href="#ulink_945d0469-eefe-5f56-8cac-3c1bf9a7ac46">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 and R Fit into Your Data Science Strategy
Using Python for Data Science
Using Open Source R 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
7 Part 3: Taking Stock of Your Data Science Capabilities Chapter 9: Developing Your Business Acumen Bridging the Business Gap Traversing the Business Landscape Surveying Use Cases and Case Studies Chapter 10: Improving Operations Establishing Essential Context for Operational Improvements Use Cases Exploring Ways That Data Science Is Used to Improve Operations Chapter 11: Making Marketing Improvements Exploring Popular Use Cases for Data Science in Marketing Turning Web Analytics into Dollars and Sense Building Data Products That Increase Sales-and-Marketing ROI Increasing Profit Margins with Marketing Mix Modeling Chapter 12: Enabling Improved Decision-Making Improving Decision-Making Barking Up the Business Intelligence Tree Using Data Analytics to Support Decision-Making Increasing Profit Margins with Data Science 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 Selling Data Products Direct Monetization of Data Resources Pricing Out Data Privacy
8 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 People-Mapping Avoiding Classic Data Science Project Pitfalls Tuning In to Your Company’s Data Ethos 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 Picking between Plug-and-Play Assessments Chapter 17: Planning for Future Data Science
