Advanced Data Analytics

1L1 Welcome to the Google Advanced Data Analytics Certificate→ 2L10 Navigate Your Data Career with Curiosity→ 3L2 Google Advanced Data Analytics Certificate Overview→ 4L3 Course 1 Overview→ 5L4 Introduction to Course 1→ 6L5 Get Started with Your Certificate→ 7L6 Data Discourse Over the Years→ 8L7 Explore Your Data Toolbox→ 9L8 Prepare to Assess Your Readiness→ 10L9 Understand Your Readiness Score→

1L1 Create a DataDriven Business Solution→ 2L10 Critical Data Security and Privacy Principles→ 3L11 The Practices and Principles of Good Data Stewardship→ 4L12 The Data Professional Career Space→ 5L13 Build the Perfect Data Team→ 6L14 Activity Organize Your Data Team→ 7L15 Activity Exemplar Organize Your Data Team→ 8L2 DataDriven Careers Drive Modern Business→ 9L3 Profiles of Data Professionals→ 10L4 Where Data Makes a Difference for the Future→ 11L5 Leverage Data Analysis in Nonprofits→ 12L6 The Top Skills Needed for a Data Career→ 13L7 Ideal Qualities for Data Analytics Professionals→ 14L8 Volunteer Data Skills to Make a Positive Impact→ 15L9 Important Ethical Considerations for Data Professionals→

1L1 A Lifelong Love of Data→ 2L2 The Future of Data Careers→ 3L3 Data Science Across Industries→ 4L4 Emerging Trends in Data Science→ 5L5 Career Growth and Development Paths→ 6L6 The Places Youll Go→ 7L7 Trajectory of the Field→

1L1 Importance of Communication in a Data Science Career→ 2L10 Connect PACE with Upcoming Course Themes→ 3L11 The Value of the PACE Strategy Document→ 4L12 Communicate Objectives with a Project Proposal→ 5L13 Connect PACE with Executive Summaries→ 6L14 Activity Create a Project Proposal→ 7L15 Activity Exemplar Create a Project Proposal→ 8L2 Introduction to PACE→ 9L3 The PACE Stages→ 10L4 Key Elements of Communication→ 11L5 Best Communication Practices for Data Professionals→ 12L6 Activity Communicate with Stakeholders in Different Roles→ 13L7 Activity Exemplar Communicate with Stakeholders→ 14L8 Communication Drives PACE→ 15L9 Elements of Successful Communication→

1L1 The Value of a Portfolio→ 2L10 Course WrapUp and Next Steps→ 3L2 Introduction to Your EndofCourse Portfolio Project→ 4L3 EndofCourse Portfolio Project Introduction→ 5L4 Explore Your Course 1 Workplace Scenarios→ 6L5 Portfolio Project Automatidata→ 7L6 Portfolio Project TikTok→ 8L7 Portfolio Project Waze→ 9L8 EndofCourse Project WrapUp→ 10L9 Course 1 Glossary→

1L1 Welcome to Course 2 Get Started with Python→ 2L10 Working with Variables and Data Types→ 3L11 Operators in Python→ 4L12 Python Syntax and Best Practices→ 5L2 Course 2 Overview→ 6L3 What Is Python→ 7L4 Why Python for Data Science→ 8L5 ObjectOriented Programming OOP Basics→ 9L6 Introduction to Jupyter Notebooks→ 10L7 Getting Started with Jupyter Notebooks→ 11L8 Variables in Python→ 12L9 Data Types in Python→

1L1 Introduction to Functions→ 2L10 Writing Reusable Code→ 3L11 Error Handling with TryExcept→ 4L12 Practice with Error Handling→ 5L2 Calling Functions→ 6L3 Defining Custom Functions→ 7L4 Working with Functions→ 8L5 Lambda Functions→ 9L6 Introduction to Conditional Statements→ 10L7 Writing Conditional Statements→ 11L8 Conditional Statements in Practice→ 12L9 Clean Code Practices→

