Data Science For Dummies

Title Page
Copyright Page
Table of Contents
Introduction
	About This Book
	Foolish Assumptions
	Icons Used in This Book
	Beyond the Book
	Where to Go from Here
Part 1 Getting Started with Data Science
	Chapter 1 Wrapping Your Head Around Data Science
		Seeing Who Can Make Use of Data Science
		Inspecting the Pieces of the Data Science Puzzle
			Collecting, querying, and consuming data
			Applying mathematical modeling to data science tasks
			Deriving insights from statistical methods
			Coding, coding, coding — it’s just part of the game
			Applying data science to a subject area
			Communicating data insights
		Exploring Career Alternatives That Involve Data Science
			The data implementer
			The data leader
			The data entrepreneur
	Chapter 2 Tapping into Critical Aspects of Data Engineering
		Defining Big Data and the Three Vs
			Grappling with data volume
			Handling data velocity
			Dealing with data variety
		Identifying Important Data Sources
		Grasping the Differences among Data Approaches
			Defining data science
			Defining machine learning engineering
			Defining data engineering
			Comparing machine learning engineers, data scientists, and data engineers
		Storing and Processing Data for Data Science
			Storing data and doing data science directly in the cloud
			Storing big data on-premise
			Processing big data in real-time
Part 2 Using Data Science to Extract Meaning from Your Data
	Chapter 3 Machine Learning Means . . . Using a Machine to Learn from Data
		Defining Machine Learning and Its Processes
			Walking through the steps of the machine learning process
			Becoming familiar with machine learning terms
		Considering Learning Styles
			Learning with supervised algorithms
			Learning with unsupervised algorithms
			Learning with reinforcement
		Seeing What You Can Do
			Selecting algorithms based on function
			Using Spark to generate real-time big data analytics
	Chapter 4 Math, Probability, and Statistical Modeling
		Exploring Probability and Inferential Statistics
			Probability distributions
			Conditional probability with Naïve Bayes
		Quantifying Correlation
			Calculating correlation with Pearson’s r
			Ranking variable-pairs using Spearman’s rank correlation
		Reducing Data Dimensionality with Linear Algebra
			Decomposing data to reduce dimensionality
			Reducing dimensionality with factor analysis
			Decreasing dimensionality and removing outliers with PCA
		Modeling Decisions with Multiple Criteria Decision-Making
			Turning to traditional MCDM
			Focusing on fuzzy MCDM
		Introducing Regression Methods
			Linear regression
			Logistic regression
			Ordinary least squares (OLS) regression methods
		Detecting Outliers
			Analyzing extreme values
			Detecting outliers with univariate analysis
			Detecting outliers with multivariate analysis
		Introducing Time Series Analysis
			Identifying patterns in time series
			Modeling univariate time series data
	Chapter 5 Grouping Your Way into Accurate Predictions
		Starting with Clustering Basics
			Getting to know clustering algorithms
			Examining clustering similarity metrics
		Identifying Clusters in Your Data
			Clustering with the k-means algorithm
			Estimating clusters with kernel density estimation (KDE)
			Clustering with hierarchical algorithms
			Dabbling in the DBScan neighborhood
		Categorizing Data with Decision Tree and Random Forest Algorithms
		Drawing a Line between Clustering and Classification
			Introducing instance-based learning classifiers
			Getting to know classification algorithms
		Making Sense of Data with Nearest Neighbor Analysis
		Classifying Data with Average Nearest Neighbor Algorithms
		Classifying with K-Nearest Neighbor Algorithms
			Understanding how the k-nearest neighbor algorithm works
			Knowing when to use the k-nearest neighbor algorithm
			Exploring common applications of k-nearest neighbor algorithms
		Solving Real-World Problems with Nearest Neighbor Algorithms
			Seeing k-nearest neighbor algorithms in action
			Seeing average nearest neighbor algorithms in action
	Chapter 6 Coding Up Data Insights and Decision Engines
		Seeing Where Python and R Fit into Your Data Science Strategy
		Using Python for Data Science
			Sorting out the various Python data types
			Putting loops to good use in Python
			Having fun with functions
			Keeping cool with classes
			Checking out some useful Python libraries
		Using Open Source R for Data Science
			Comprehending R’s basic vocabulary
			Delving into functions and operators
			Iterating in R
			Observing how objects work
			Sorting out R’s popular statistical analysis packages
			Examining packages for visualizing, mapping, and graphing in R
	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
			Defining data types
			Designing constraints properly
			Normalizing your database
		Narrowing the Focus with SQL Functions
		Making Life Easier with Excel
			Using Excel to quickly get to know your data
			Reformatting and summarizing with PivotTables
			Automating Excel tasks with macros
	Chapter 8 Telling Powerful Stories with Data
		Data Visualizations: The Big Three
			Data storytelling for decision makers
			Data showcasing for analysts
			Designing data art for activists
		Designing to Meet the Needs of Your Target Audience
			Step 1: Brainstorm (All about Eve)
			Step 2: Define the purpose
			Step 3: Choose the most functional visualization type for your purpose
		Picking the Most Appropriate Design Style
			Inducing a calculating, exacting response
			Eliciting a strong emotional response
		Selecting the Appropriate Data Graphic Type
			Standard chart graphics
			Comparative graphics
			Statistical plots
			Topology structures
			Spatial plots and maps
		Testing Data Graphics
		Adding Context
			Creating context with data
			Creating context with annotations
			Creating context with graphical elements
Part 3 Taking Stock of Your Data Science Capabilities
	Chapter 9 Developing Your Business Acumen
		Bridging the Business Gap
