Advanced Data Analytics

1Welcome to the Google Advanced Data Analytics Certificate▸ 2Navigate Your Data Career with Curiosity▸ 3Google Advanced Data Analytics Certificate Overview▸ 4Course 1 Overview▸ 5Introduction to Course 1▸ 6Get Started with Your Certificate▸ 7Data Discourse Over the Years▸ 8Explore Your Data Toolbox▸ 9Prepare to Assess Your Readiness▸ 10Understand Your Readiness Score▸ 11T Final Module Assessment▸ 12T1 Mid Module Quiz▸

1Create a DataDriven Business Solution▸ 2Critical Data Security and Privacy Principles▸ 3The Practices and Principles of Good Data Stewardship▸ 4The Data Professional Career Space▸ 5Build the Perfect Data Team▸ 6Activity Organize Your Data Team▸ 7Activity Exemplar Organize Your Data Team▸ 8DataDriven Careers Drive Modern Business▸ 9Profiles of Data Professionals▸ 10Where Data Makes a Difference for the Future▸ 11Leverage Data Analysis in Nonprofits▸ 12The Top Skills Needed for a Data Career▸ 13Ideal Qualities for Data Analytics Professionals▸ 14Volunteer Data Skills to Make a Positive Impact▸ 15Important Ethical Considerations for Data Professionals▸ 16T Final Module Assessment▸ 17T1 Mid Module Quiz▸

1A Lifelong Love of Data▸ 2The Future of Data Careers▸ 3Data Science Across Industries▸ 4Emerging Trends in Data Science▸ 5Career Growth and Development Paths▸ 6The Places Youll Go▸ 7Trajectory of the Field▸ 8T Final Module Assessment▸ 9T1 Mid Module Quiz▸

1Importance of Communication in a Data Science Career▸ 2Connect PACE with Upcoming Course Themes▸ 3The Value of the PACE Strategy Document▸ 4Communicate Objectives with a Project Proposal▸ 5Connect PACE with Executive Summaries▸ 6Activity Create a Project Proposal▸ 7Activity Exemplar Create a Project Proposal▸ 8Introduction to PACE▸ 9The PACE Stages▸ 10Key Elements of Communication▸ 11Best Communication Practices for Data Professionals▸ 12Activity Communicate with Stakeholders in Different Roles▸ 13Activity Exemplar Communicate with Stakeholders▸ 14Communication Drives PACE▸ 15Elements of Successful Communication▸ 16T Final Module Assessment▸ 17T1 Mid Module Quiz▸

1The Value of a Portfolio▸ 2Course WrapUp and Next Steps▸ 3Introduction to Your EndofCourse Portfolio Project▸ 4EndofCourse Portfolio Project Introduction▸ 5Explore Your Course 1 Workplace Scenarios▸ 6Portfolio Project Automatidata▸ 7Portfolio Project TikTok▸ 8Portfolio Project Waze▸ 9EndofCourse Project WrapUp▸ 10Course 1 Glossary▸ 11T Final Module Assessment▸ 12T1 Mid Module Quiz▸

1Welcome to Course 2 Get Started with Python▸ 2Working with Variables and Data Types▸ 3Operators in Python▸ 4Python Syntax and Best Practices▸ 5Course 2 Overview▸ 6What Is Python▸ 7Why Python for Data Science▸ 8ObjectOriented Programming OOP Basics▸ 9Introduction to Jupyter Notebooks▸ 10Getting Started with Jupyter Notebooks▸ 11Variables in Python▸ 12Data Types in Python▸ 13T Final Module Assessment▸ 14T1 Mid Module Quiz▸

1Introduction to Functions▸ 2Writing Reusable Code▸ 3Error Handling with TryExcept▸ 4Practice with Error Handling▸ 5Calling Functions▸ 6Defining Custom Functions▸ 7Working with Functions▸ 8Lambda Functions▸ 9Introduction to Conditional Statements▸ 10Writing Conditional Statements▸ 11Conditional Statements in Practice▸ 12Clean Code Practices▸ 13T Final Module Assessment▸ 14T1 Mid Module Quiz▸

