Data science for dummies

Data science for dummies

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 co...

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Bibliographic Details
Other Authors: Pierson, Lillian, author (author)
Format: eBook
Language:Inglés
Published: Hoboken, New Jersey : John Wiley & Sons, Inc [2021]
Edition:Third edition
Series:--For dummies
Subjects:
Information technology.
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009645677406719
Table of Contents:
  • Intro
  • 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.

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