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Published:
September 15, 2021

Data Science For Dummies

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.

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About The Author

Lillian Pierson is the CEO of Data-Mania, where she supports data professionals in transforming into world-class leaders and entrepreneurs. She has trained well over one million individuals on the topics of AI and data science. Lillian has assisted global leaders in IT, government, media organizations, and nonprofits.

Sample Chapters

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.

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Open data is part of a larger trend toward a less restrictive, more open understanding of the idea of intellectual property, a trend that's been gaining tremendous popularity over the past decade. Open data is data that has been made publicly available and is permitted to be used, reused, built on, and shared with others.
The Washington Post story "The Black Budget" is an incredible example of data science in journalism. When former NSA contractor Edward Snowden leaked a trove of classified documents, he unleashed a storm of controversy not only among the public but also among the data journalists who were tasked with analyzing the documents for stories.
Coding is one of the primary skills in a data scientist's toolbox. Some incredibly powerful applications have successfully done away with the need to code in some data-science contexts, but you're never going to be able to use those applications for custom analysis and visualization. For advanced tasks, you're going to have to code things up for yourself, using either the Python programming language or the R programming language.
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.
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.
When most people think of data, questions about who (as it relates to the data) don't readily come to mind. In data journalism, however, answers to questions about who are profoundly important to the success of any data-driven story. You must consider who created and maintains the sources of your datasets to determine whether those datasets are a credible basis for a story.
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.
"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.
Although environmental intelligence (EI) and business intelligence (BI) technologies have a lot in common, EI is still considered applied data science. Consider the following ways in which EI and BI are similar: Simple inference from mathematical models: BI generates predictions based on simple mathematical inference, and not from complex statistical predictive modeling.
You can incorporate descriptive spatial statistics into crime analysis in order to produce analytics you can then use to understand and monitor the location-based attributes of ongoing criminal activities. You can use descriptive spatial statistics to provide your law enforcement agency with up-to-date information on the location, intensity, and size of criminal activity hot spots, as well as to derive important information about the characteristics of local areas that are positioned between these hot spots.
In growth, you use testing methods to optimize your web design and messaging so that it performs at its absolute best with the audiences to which it's targeted. Although testing and web analytics methods are both intended to optimize performance, testing goes one layer deeper than web analytics. You use web analytics to get a general idea about the interests of your channel audiences and how well your marketing efforts are paying off over time.
Web analytics can be described as the practice of generating, collecting, and making sense of Internet data in order to optimize web design and strategy. Configure web analytics applications to monitor and track absolutely all your growth tactics and strategies, because without this information, you're operating in the dark — and nothing grows in the dark.
You can use data science to model natural resources in their raw form. This type of environmental data science generally involves some advanced statistical modeling to better understand natural resources. You model the resources in the raw — water, air, and land conditions as they occur in nature — to better understand the natural environment's organic effects on human life.
If statistics has been described as the science of deriving insights from data, then what’s the difference between a statistician and a data scientist? Good question! While many tasks in data science require a fair bit of statistical know how, the scope and breadth of a data scientist’s knowledge and skill base is distinct from those of a statistician.
Because environmental intelligence (EI) is a social-good application of data science, there aren't a ton of funding sources out there, which is probably the chief reason not many people are working in this line of data science. EI is small, but some folks in dedicated organizations have found a way to earn a living by creating EI solutions that serve the public good.
Elva is a shining example of how environmental intelligence technologies can be used to make a positive impact. This free, open-source platform facilitates cause mapping and data visualization reporting for election monitoring, human rights violations, environmental degradation, and disaster risk in developing nations.
Data science in e-commerce serves the same purpose that it does in any other discipline — to derive valuable insights from raw data. In e-commerce, you're looking for data insights that you can use to optimize a brand's marketing return on investment (ROI) and to drive growth in every layer of the sales funnel.
