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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Feel confident navigating the fundamentals of data science
Data Science Essentials For Dummies is a quick reference on the core concepts of the exploding and in-demand data science field, which involves data collection and working on dataset cleaning, processing, and visualization. This direct and accessible resource helps you brush up on key topics and is right to the point—eliminating review material, wordy explanations, and fluff—so you get what you need, fast.
- Strengthen your understanding of data science basics
- Review what you've already learned or pick up key skills
- Effectively work with data and provide accessible materials to others
- Jog your memory on the essentials as you work and get clear answers to your questions
Perfect for supplementing classroom learning, reviewing for a certification, or staying knowledgeable on the job, Data Science Essentials For Dummies is a reliable reference that's great to keep on hand as an everyday desk reference.
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Seitenzahl: 252
Veröffentlichungsjahr: 2024
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Data Science Essentials For Dummies®
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Table of Contents
Cover
Title Page
Copyright
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Where to Go from Here
Chapter 1: Wrapping Your Head Around Data Science
Seeing Who Can Make Use of Data Science
Inspecting the Pieces of the Data Science Puzzle
Chapter 2: Tapping into Critical Aspects of Data Engineering
Defining the Three Vs
Identifying Important Data Sources
Grasping the Differences among Data Approaches
Storing and Processing Data for Data Science
Recognizing the Impact of Generative AI
Chapter 3: Using a Machine to Learn from Data
Defining Machine Learning and Its Processes
Considering Learning Styles
Seeing What You Can Do
Chapter 4: Math, Probability, and Statistical Modeling
Exploring Probability and Inferential Statistics
Quantifying Correlation
Reducing Data Dimensionality with Linear Algebra
Modeling Decisions with Multiple Criteria Decision-Making
Introducing Regression Methods
Detecting Outliers
Introducing Time Series Analysis
Chapter 5: Grouping Your Way into Accurate Predictions
Starting with Clustering Basics
Identifying Clusters in Your Data
Categorizing Data with Decision Tree and Random Forest Algorithms
Drawing a Line between Clustering and Classification
Making Sense of Data with Nearest Neighbor Analysis
Classifying Data with Average Nearest Neighbor Algorithms
Classifying with K-Nearest Neighbor Algorithms
Solving Real-World Problems with Nearest Neighbor Algorithms
Chapter 6: Coding Up Data Insights and Decision Engines
Seeing Where Python Fits into Your Data Science Strategy
Using Python for Data Science
Chapter 7: Generating Insights with Software Applications
Choosing the Best Tools for Your Data Science Strategy
Getting a Handle on SQL and Relational Databases
Investing Some Effort into Database Design
Narrowing the Focus with SQL Functions
Making Life Easier with Excel
Chapter 8: Telling Powerful Stories with Data
Data Visualizations: The Big Three
Designing to Meet the Needs of Your Target Audience
Picking the Most Appropriate Design Style
Selecting the Appropriate Data Graphic Type
Testing Data Graphics
Adding Context
Chapter 9: Ten Free or Low-Cost Data Science Libraries and Platforms
Scraping the Web with Beautiful Soup
Wrangling Data with pandas
Visualizing Data with Looker Studio
Machine Learning with scikit-learn
Creating Interactive Dashboards with Streamlit
Doing Geospatial Data Visualization with Kepler.gl
Making Charts with Tableau Public
Doing Web-Based Data Visualization with RAWGraphs
Making Cool Infographics with Infogram
Making Cool Infographics with Canva
Index
About the Author
Connect with Dummies
End User License Agreement
List of Tables
Chapter 5
TABLE 5-1 Business Analyst Stu’s Employee Data
List of Illustrations
Chapter 3
FIGURE 3-1: Unsupervised machine learning breaks down unlabeled data into subgr...
FIGURE 3-2: Neural networks are connected layers of artificial neural units.
FIGURE 3-3: A deep learning network is a neural network with more than one hidd...
Chapter 4
FIGURE 4-1: An example of a linear relationship between months and YouTube subs...
FIGURE 4-2: An example of a nonlinear relationship between watch time and perce...
FIGURE 4-3: Applying SVD to compress a sparse, clean dataset.
FIGURE 4-4: Applying SVD to clean and compress a sparse, dirty dataset.
FIGURE 4-5: You can use SVD to decompose data down to u, S, and V matrices.
