living in a U.S. state that directly borders Mexico. You've heard rumors that the local library's selection of Spanish-language children's books is woefully inadequate. You call the library, but its staff fear negative publicity and won't share any statistics with you about the topic. Because the library won't budge on its data-sharing, you're forced to scrape the library's online catalog to get the source data you need to support this story. Your scraping tool is customized to iterate over all possible searches and keep track of the results. After scraping the site, you discover that 25 percent of children's books at the library are Spanish-language books. Spanish-speakers make up 45 percent of the primary-school population; is this difference significant enough to form the basis of a story? Maybe, maybe not. To dig a little deeper and possibly discover a reason behind this difference, you decide to scrape the catalog once a week for several weeks, and then compare patterns of borrowing. When you find that a larger proportion of Spanish books are being checked out, this indicates that there is, indeed, a high demand for children's books in Spanish. This finding, coupled with the results from your previous site scrape, give you all the support you need to craft a compelling article around the issue.

Setting up data alerts

To generate hot stories, data journalists must have access to the freshest, newest data releases that are coming from the most credible organizations. To stay on top of what datasets are being released where, data journalists subscribe to alert systems that send them notifications every time potentially important data is released. These alert systems often issue notifications via RSS feeds or via email. It's also possible to set up a custom application like
DataStringer to send push notifications when significant modifications or updates are made to source databases. After you subscribe to data alerts and form a solid idea about the data-release schedule, you can begin planning for data releases in advance. For example, if you're doing data journalism in the business analytics niche and know that a particularly interesting quarterly report is to be released in one week, you can use the time you have before its release to formulate a plan on how you'll analyze the data when it does become available.

Many times, after you're alerted to important new data releases, you still need to scrape the source site in order to get that data. In particular, if you're pulling data from a government department, you're likely to need to scrape the source site. Although most government organizations in western countries are legally obligated to release data, they aren't required to release it in a format that's readily consumable. Don't expect them to make it easy for you to get the data you need to tell a story about their operations.

General Macs Articles

Data Journalism: How to Develop, Tell, and Present the Story

By thinking through the how of a story, you are putting yourself in position to craft better data-driven stories. Looking at your data objectively and considering factors like how it was created helps you to discover interesting insights that you can include in your story. Also, knowing how to quickly find stories in potential data sources helps you to quickly sift through the staggering array of options. And, how you present your data-driven story determines much about how well that story is received by your target audience. You could have done everything right — really taken the time to get to know who your audience is, boiled your story down so that it says exactly what you intend, published it at just the right time, crafted your story around what you know about why people care, and even published it to just the right venue — but if your data visualization looks bad, or if your story layout makes it difficult for readers to quickly gather useful information, then your positive response rates are likely to be low.

Integrating how as a source of data and story context

You need to think about how your data was generated because that line of thinking often leads you into more interesting and compelling storylines. Before drawing up a final outline for your story, brainstorm about how your source data was generated. If you find startling or attention-grabbing answers that are relevant to your story, consider introducing those in your writing or data visualization.

Finding stories in your data

If you know how to quickly and skillfully find stories in datasets, you can use this set of skills to save time when you're exploring the array of stories that your datasets offer. If you want to quickly analyze, understand, and evaluate the stories in datasets, then you need to have solid data analysis and visualization skills. With these skills, you can quickly discover which datasets to keep and which to discard. Getting up to speed in relevant data science skills also helps you quickly find the most interesting, relevant stories in the datasets you select to support your story.

Presenting a data-driven story

How you present your data-driven story determines much about whether it succeeds or fails with your target audience. Should you use an infographic? A chart? A map? Should your visualization be static or interactive? You have to consider countless aspects when deciding how to best present your story.

General Macs Articles

Data Journalism: Why the Story Matters

The human capacity to question and understand why things are the way they are is a clear delineation point between the human species and other highly cognitive mammals. Answers to questions about why help you to make better-informed decisions. These answers help you to better structure the world around you and help you develop reasoning beyond what you need for mere survival. In data journalism, as in all other types of business, answers to the question why help you predict how people and markets respond. These answers help you know how to proceed to achieve an outcome of most probable success. Knowing why your story matters helps you write and present it in a way that achieves the most favorable outcomes — presumably, that your readers enjoy and take tremendous value from consuming your content.

Asking why in order to generate and augment a storyline

No matter what topic you're crafting a story around, it's incredibly important to generate a storyline around the wants and needs of your target audience. After you know who your audience is and what needs they most often try to satisfy by consuming content, use that knowledge to help you craft your storyline. If you want to write a story and design a visualization that precisely targets the needs and wants of your readership, take the time to pinpoint why people would be interested in your story, and create a story that directly meets that desire in as many ways as possible.

Why your audience should care

People care about things that matter to them and that affect their lives. Generally, people want to feel happy and safe. They want to have fulfilling relationships. They want to have good status among their peers. People like to learn things, particularly things that help them earn more money. People like possessions and things that bring them comfort, status, and security. People like to feel good about themselves and what they do. This is all part of human nature. The desires just described summarize why people care about anything — from the readers of your story to the person down the street. People care because it does something for them, it fills one of their core desires. Consequently, if your goal is to publish a high-performing, well-received data journalism piece, make sure to craft it in a way that fulfills one or two core desires of your target readership.