1L1 Introduction to Loops→ 2L10 String Formatting→ 3L11 Working with Strings Practice→ 4L12 Loops and Strings Together→ 5L2 For Loops→ 6L3 While Loops→ 7L4 Working with Loops→ 8L5 Loop Control Break Continue Pass→ 9L6 Comprehensions in Python→ 10L7 Introduction to Strings→ 11L8 String Indexing and Slicing→ 12L9 String Methods→

1L1 Introduction to Data Structures→ 2L10 Introduction to pandas→ 3L11 Working with pandas DataFrames→ 4L12 Data Loading Cleaning and Binning→ 5L13 Practice NumPy and pandas Together→ 6L2 Lists→ 7L3 Tuples→ 8L4 Dictionaries→ 9L5 Sets→ 10L6 Working with Data Structures→ 11L7 Choosing the Right Data Structure→ 12L8 Introduction to NumPy→ 13L9 NumPy Array Operations→

1L1 EndofCourse Project Overview→ 2L2 Explore Your Workplace Scenarios→ 3L3 Project Automatidata Scenario→ 4L4 Project TikTok Scenario→ 5L5 Project Waze Scenario→ 6L6 Activity Exemplars→ 7L7 Course 2 Glossary→ 8L8 Course WrapUp and Next Steps→

1L1 Course 3 Overview→ 2L10 Ethics in Data Storytelling→ 3L11 HandsOn Activity Identify the Story→ 4L12 Module 1 Review and WrapUp→ 5L2 What is EDA Exploratory Data Analysis→ 6L3 The 6 Practices of EDA→ 7L4 Data Cleaning Why It Matters→ 8L5 Data Cleaning Methods and Techniques→ 9L6 Discovering the Story in Data→ 10L7 Data Visualization Fundamentals→ 11L8 Choosing the Right Visualization→ 12L9 Communicating Insights to Stakeholders→

1L1 Introduction to EDA with Python→ 2L10 HandsOn Activity Discovering Data with Python→ 3L11 Structuring Activity Organize and Navigate Data→ 4L12 Module 2 Review and Next Steps→ 5L2 Loading and Inspecting Data→ 6L3 Understanding Data Types→ 7L4 Descriptive Statistics with Python→ 8L5 Discovering Patterns Value Counts and Distributions→ 9L6 Structuring Data Sorting and Filtering→ 10L7 Structuring Data Grouping and Aggregation→ 11L8 Working with DateTime Data→ 12L9 Initial Data Exploration Workflow→

1L1 Why Clean Data Matters→ 2L10 Categorical to Numerical Transformation→ 3L11 HandsOn Activity Clean and Validate a Dataset→ 4L12 Module 3 Review→ 5L2 Handling Missing Values→ 6L3 Removing Duplicates→ 7L4 Data Type Conversion→ 8L5 Handling Outliers→ 9L6 String Cleaning and Standardization→ 10L7 Joining Datasets merge→ 11L8 Joining Datasets concat and append→ 12L9 Validating Your Data→

1L1 Data Visualization Best Practices→ 2L10 Presentation Skills for Data Professionals→ 3L11 HandsOn Activity Create Visualizations in Python and Tableau→ 4L12 Module 4 Review→ 5L2 Matplotlib Fundamentals→ 6L3 Seaborn for Statistical Visualization→ 7L4 Choosing Visualizations by Data Type→ 8L5 Accessible and Ethical Visualizations→ 9L6 Introduction to Tableau→ 10L7 Creating Charts in Tableau→ 11L8 Tableau Dashboards→ 12L9 Data Storytelling with Tableau→

1L1 EndofCourse Project Overview→ 2L2 Workplace Scenarios Choose Your Project→ 3L3 Project Automatidata Scenario→ 4L4 Project TikTok Scenario→ 5L5 Project Waze Scenario→ 6L6 Activity Exemplars→ 7L7 Course 3 Glossary→ 8L8 Course WrapUp and Next Steps→

1L1 Introduction to Course 4→ 2L10 Module 1 Glossary→ 3L11 Module 1 Review→ 4L2 The Role of Statistics in Data Science→ 5L3 Statistics in Action AB Testing→ 6L4 Descriptive vs Inferential Statistics→ 7L5 Measures of Central Tendency→ 8L6 Measures of Dispersion→ 9L7 Measures of Position→ 10L8 Compute Descriptive Statistics with Python→ 11L9 Activity Introduction to Statistics→