1Introduction to Loops▸ 2String Formatting▸ 3Working with Strings Practice▸ 4Loops and Strings Together▸ 5For Loops▸ 6While Loops▸ 7Working with Loops▸ 8Loop Control Break Continue Pass▸ 9Comprehensions in Python▸ 10Introduction to Strings▸ 11String Indexing and Slicing▸ 12String Methods▸ 13T Final Module Assessment▸ 14T1 Mid Module Quiz▸

1Introduction to Data Structures▸ 2Introduction to pandas▸ 3Working with pandas DataFrames▸ 4Data Loading Cleaning and Binning▸ 5Practice NumPy and pandas Together▸ 6Lists▸ 7Tuples▸ 8Dictionaries▸ 9Sets▸ 10Working with Data Structures▸ 11Choosing the Right Data Structure▸ 12Introduction to NumPy▸ 13NumPy Array Operations▸ 14T Final Module Assessment▸ 15T1 Mid Module Quiz▸

1EndofCourse Project Overview▸ 2Explore Your Workplace Scenarios▸ 3Project Automatidata Scenario▸ 4Project TikTok Scenario▸ 5Project Waze Scenario▸ 6Activity Exemplars▸ 7Course 2 Glossary▸ 8Course WrapUp and Next Steps▸ 9T Final Module Assessment▸ 10T1 Mid Module Quiz▸

1Course 3 Overview▸ 2Ethics in Data Storytelling▸ 3HandsOn Activity Identify the Story▸ 4Module 1 Review and WrapUp▸ 5What is EDA Exploratory Data Analysis▸ 6The 6 Practices of EDA▸ 7Data Cleaning Why It Matters▸ 8Data Cleaning Methods and Techniques▸ 9Discovering the Story in Data▸ 10Data Visualization Fundamentals▸ 11Choosing the Right Visualization▸ 12Communicating Insights to Stakeholders▸ 13T Final Module Assessment▸ 14T1 Mid Module Quiz▸

1Introduction to EDA with Python▸ 2HandsOn Activity Discovering Data with Python▸ 3Structuring Activity Organize and Navigate Data▸ 4Module 2 Review and Next Steps▸ 5Loading and Inspecting Data▸ 6Understanding Data Types▸ 7Descriptive Statistics with Python▸ 8Discovering Patterns Value Counts and Distributions▸ 9Structuring Data Sorting and Filtering▸ 10Structuring Data Grouping and Aggregation▸ 11Working with DateTime Data▸ 12Initial Data Exploration Workflow▸ 13T Final Module Assessment▸ 14T1 Mid Module Quiz▸

1Why Clean Data Matters▸ 2Categorical to Numerical Transformation▸ 3HandsOn Activity Clean and Validate a Dataset▸ 4Module 3 Review▸ 5Handling Missing Values▸ 6Removing Duplicates▸ 7Data Type Conversion▸ 8Handling Outliers▸ 9String Cleaning and Standardization▸ 10Joining Datasets merge▸ 11Joining Datasets concat and append▸ 12Validating Your Data▸ 13T Final Module Assessment▸ 14T1 Mid Module Quiz▸

1Data Visualization Best Practices▸ 2Presentation Skills for Data Professionals▸ 3HandsOn Activity Create Visualizations in Python and Tableau▸ 4Module 4 Review▸ 5Matplotlib Fundamentals▸ 6Seaborn for Statistical Visualization▸ 7Choosing Visualizations by Data Type▸ 8Accessible and Ethical Visualizations▸ 9Introduction to Tableau▸ 10Creating Charts in Tableau▸ 11Tableau Dashboards▸ 12Data Storytelling with Tableau▸ 13T Final Module Assessment▸ 14T1 Mid Module Quiz▸