FIGURE 4-6: Linear regression used to predict home prices based on the number o...
FIGURE 4-7: Spotting outliers with a Tukey box plot.
FIGURE 4-8: Using PCA to spot outliers.
FIGURE 4-9: A comparison of patterns exhibited by time series.
FIGURE 4-10: An example of an ARMA forecast model.
Chapter 5
FIGURE 5-1: A simple scatterplot.
FIGURE 5-2: A simple scatterplot, showing eyeballed estimations of clustering.
FIGURE 5-3: KDE smoothing of the World Bank’s Income and Education data scatter...
FIGURE 5-4: A schematic layout of a sample dendrogram.
FIGURE 5-5: Using DBScan to detect outliers (in black) within the Iris dataset....
FIGURE 5-6: A decision tree model predicts survival rates from the
Titanic
cata...
FIGURE 5-7: Using the Continent feature to classify World Bank data.
FIGURE 5-8: The distances between the employees’ tuples.
FIGURE 5-9: Finding the average similarity between employees.
FIGURE 5-10: How
k
-NN works.
Chapter 6
FIGURE 6-1: Sample output from Python’s Matplotlib library.
FIGURE 6-2: Time series plot of monthly snow depth data.
Chapter 7
FIGURE 7-1: An example of how SQL is human-readable.
FIGURE 7-2: A relationship between data tables that share a column.
FIGURE 7-3: The full dataset that tracks employee sales performance.
FIGURE 7-4: The sales performance dataset, filtered to show only Abbie’s record...
FIGURE 7-5: Spotting outliers in a tabular dataset with conditional formatting ...
FIGURE 7-6: Spotting outliers in a tabular dataset with color scales.
FIGURE 7-7: Excel XY (scatter) plots provide a simple way to visually detect ou...
FIGURE 7-8: Excel line charts make it easy to visually detect trends in data.
FIGURE 7-9: A long dataset and a wide spreadsheet.
FIGURE 7-10: Creating a wide data table from the long dataset via a PivotTable.
FIGURE 7-11: Using a macro to insert empty cells between values.
Chapter 8
FIGURE 8-1: This design style conveys a calculating and exacting feel.
FIGURE 8-2: This design style is intended to evoke an emotional response.
FIGURE 8-3: Data visualization versus data graphics.
FIGURE 8-4: Types of data graphics, broken down by audience and data visualizat...
FIGURE 8-5: An area chart in three dimensions.
FIGURE 8-6: A bar chart showing the area of U.S. states by their acreage, in th...
FIGURE 8-7: A line chart.
FIGURE 8-8: A pie chart.
FIGURE 8-9: A bubble chart.
FIGURE 8-10: A packed circle diagram.
FIGURE 8-11: A Gantt chart.
FIGURE 8-12: A stacked chart.
FIGURE 8-13: A tree map.
FIGURE 8-14: A simple word cloud.
FIGURE 8-15: A histogram.
FIGURE 8-16: A scatterplot.
FIGURE 8-17: A scatterplot matrix.
FIGURE 8-18: A linear topology.
FIGURE 8-19: A graph mesh network topology.
FIGURE 8-20: A hierarchical tree topology.
FIGURE 8-21: A Cloropleth map.
FIGURE 8-22: A point map.
FIGURE 8-23: A raster surface map.
FIGURE 8-24: Here you see the importance of selecting effective data graphics.
FIGURE 8-25: Using annotation to create context.
FIGURE 8-26: Using graphical elements to create context.
Guide
Cover
Table of Contents
Title Page
Copyright
Begin Reading
Index
About the Author
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Data Science Essentials For Dummies®
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Introduction
This book was written as much for expert data scientists as it was for aspiring ones. Its content represents a new approach to doing data science — one that puts business vision and profitably at the heart of our work as data scientists.
Data science and artificial intelligence (AI) have disrupted the business world so radically that it’s nearly unrecognizable compared to what things were like just 10 or 15 years ago. The good news is that most of these changes have made everyone’s lives and businesses more efficient, more fun, and dramatically more interesting. The bad news is that if you don’t yet have at least a modicum of data science competence, your business and employment prospects are growing dimmer by the moment.