1L1 Objective vs Subjective Probability→ 2L10 The Normal Distribution→ 3L11 Standardize Data Using ZScores→ 4L12 Work with Probability Distributions in Python→ 5L13 Module 2 Review→ 6L2 The Principles of Probability→ 7L3 Basic Rules of Probability and Events→ 8L4 Conditional Probability→ 9L5 Bayes Theorem→ 10L6 Expanded Bayes Theorem→ 11L7 Introduction to Probability Distributions→ 12L8 The Binomial Distribution→ 13L9 The Poisson Distribution→

1L1 Introduction to Sampling→ 2L10 Sampling Distributions with Python→ 3L11 Module 3 Review→ 4L2 The Sampling Process→ 5L3 Probability Sampling Methods→ 6L4 NonProbability Sampling Methods→ 7L5 The Impact of Bias in Sampling→ 8L6 How Sampling Affects Your Data→ 9L7 The Central Limit Theorem→ 10L8 Sampling Distribution of the Proportion→ 11L9 Sampling Distribution of the Mean→

1L1 Introduction to Confidence Intervals→ 2L2 Interpret Confidence Intervals→ 3L3 Construct a Confidence Interval for a Proportion→ 4L4 Construct a Confidence Interval for a Mean→ 5L5 Confidence Interval for Small Sample Size→ 6L6 Confidence Intervals with Python→ 7L7 Module 4 Glossary→ 8L8 Module 4 Review→

1L1 Introduction to Hypothesis Testing→ 2L10 Experimental Design→ 3L11 Hypothesis Testing with Python→ 4L12 Module 5 Review→ 5L2 Null and Alternative Hypotheses→ 6L3 Type I and Type II Errors→ 7L4 Significance Level and PValues→ 8L5 OneSample Test for Means→ 9L6 TwoSample Tests for Means→ 10L7 OneTailed and TwoTailed Tests→ 11L8 TwoSample Tests for Proportions→ 12L9 AB Testing→

1L1 EndofCourse Project Introduction→ 2L2 Explore Your Workplace Scenarios→ 3L3 Project Automatidata Scenario→ 4L4 Project TikTok Scenario→ 5L5 Project Waze Scenario→ 6L6 Activity Exemplars→ 7L7 Course 4 Glossary→ 8L8 Course WrapUp and Next Steps→

1L1 Course 5 Overview→ 2L2 Correlation and Causation→ 3L3 Types of Relationships Between Variables→ 4L4 Introduction to Regression Analysis→ 5L5 Correlation Coefficients→ 6L6 Explore Variable Relationships with Python→ 7L7 Assumptions of Linear Regression→ 8L8 HandsOn Activity Explore Data Relationships→ 9L9 Module 1 Review→

1L1 What is Simple Linear Regression→ 2L2 Ordinary Least Squares OLS→ 3L3 Evaluate Simple Linear Regression→ 4L4 Interpret Regression Coefficients→ 5L5 Check Model Assumptions→ 6L6 Build a Simple Linear Regression Model with Python→ 7L7 Activity Simple Linear Regression→ 8L8 Module 2 Review→

1L1 From Simple to Multiple Regression→ 2L2 Build a Multiple Linear Regression Model→ 3L3 Multicollinearity→ 4L4 Feature Selection Techniques→ 5L5 Model Assumptions for MLR→ 6L6 Introduction to Logistic Regression→ 7L7 Logistic Regression with Python→ 8L8 Activity Multiple Linear Regression→ 9L9 Module 3 Review→

1L1 Introduction to ANOVA→ 2L10 Module 4 Review→ 3L2 OneWay ANOVA→ 4L3 TwoWay ANOVA→ 5L4 PostHoc Tests→ 6L5 ANOVA Assumptions and Alternatives→ 7L6 ChiSquare Test Goodness of Fit→ 8L7 ChiSquare Test Test of Independence→ 9L8 ANOVA Lab Activity→ 10L9 ChiSquare Lab Activity→