			Contrasting business acumen with subject matter expertise
			Defining business acumen
		Traversing the Business Landscape
			Seeing how data roles support the business in making money
			Leveling up your business acumen
			Fortifying your leadership skills
		Surveying Use Cases and Case Studies
			Documentation for data leaders
			Documentation for data implementers
	Chapter 10 Improving Operations
		Establishing Essential Context for Operational Improvements Use Cases
		Exploring Ways That Data Science Is Used to Improve Operations
			Making major improvements to traditional manufacturing operations
			Optimizing business operations with data science
			An AI case study: Automated, personalized, and effective debt collection processes
			Gaining logistical efficiencies with better use of real-time data
			Another AI case study: Real-time optimized logistics routing
			Modernizing media and the press with data science and AI
			Generating content with the click of a button
			Yet another case study: Increasing content generation rates
	Chapter 11 Making Marketing Improvements
		Exploring Popular Use Cases for Data Science in Marketing
		Turning Web Analytics into Dollars and Sense
			Getting acquainted with omnichannel analytics
			Mapping your channels
			Building analytics around channel performance
			Scoring your company’s channels
		Building Data Products That Increase Sales-and-Marketing ROI
		Increasing Profit Margins with Marketing Mix Modeling
			Collecting data on the four Ps
			Implementing marketing mix modeling
			Increasing profitability with MMM
	Chapter 12 Enabling Improved Decision-Making
		Improving Decision-Making
		Barking Up the Business Intelligence Tree
		Using Data Analytics to Support Decision-Making
			Types of analytics
			Common challenges in analytics
			Data wrangling
		Increasing Profit Margins with Data Science
			Seeing which kinds of data are useful when using data science for decision support
			Directing improved decision-making for call center agents
			Discovering the tipping point where the old way stops working
	Chapter 13 Decreasing Lending Risk and Fighting Financial Crimes
		Decreasing Lending Risk with Clustering and Classification
		Preventing Fraud Via Natural Language Processing (NLP)
	Chapter 14 Monetizing Data and Data Science Expertise
		Setting the Tone for Data Monetization
		Monetizing Data Science Skills as a Service
			Data preparation services
			Model building services
		Selling Data Products
		Direct Monetization of Data Resources
			Coupling data resources with a service and selling it
			Making money with data partnerships
		Pricing Out Data Privacy
Part 4 Assessing Your Data Science Options
	Chapter 15 Gathering Important Information about Your Company
		Unifying Your Data Science Team Under a Single Business Vision
		Framing Data Science around the Company’s Vision, Mission, and Values
		Taking Stock of Data Technologies
		Inventorying Your Company’s Data Resources
			Requesting your data dictionary and inventory
			Confirming what’s officially on file
			Unearthing data silos and data quality issues
		People-Mapping
			Requesting organizational charts
			Surveying the skillsets of relevant personnel
		Avoiding Classic Data Science Project Pitfalls
			Staying focused on the business, not on the tech
			Drafting best practices to protect your data science project
		Tuning In to Your Company’s Data Ethos
			Collecting the official data privacy policy
			Taking AI ethics into account
		Making Information-Gathering Efficient
	Chapter 16 Narrowing In on the Optimal Data Science Use Case
		Reviewing the Documentation
		Selecting Your Quick-Win Data Science Use Cases
			Zeroing in on the quick win
			Producing a POTI model
		Picking between Plug-and-Play Assessments
			Carrying out a data skill gap analysis for your company
			Assessing the ethics of your company’s AI projects and products
			Assessing data governance and data privacy policies
	Chapter 17 Planning for Future Data Science Project Success
		Preparing an Implementation Plan
		Supporting Your Data Science Project Plan
			Analyzing your alternatives
			Interviewing intended users and designing accordingly
			POTI modeling the future state
		Executing On Your Data Science Project Plan
	Chapter 18 Blazing a Path to Data Science Career Success
		Navigating the Data Science Career Matrix
		Landing Your Data Scientist Dream Job
			Leaning into data science implementation
			Acing your accreditations
			Making the grade with coding bootcamps and data science career accelerators
			Networking and building authentic relationships
			Developing your own thought leadership in data science
			Building a public data science project portfolio
		Leading with Data Science
		Starting Up in Data Science
			Choosing a business model for your data science business
			Selecting a data science start-up revenue model
			Taking inspiration from Kam Lee’s success story
			Following in the footsteps of the data science entrepreneurs
Part 5 The Part of Tens
	Chapter 19 Ten Phenomenal Resources for Open Data
		Digging Through data.gov
		Checking Out Canada Open Data
		Diving into data.gov.uk
		Checking Out US Census Bureau Data
		Accessing NASA Data
		Wrangling World Bank Data
		Getting to Know Knoema Data
		Queuing Up with Quandl Data
		Exploring Exversion Data
		Mapping OpenStreetMap Spatial Data
	Chapter 20 Ten Free or Low-Cost Data Science Tools and Applications
		Scraping, Collecting, and Handling Data Tools
			Sourcing and aggregating image data with ImageQuilts
			Wrangling data with DataWrangler
		Data-Exploration Tools
			Getting up to speed in Gephi
			Machine learning with the WEKA suite
		Designing Data Visualizations
			Getting Shiny by RStudio
			Mapmaking and spatial data analytics with CARTO
			Talking about Tableau Public
			Using RAWGraphs for web-based data visualization
		Communicating with Infographics
			Making cool infographics with Infogram
			Making cool infographics with Piktochart
Index
EULA