Since 2014, when this book was first written (throughout the first several editions), I’ve harped on this same point. Lots of people listened! So much has changed about data science over the years, however, that this book has needed two full rewrites since it was originally published. What changed? The math and scientific approach that underlie data science haven’t changed one bit. But over the years, with all the expansion of AI adoption across business and with the remarkable increase in the supply of data science workers, the data science landscape has seen a hundredfold increase in diversity with respect to what people and businesses are using data science to achieve.
The original idea behind this book when it was first published was to provide “a reference manual to guide you through the vast and expansive areas encompassed by data science.” At the time, not too much information out there covered the breadth of data science in one resource. That’s changed!
Data scientist as a title only really began to emerge in 2012. Most of us practitioners in the field back then were all new and still finding our way. In 2014, I didn’t have the perspective or confidence I needed to write a book like the one you’re reading now. Thank you so much to all the readers who have read this book previously, shared positive feedback, and applied what they learned to create better lives for themselves and better outcomes for their companies. The positive transformation of my readers is a big part of what keeps me digging deep to produce the very best version of this book that I possibly can.
The internet is full of information for the sake of information — information that lacks the depth, context, and relevance that are needed to transform that information to true meaning in the lives of its consumers. Publishing more of this type of content doesn’t help people — it confuses them, overwhelms them, and wastes their precious time! When writing this book for a third time, I took a radical stance against “information for the sake of information.”
I also want to make three further promises about the content in this book: It’s meaningful, it’s actionable, and it’s relevant. If it isn’t one of these three adjectives, I’ve made sure it hasn’t made its way into this book.
In this book, I detail what data science actually is and what its theoretical underpinnings are. You’ll find references to ancillary materials that directly support what you’re learning within these pages. All these support materials are hosted on the companion website for this book: https://businessgrowth.ai. I highly recommend you take advantage of those assets — I’ve donated many of them from my archived bank of limited-edition paid products.
Note: If you want me to show you how to implement the data science that’s discussed in this book, I have two Python for Data Science Essential Training courses on LinkedIn Learning. You’re most welcome to follow up by taking those courses. You can access them both directly through my course author page on LinkedIn Learning: www.linkedin.com/learning/instructors/lillian-pierson-p-e.
About This Book
In keeping with the For Dummies brand, this book is organized in a modular, easy-to-access format that allows you to use the book as an owner’s manual. The book’s chapters are structured to walk you through a clear process, so reading them in order may make the most sense. You don’t absolutely have to read the book from cover to cover, however — you can glean a great deal from jumping around, although now and then you may miss some important context by doing so.
Within this book, you may note that some web addresses break across two lines of text. If you’re reading this book in print and want to visit one of these web pages, simply key in the web address exactly as it’s noted in the text, pretending as though the line break doesn’t exist. If you’re reading this as an e-book, you’ve got it easy — just click the web address to be taken directly to the web page.
Foolish Assumptions
In writing this book, I’ve assumed that you’re comfortable with advanced tasks in Microsoft Excel — pivot tables, grouping, sorting, plotting, and the like. Having strong skills in algebra, basic statistics, or even business calculus helps as well. Foolish or not, it’s my high hope that all readers have subject matter expertise to which they can apply the skills presented in this book. Because data scientists need to know the implications and applications of the data insights they derive, subject matter expertise is a major requirement for data science.
Icons Used in This Book
As you make your way through this book, you see the following icons in the margins:
The Tip icon marks tips (duh!) and shortcuts you can use to make subject mastery easier.
The Remember icon marks information that’s especially important to know. To siphon off the most important information in each chapter, just skim the material next to these icons.
The Warning icon tells you to watch out! It marks important information that may save you headaches.
Where to Go from Here
If you’re new to data science, you’re best off starting from Chapter 1 and reading the book from beginning to end. If you already know the data science basics, I suggest that you read the last part of Chapter 1, skim Chapter 2, and then dig deep into the rest of the book.
This book is unlike any other data science book or course on the market. How do I know? Because I created it from scratch based on my own unique experience and perspective. That perspective is based on nearly 20 years of consulting experience within the data, technology, and engineering domains. This book is not a remake of what some other expert wrote in their book — it’s an original work of art and a labor of love for me. If you enjoy the contents of this book, please reach out to me at [email protected] and let me know.
Helping readers like you is my mission in life!
