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Data-Driven Decisions; Scaling Impact at Google

Data-Driven Decisions; Scaling Impact at Google

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Data-Driven Decisions: Scaling Impact at Google - Course Curriculum

Data-Driven Decisions: Scaling Impact at Google

Unlock the power of data to drive impactful decisions and scale your impact, Google-style! This comprehensive course, developed with insights from Google's leading data scientists and decision-makers, provides you with the knowledge, skills, and tools needed to transform raw data into actionable insights and achieve measurable results. Participants receive a prestigious certificate upon completion, issued by The Art of Service.



Course Overview

This course is designed to be interactive, engaging, comprehensive, personalized, and up-to-date. You'll gain practical, real-world applications through hands-on projects, bite-sized lessons, and actionable insights. Enjoy lifetime access, gamification, progress tracking, a user-friendly mobile experience, and become part of a community-driven learning environment.



Course Curriculum

Module 1: Foundations of Data-Driven Decision Making

  • Chapter 1: Introduction to Data-Driven Decision Making: What, Why, and How
  • Chapter 2: The Data Ecosystem: Understanding Data Sources and Types
  • Chapter 3: Defining Business Objectives and KPIs: Aligning Data with Strategic Goals
  • Chapter 4: Ethical Considerations in Data Analysis: Privacy, Bias, and Responsible Use
  • Chapter 5: Data Governance and Compliance: Ensuring Data Quality and Security

Module 2: Data Collection and Preparation

  • Chapter 6: Data Collection Methods: Surveys, Experiments, and Observational Studies
  • Chapter 7: Data Warehousing and Data Lakes: Centralizing and Organizing Data
  • Chapter 8: Data Cleaning and Preprocessing: Handling Missing Values and Outliers
  • Chapter 9: Data Transformation and Feature Engineering: Creating Meaningful Variables
  • Chapter 10: Data Validation and Verification: Ensuring Data Accuracy and Reliability

Module 3: Data Analysis and Visualization

  • Chapter 11: Descriptive Statistics: Understanding Data Distribution and Central Tendency
  • Chapter 12: Inferential Statistics: Making Predictions and Drawing Conclusions
  • Chapter 13: Hypothesis Testing: Formulating and Testing Business Hypotheses
  • Chapter 14: Data Visualization Principles: Creating Effective Charts and Graphs
  • Chapter 15: Data Visualization Tools: Mastering Tableau, Power BI, and Google Data Studio

Module 4: Predictive Analytics and Machine Learning

  • Chapter 16: Introduction to Machine Learning: Concepts and Applications
  • Chapter 17: Regression Analysis: Predicting Continuous Outcomes
  • Chapter 18: Classification Algorithms: Predicting Categorical Outcomes
  • Chapter 19: Clustering Analysis: Identifying Patterns and Segments
  • Chapter 20: Time Series Analysis: Forecasting Future Trends

Module 5: A/B Testing and Experimentation

  • Chapter 21: Principles of A/B Testing: Designing and Running Effective Experiments
  • Chapter 22: Statistical Significance and Power Analysis: Interpreting Results Accurately
  • Chapter 23: Multivariate Testing: Optimizing Multiple Variables Simultaneously
  • Chapter 24: Personalization and Targeting: Delivering Relevant Experiences
  • Chapter 25: Iteration and Optimization: Continuously Improving Performance

Module 6: Data Storytelling and Communication

  • Chapter 26: Crafting Compelling Data Narratives: Communicating Insights Effectively
  • Chapter 27: Presentation Skills for Data Professionals: Delivering Engaging Presentations
  • Chapter 28: Data-Driven Report Writing: Creating Clear and Concise Reports
  • Chapter 29: Stakeholder Management: Building Consensus and Driving Action
  • Chapter 30: Communicating Technical Information to Non-Technical Audiences

Module 7: Data-Driven Product Development

  • Chapter 31: Data-Informed Product Strategy: Identifying Opportunities and Priorities
  • Chapter 32: User Research and Data Integration: Understanding User Needs
  • Chapter 33: Agile Development and Data Feedback Loops: Iterating Quickly and Effectively
  • Chapter 34: Measuring Product Success: Defining and Tracking Key Metrics
  • Chapter 35: Using Data to Prioritize Features and Improvements

Module 8: Data-Driven Marketing and Sales

  • Chapter 36: Customer Segmentation and Targeting: Reaching the Right Customers
  • Chapter 37: Campaign Measurement and Optimization: Maximizing ROI
  • Chapter 38: Lead Scoring and Qualification: Identifying High-Potential Leads
  • Chapter 39: Sales Forecasting and Pipeline Management: Predicting Future Sales
  • Chapter 40: Personalizing Customer Experiences with Data