1L1 Course 5 EndofCourse Project Overview→ 2L2 Workplace Scenario Automatidata→ 3L3 Workplace Scenario TikTok→ 4L4 Workplace Scenario Waze→ 5L5 Exemplar Automatidata→ 6L6 Exemplar TikTok→ 7L7 Exemplar Waze→ 8L8 Course 5 Glossary→ 9L9 Course 5 WrapUp→

1L1 Welcome to Machine Learning→ 2L2 What is Machine Learning→ 3L3 Types of Machine Learning→ 4L4 Categorical vs Continuous Target Variables→ 5L5 Common ML Algorithms Overview→ 6L6 Ethics in Machine Learning→ 7L7 Python Tools for Machine Learning→ 8L8 The Machine Learning Team→ 9L9 Module 1 Review→

1L1 The PACE Workflow for ML→ 2L10 Module 2 Review→ 3L2 Feature Engineering Part 1→ 4L3 Feature Engineering Part 2→ 5L4 Handling Imbalanced Datasets→ 6L5 Naive Bayes Classification→ 7L6 Build a Naive Bayes Model in Python→ 8L7 Model Evaluation Metrics Deep Dive→ 9L8 CrossValidation→ 10L9 Activity Feature Engineering and Model Building→

1L1 Introduction to Unsupervised Learning→ 2L2 KMeans Clustering Concept→ 3L3 KMeans Clustering Python Implementation→ 4L4 Determining Optimal Number of Clusters→ 5L5 Evaluating Clustering Results→ 6L6 KMeans Initialization and Variants→ 7L7 Beyond KMeans Brief Overview→ 8L8 Activity Customer Segmentation with KMeans→ 9L9 Module 3 Review→

1L1 Decision Trees Concept→ 2L10 Activity TreeBased Models→ 3L11 Module 4 Review→ 4L2 Decision Trees Python Implementation→ 5L3 Overfitting and Hyperparameter Tuning→ 6L4 Ensemble Learning Bagging→ 7L5 Random Forest→ 8L6 Boosting AdaBoost→ 9L7 Gradient Boosting→ 10L8 XGBoost→ 11L9 Model Validation and Comparison→

1L1 Course 6 EndofCourse Project Overview→ 2L2 Workplace Scenario Automatidata→ 3L3 Workplace Scenario TikTok→ 4L4 Workplace Scenario Waze→ 5L5 Exemplar Automatidata→ 6L6 Exemplar TikTok→ 7L7 Exemplar Waze→ 8L8 Course 6 Glossary→ 9L9 Course 6 WrapUp→

1L1 Welcome to the Capstone→ 2L2 Capstone Project Overview→ 3L3 Review The PACE Workflow→ 4L4 Problem Definition and Scoping→ 5L5 Data Assessment and Planning→ 6L6 Workplace Scenario Final Stage Setup→ 7L7 Module 1 Review→

1L1 Data Loading and Initial Exploration→ 2L2 Comprehensive EDA Univariate Analysis→ 3L3 Comprehensive EDA Bivariate and Multivariate Analysis→ 4L4 Data Cleaning Capstone Application→ 5L5 Feature Engineering for Your Capstone→ 6L6 Statistical Insights from EDA→ 7L7 PACE Analyze Phase Documentation→ 8L8 Module 2 Review→

1L1 Model Planning Strategy→ 2L2 Regression Modeling Capstone Application→ 3L3 TreeBased Models Capstone Application→ 4L4 Hyperparameter Tuning→ 5L5 Model Evaluation and Comparison→ 6L6 Model Interpretation→ 7L7 PACE Construct Phase Documentation→ 8L8 Module 3 Review→

1L1 From Analysis to Communication→ 2L2 Building an Executive Summary→ 3L3 Creating Effective Visualizations for Stakeholders→ 4L4 Stakeholder Presentation Preparation→ 5L5 Workplace Scenario Final Deliverables→ 6L6 Exemplar Review Final Project→ 7L7 Portfolio Preparation→ 8L8 Module 4 Review→

1L1 Certificate Program Recap→ 2L2 Skills Assessment→ 3L3 Career Paths in Advanced Analytics→ 4L4 Building Your Professional Brand→ 5L5 Course 7 Glossary→ 6L6 Certificate Program Complete Final WrapUp→