Chapter 1
Wrapping Your Head Around Data Science
IN THIS CHAPTER
Deploying data science methods across various industries
Piecing together the core data science components
Identifying viable data science solutions to business challenges
Exploring data science career alternatives
For over a decade now, everyone has been absolutely deluged by data. It’s coming from every computer, every mobile device, every camera, and every imaginable sensor — and now it’s even coming from watches and other wearable technologies. Data is generated in every social media interaction we humans make, every file we save, every picture we take, and every query we submit; data is even generated when we do something as simple as ask a favorite search engine for directions to the closest ice cream shop.
If you’re anything like I was, you may have wondered, “What’s the point of all this data? Why use valuable resources to generate and collect it?” Although even just two decades ago, no one was in a position to make much use of most of the data that’s generated, the tides today have definitely turned. Specialists known as data engineers are constantly finding innovative and powerful new ways to capture, collate, and condense unimaginably massive volumes of data. Other specialists known as data scientists are leading change by deriving valuable and actionable insights from that data.
In its truest form, data science represents the optimization of processes and resources. Data science produces data insights — actionable, data-informed conclusions or predictions that you can use to understand and improve your business, your investments, your health, and even your lifestyle and social life. Using data science insights is like being able to see in the dark. For any goal or pursuit you can imagine, you can find data science methods to help you predict the most direct route from where you are to where you want to be — and to anticipate every pothole in the road between both places.
In this chapter, I explain the difference between data science and data engineering.
Seeing Who Can Make Use of Data Science
The terms data science and data engineering are often misused and confused, so let me start off by clarifying that these two fields are, in fact, separate and distinct domains of expertise. Data science is the computational science of extracting meaningful insights from raw data and then effectively communicating those insights to generate value. Data engineering, on the other hand, is an engineering domain that’s dedicated to building and maintaining systems that overcome data processing bottlenecks and data handling problems for applications that consume, process, and store large volumes, varieties, and velocities of data.
In both data science and data engineering, you commonly work with the following types of data:
Structured data:
Data that is stored, processed, and manipulated in a traditional
relational database management system
(RDBMS). An example of this type of data can be seen in the tabular schema of rows and columns you’d commonly encounter when working with corporate databases.
Unstructured data:
Data that is commonly generated from human activities and doesn’t fit into a structured database format. Examples of unstructured data are data that comprises email documents, Microsoft Word documents or audio or video files.
Semistructured data:
Data that doesn’t fit into a structured database system but is nonetheless organizable by tags that are useful for creating a form of order and hierarchy in the data. XML and JSON files are examples of data that comes in semistructured form.
In the past, only large tech companies with massive funding had the skills and computing resources required to implement data science methodologies to optimize and improve their business, but that hasn’t been the case for quite a while now. The proliferation of data has created a demand for insights, and this demand is embedded in many aspects of modern culture — from the Uber passenger who expects the driver to show up exactly at the time and location predicted by the Uber app to the online shopper who expects the Amazon platform to recommend the best product alternatives for comparing similar goods before making a purchase. Data and the need for data-informed insights are ubiquitous. Because organizations of all sizes are beginning to recognize that they’re immersed in a sink-or-swim, data-driven, competitive environment, data know-how has emerged as a core and requisite function in almost every line of business.
What does this mean for the average knowledge worker? It means that everyday employees are increasingly expected to support a progressively advancing set of technological and data requirements. Why? Because almost all industries are reliant on data technologies and the insights they spur. Consequently, many people are in continuous need of upgrading their data skills, or else they face the real possibility of being replaced by a more data-savvy employee.
The good news is that upgrading data skills doesn’t usually require people to go back to college or earn a university degree in statistics, computer science, or data science. The bad news is that, even with professional training or self-teaching, it always takes extra work to stay industry-relevant and tech-savvy. In this respect, the data revolution isn’t so different from any other change that has hit industry in the past. The fact is, in order to stay relevant, you need to take the time and effort to acquire the skills that keep you current. When you’re learning how to do data science, you can take some courses, educate yourself using online resources, read books like this one, and attend events where you can learn what you need to know to stay on top of the game.
Who can use data science? You can. Your organization can. Your employer can. Anyone who has a bit of understanding and training can begin using data insights to improve their lives, their careers, and the well-being of their businesses. Data science represents a change in the way you approach the world. When determining outcomes, people once used to make their best guess, act on that guess, and then hope for the desired result. With data insights, however, people now have access to the predictive vision that they need to truly drive change and achieve the results they want.
