Data-Driven Decisions; Scaling Impact at Google

Description

What does the Data-Driven Decisions course cover?

Data-Driven Decisions is covered here in 18 modules: Foundations of Data-Driven Decision Making, Data Collection and Preparation, Data Analysis and Visualization and 15 more. The outline lists 90 specific topics, opening with Chapter 1: Introduction to Data-Driven Decision Making: What, Why, and How and closing with Chapter 90: Obtaining Your Certificate: Celebrate your success..

How do you approach Data-Driven Decisions step by step?

The work is sequenced in 18 stages. It starts with Foundations of Data-Driven Decision Making, moves through Data Collection and Preparation and Data Analysis and Visualization, and ends at Course Conclusion and Next Steps. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Data-Driven Decisions course?

Module 1 is Foundations of Data-Driven Decision Making. It works through 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 and 2 more. It sets the vocabulary the remaining 17 modules build on.

How is the Data-Driven Decisions course delivered?

The Data-Driven Decisions course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Data-Driven Decisions course cost?

The Data-Driven Decisions course is $199 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

More answers: what you get with every course, refund policy, all help answers.

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 40: Personalizing Customer Experiences with Data

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 45: Data-Driven Quality Control

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 50: Workforce Planning and Forecasting

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 54: Geospatial Analysis: Analyzing Location Data

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 60: Building a Culture of Data Security

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 70: Measuring the Impact of Data-Driven Initiatives

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.