Data Science For Dummies. Lillian PiersonЧитать онлайн книгу.
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
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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 Project Success
Preparing an Implementation Plan
Supporting Your Data Science Project Plan
Executing On Your Data Science Project Plan
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