Modeling the travel demand of criminal activity allows you to describe and predict the travel patterns of criminals so that law enforcement can use this information in tactical response planning. If you want to predict the most likely routes that criminals will take between the locations from where they start out and the locations where they actually commit the crimes, use crime travel modeling.
You can incorporate predictive statistical models into crime analysis methods to produce analytics that describe and predict where and what kinds of criminal activity are likely to occur.Predictive spatial models can help you predict the behavior, location, or criminal activities of repeat offenders. You can also apply statistical methods to spatio-temporal data to ascertain causative or correlative variables relevant to crime and law enforcement.
Although data science for crime analysis has a promising future, it's not without its limitations. The field is still young, and it has a long way to go before the bugs are worked out. Currently, the approach is subject to significant criticism for both legal and technical reasons. Caving in on civil rights The legal systems of western nations such as the United States are fundamentally structured around the basic notion that people have the right to life, liberty, and the pursuit of property.
Traditionally, big data is the term for data that has incredible volume, velocity, and variety. Traditional database technologies aren't capable of handling big data — more innovative data-engineered solutions are required. To evaluate your project for whether it qualifies as a big data project, consider the following criteria: Volume: Between 1 terabytes/year and10 petabytes/year Velocity: Between 30 kilobytes/second and 30 gigabytes/second Variety: Combined sources of unstructured, semi-structured, and structured data Data science and data engineering are not the same Hiring managers tend to confuse the roles of data scientist and data engineer.
The purpose of segmenting your channels and audiences is so that you can exact-target your messaging and offerings for optimal conversions, according to the specific interests and preferences of each user segment.If your goal is to optimize your marketing return on investment by exact-targeting customized messages to entire swathes of your audience at one time, you can use segmentation analysis to group together audience members by shared attributes and then customize your messaging to those target audiences on a group-by-group basis.
You can use GIS technologies, data modeling, and advanced spatial statistics to build information products for the prediction and monitoring of criminal activity. Spatial data is tabular data that's earmarked with spatial coordinate information for each record in the dataset.Many times, spatial datasets also have a field that indicates a date/time attribute for each of the records in the set — making it spatio-temporal data.
The temporal analysis of crime data produces analytics that describe patterns in criminal activity based on time. You can analyze temporal crime data to develop prescriptive analytics, either through traditional crime analysis means or through a data science approach. Knowing how to produce prescriptive analytics from temporal crime data allows you to provide decision-support to law enforcement agencies that want to optimize their tactical crime fighting.
Before getting into the nitty-gritty of how you can begin using web analytics, testing tactics, and segmentation and targeting initiatives to ignite growth in all layers of your e-commerce sales funnel, you first need to understand the fundamental structure and function of each layer in a sales funnel.In keeping with a logical and systematic approach, the e-commerce sales funnel can be broken down into the following five stages: acquisition, activation, retention, referral, and revenue.
The what, in data journalism, refers to the gist of the story. In all forms of journalism, a journalist absolutely must be able to get straight to the point. Keep it clear, concise, and easy to understand.When crafting data visualizations to accompany your data journalism piece, make sure that the visual story is easy to discern at a moment's glance.
As the old adage goes, timing is everything. It's a valuable skill to know how to refurbish old data so that it's interesting to a modern readership. Likewise, in data journalism, it's imperative to keep an eye on contextual relevancy and know when is the optimal time to craft and publish a particular story. When as the context to your story If you want to craft a data journalism piece that really garners a lot of respect and attention from your target audience, consider when — over what time period — your data is relevant.
Data and stories are always more relevant to some places than others. From where is a story derived, and where is it going? If you keep these important facts in mind, the publications you develop are more relevant to their intended audience. The where aspect in data journalism is a bit ambiguous because it can refer to a geographical location or a digital location, or both.
By their very nature, environmental variables are location-dependent: They change with changes in geospatial location. The purpose of modeling environmental variables with spatial statistics is to enable accurate spatial predictions so that you can use those predictions to solve problems related to the environment.
All of the information and insight in the world is useless if it can't be communicated. If data scientists cannot clearly communicate their findings to others, potentially valuable data insights may remain unexploited. Following clear and specific best practices in data visualization design can help you develop visualizations that communicate in a way that's highly relevant and valuable to the stakeholders for whom you're working.
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