Here are some examples of ways you can use data insights to make the world, and your company, a better place:
Develop key performance indicators (KPIs) for your business systems.
Use KPIs to track performance and optimize the return on investment (ROI) for measurable business activities.
Develop your marketing strategy.
Use data insights and predictive analytics to identify marketing strategies that work, eliminate underperforming efforts, and test new marketing strategies.
Keep communities safe.
Predictive policing applications help law enforcement personnel predict and prevent local criminal activities.
Help make the world a better place for those less fortunate.
Data scientists in developing nations are using social data, mobile data, and data from websites to generate real-time analytics that improve the effectiveness of humanitarian responses to disasters, epidemics, food scarcity issues, and more.
Inspecting the Pieces of the Data Science Puzzle
To practice data science, in the true meaning of the term, you need the analytical know-how of math and statistics, the coding skills necessary to work with data, and an area of subject matter expertise. Without this expertise, you may as well call yourself a mathematician or a statistician. Similarly, a programmer without subject matter expertise and analytical know-how may better be considered a software engineer or developer, but not a data scientist.
The need for data-informed business and product strategy has been increasing exponentially for about a decade now, forcing all business sectors and industries to adopt a data science approach. As such, different flavors of data science have emerged. The following are just a few titles under which experts of every discipline are required to know and regularly do data science:
Clinical biostatistician
Data and tech policy analyst
Data scientist–geospatial and agriculture analyst
Data scientist–health care
Digital banking product owner
Director of data science–advertising technology
Geotechnical data scientist
Global channel ops–data excellence lead
Nowadays, it’s almost impossible to differentiate between a proper data scientist and a subject matter expert (SME) whose success depends heavily on their ability to use data science to generate insights. Looking at a person’s job title may or may not be helpful, simply because many roles are titled data scientist when they may as well be labeled data strategist or product manager, based on the actual requirements. In addition, many knowledge workers are doing daily data science and not working under the title of data scientist. It’s an overhyped, often misleading label that’s not always helpful if you’re trying to find out what a data scientist does by looking at online job boards.
To shed some light, in the following sections I spell out the key components that are part of any data science role, regardless of whether that role is assigned the data scientist label.
Collecting, querying, and consuming data
Data engineers have the job of capturing and collating large volumes of structured, unstructured, and semistructured big data (an outdated term that’s used to describe data that exceeds the processing capacity of conventional database systems because it’s too big, it moves too fast, or it lacks the structural requirements of traditional database architectures).
Data engineering tasks are separate from the work that’s performed in data science, which focuses more on analysis, prediction, and visualization. Despite this distinction, whenever data scientists collect, query, and consume data during the analysis process, they perform work similar to that of the data engineer (the role I tell you about earlier in this chapter).
Although valuable insights can be generated from a single data source, often the combination of several relevant sources delivers the contextual information required to drive better data-informed decisions. A data scientist can work from several datasets that are stored in a single database, or even in several different data storage environments. At other times, source data is stored and processed on a cloud-based platform built by software and data engineers.
No matter how the data is combined or where it’s stored, if you’re a data scientist, you almost always have to query data — in other words, write commands to extract relevant datasets from data storage systems. Most of the time, you use Structured Query Language (SQL) to query data. (Chapter 7 is all about SQL, so if the acronym scares you, jump ahead to that chapter now.)
Whether you’re using a third-party application or doing custom analyses by using a programming language such as R or Python, you can choose from a number of universally accepted file formats:
Comma-separated values (CSV):
Almost every brand of desktop and web-based analysis application accepts this file type, as do commonly used scripting languages such as Python and R.
Script:
Most data scientists know how to use Python to analyze and visualize data. These script files end with the extension
.ply
or
.ipynb
(Python).
Application:
Excel is useful for quick-and-easy, spot-check analyses on small- to medium-size datasets. These application files have the
.xls
or
.xlsx
extension.
Web programming:
If you're building custom, web-based data visualizations, you may be working in D3.js — or data-driven documents, a JavaScript library for data visualization. When you work in D3.js, you use data to manipulate web-based documents using
.html
,
.svg
, and
.css
files.