Module 9: Data-Driven Operations and Supply Chain

  • Chapter 41: Demand Forecasting and Inventory Management: Optimizing Inventory Levels
  • Chapter 42: Process Optimization and Automation: Improving Efficiency and Reducing Costs
  • Chapter 43: Supply Chain Visibility and Risk Management: Minimizing Disruptions
  • Chapter 44: Predictive Maintenance: Preventing Equipment Failures
  • Chapter 45: Data-Driven Quality Control

Module 10: Data-Driven Human Resources

  • Chapter 46: Talent Acquisition and Recruitment: Finding the Best Candidates
  • Chapter 47: Employee Engagement and Retention: Improving Employee Satisfaction
  • Chapter 48: Performance Management and Development: Optimizing Employee Performance
  • Chapter 49: Diversity and Inclusion Analytics: Promoting a Diverse and Inclusive Workplace
  • Chapter 50: Workforce Planning and Forecasting

Module 11: Advanced Data Analysis Techniques

  • Chapter 51: Natural Language Processing (NLP): Analyzing Text Data
  • Chapter 52: Computer Vision: Analyzing Image and Video Data
  • Chapter 53: Network Analysis: Understanding Relationships and Connections
  • Chapter 54: Geospatial Analysis: Analyzing Location Data
  • Chapter 55: Causal Inference: Determining Cause-and-Effect Relationships

Module 12: Data Security and Privacy

  • Chapter 56: Data Encryption and Anonymization: Protecting Sensitive Data
  • Chapter 57: Data Access Control and Authorization: Limiting Access to Data
  • Chapter 58: Data Breach Prevention and Response: Minimizing the Impact of Breaches
  • Chapter 59: Privacy Regulations and Compliance: Understanding GDPR and CCPA
  • Chapter 60: Building a Culture of Data Security

Module 13: Big Data and Cloud Computing

  • Chapter 61: Introduction to Big Data Technologies: Hadoop, Spark, and Kafka
  • Chapter 62: Cloud Computing Platforms: AWS, Azure, and Google Cloud
  • Chapter 63: Data Storage and Processing in the Cloud: Scalability and Cost Optimization
  • Chapter 64: Data Integration in the Cloud: Connecting Different Data Sources
  • Chapter 65: Leveraging Cloud Services for Machine Learning

Module 14: Building a Data-Driven Culture

  • Chapter 66: Leadership Commitment and Support: Creating a Data-First Mindset
  • Chapter 67: Data Literacy and Training: Empowering Employees with Data Skills
  • Chapter 68: Data Sharing and Collaboration: Breaking Down Data Silos
  • Chapter 69: Data Governance and Ethics: Establishing Clear Guidelines
  • Chapter 70: Measuring the Impact of Data-Driven Initiatives

Module 15: Real-World Case Studies at Google

  • Chapter 71: Case Study 1: Data-Driven Search Engine Optimization (SEO)
  • Chapter 72: Case Study 2: Using Data to Improve Google Ads Performance
  • Chapter 73: Case Study 3: Data-Driven Product Recommendation Systems
  • Chapter 74: Case Study 4: Analyzing User Behavior on YouTube
  • Chapter 75: Case Study 5: Optimizing Google Cloud Infrastructure with Data

Module 16: Scaling Data-Driven Impact

  • Chapter 76: Defining and Measuring Impact at Scale: Setting meaningful goals and metrics.
  • Chapter 77: Building Scalable Data Pipelines: Automating data workflows for efficiency.
  • Chapter 78: Democratizing Data Access: Empowering teams with self-service analytics.
  • Chapter 79: Fostering a Culture of Experimentation: Encouraging continuous improvement.
  • Chapter 80: The Future of Data-Driven Decision Making: Emerging trends and technologies.

Module 17: Capstone Project: Applying Your Knowledge

  • Chapter 81: Project Selection: Choose a real-world data challenge.
  • Chapter 82: Data Analysis and Modeling: Apply the techniques you've learned.
  • Chapter 83: Developing Actionable Recommendations: Present your findings and insights.
  • Chapter 84: Peer Review and Feedback: Learn from your classmates' projects.
  • Chapter 85: Final Project Submission and Evaluation

Module 18: Course Conclusion and Next Steps

  • Chapter 86: Review of Key Concepts: Consolidate your learning.
  • Chapter 87: Resources for Continued Learning: Expand your knowledge.
  • Chapter 88: Building Your Data Portfolio: Showcase your skills to employers.
  • Chapter 89: Networking Opportunities: Connect with other data professionals.
  • Chapter 90: Obtaining Your Certificate: Celebrate your success.
Upon successful completion of all modules and the capstone project, you will receive a certificate issued by The Art of Service, demonstrating your expertise in data-driven decision making and scaling impact, Google-style.

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