• forward arrow
    Main Menu

  • Book & Article Categories

  • forward arrow
    Main Menu

  • Book & Article Categories

Home
Technology Books
Information Technology Books
Data Science Books
General Data Science Books
https://resources.learning.wiley.com/isbn/9781394424092
Lillian Pierson
|
Published:
August 31, 2026

Data Science For Dummies

Buy Now
Subscribe on Perlego
Engage with Book

Overview

Understand the foundations of data science and modern AI tools with this fully updated beginner's guide.

Whether you're a business analyst who's never coded, a manager who’s making data-driven decisions, or a career changer eyeing one of the fastest-growing professions, data science skills are vital to success. In the fourth edition of Data Science For Dummies, Lillian Pierson, a fractional CMO and GTM engineer who has trained over two million learners, delivers a hands-on introduction to the key concepts and tools such as AI-enabled shortcuts that make advanced analytics accessible to everyone.

This updated edition covers generative AI, prompt engineering, and predictive analytics right alongside evergreen fundamentals like Python programming, statistics, and data wrangling. Brand-new chapters explain practical applications using ChatGPT and Claude, while expanded no-code workflows make AI-powered analysis accessible to readers who have never written a single line of code.

Data Science For Dummies walks you through:

  • Foundational skills in Python, statistics, and machine learning for beginners and career changers
  • Hands-on generative AI coverage with practical demos using ChatGPT and Claude
  • No-code workflows that show non-programmers how to perform AI-powered data analysis tasks
  • Updated guidance on ethical AI practices, data regulations, and responsible compliance
  • Career-focused advice on emerging roles, portfolio building, and industry preparation

Perfect for professionals, students, career changers, and business leaders looking for an accessible launching pad into an exciting and rewarding discipline, Data Science For Dummies gives you the foundational skills and modern AI tools you need to extract insights, create visualizations, and make smarter, data-driven decisions.

Read More

About The Author

Lillian Pierson, PE, is an AI-native growth strategist, advisor, and fractional CMO who helps B2B technology companies modernize their go-to-market strategies and systems. A licensed professional engineer, AI author, and educator, she combines deep technical expertise with growth leadership to help companies scale demand, strengthen competitive positioning, and drive revenue growth. She has written more than a dozen books on AI and data science, supported 10% of Fortune 100 companies, and educated more than two million learners worldwide.

data science for dummies

CHEAT SHEET

Doing data science with AI tools means knowing which tool to grab, which prompt to use, and how far you can trust an AI answer before you take any actions. Use the tables, templates, and checklists in this cheat sheet to move faster on everyday analytics work — from cleaning a messy CSV to picking the right chart for the message you want to communicate.
View Cheat Sheet
View Cheat Sheet

HAVE THIS BOOK?

View Book Companion Site
Access practice tests
Additional Book Resources

Articles from
the book

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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
Doing data science with AI tools means knowing which tool to grab, which prompt to use, and how far you can trust an AI answer before you take any actions. Use the tables, templates, and checklists in this cheat sheet to move faster on everyday analytics work — from cleaning a messy CSV to picking the right chart for the message you want to communicate.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
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.
Read More
Read More
Load More Articles

Related Books

0:00
0:00
Listen now
Pause
https://cdn.prod.website-files.com/6630d85d73068bc09c7c436c/69195ee32d5c606051d9f433_4.%20All%20For%20You.mp3

Frequently Asked Questions

No items found.

Get a Subscription