1EndofCourse Project Overview▸ 2Workplace Scenarios Choose Your Project▸ 3Project Automatidata Scenario▸ 4Project TikTok Scenario▸ 5Project Waze Scenario▸ 6Activity Exemplars▸ 7Course 3 Glossary▸ 8Course WrapUp and Next Steps▸ 9T Final Module Assessment▸ 10T1 Mid Module Quiz▸

1Introduction to Course 4▸ 2Module 1 Glossary▸ 3Module 1 Review▸ 4The Role of Statistics in Data Science▸ 5Statistics in Action AB Testing▸ 6Descriptive vs Inferential Statistics▸ 7Measures of Central Tendency▸ 8Measures of Dispersion▸ 9Measures of Position▸ 10Compute Descriptive Statistics with Python▸ 11Activity Introduction to Statistics▸ 12T Final Module Assessment▸ 13T1 Mid Module Quiz▸

1Objective vs Subjective Probability▸ 2The Normal Distribution▸ 3Standardize Data Using ZScores▸ 4Work with Probability Distributions in Python▸ 5Module 2 Review▸ 6The Principles of Probability▸ 7Basic Rules of Probability and Events▸ 8Conditional Probability▸ 9Bayes Theorem▸ 10Expanded Bayes Theorem▸ 11Introduction to Probability Distributions▸ 12The Binomial Distribution▸ 13The Poisson Distribution▸ 14T Final Module Assessment▸ 15T1 Mid Module Quiz▸

1Introduction to Sampling▸ 2Sampling Distributions with Python▸ 3Module 3 Review▸ 4The Sampling Process▸ 5Probability Sampling Methods▸ 6NonProbability Sampling Methods▸ 7The Impact of Bias in Sampling▸ 8How Sampling Affects Your Data▸ 9The Central Limit Theorem▸ 10Sampling Distribution of the Proportion▸ 11Sampling Distribution of the Mean▸ 12T Final Module Assessment▸ 13T1 Mid Module Quiz▸

1Introduction to Confidence Intervals▸ 2Interpret Confidence Intervals▸ 3Construct a Confidence Interval for a Proportion▸ 4Construct a Confidence Interval for a Mean▸ 5Confidence Interval for Small Sample Size▸ 6Confidence Intervals with Python▸ 7Module 4 Glossary▸ 8Module 4 Review▸ 9T Final Module Assessment▸ 10T1 Mid Module Quiz▸

1Introduction to Hypothesis Testing▸ 2Experimental Design▸ 3Hypothesis Testing with Python▸ 4Module 5 Review▸ 5Null and Alternative Hypotheses▸ 6Type I and Type II Errors▸ 7Significance Level and PValues▸ 8OneSample Test for Means▸ 9TwoSample Tests for Means▸ 10OneTailed and TwoTailed Tests▸ 11TwoSample Tests for Proportions▸ 12AB Testing▸ 13T Final Module Assessment▸ 14T1 Mid Module Quiz▸

1EndofCourse Project Introduction▸ 2Explore Your Workplace Scenarios▸ 3Project Automatidata Scenario▸ 4Project TikTok Scenario▸ 5Project Waze Scenario▸ 6Activity Exemplars▸ 7Course 4 Glossary▸ 8Course WrapUp and Next Steps▸ 9T Final Module Assessment▸ 10T1 Mid Module Quiz▸

1Course 5 Overview▸ 2Correlation and Causation▸ 3Types of Relationships Between Variables▸ 4Introduction to Regression Analysis▸ 5Correlation Coefficients▸ 6Explore Variable Relationships with Python▸ 7Assumptions of Linear Regression▸ 8HandsOn Activity Explore Data Relationships▸ 9Module 1 Review▸ 10T Final Module Assessment▸ 11T1 Mid Module Quiz▸

1What is Simple Linear Regression▸ 2Ordinary Least Squares OLS▸ 3Evaluate Simple Linear Regression▸ 4Interpret Regression Coefficients▸ 5Check Model Assumptions▸ 6Build a Simple Linear Regression Model with Python▸ 7Activity Simple Linear Regression▸ 8Module 2 Review▸ 9T Final Module Assessment▸ 10T1 Mid Module Quiz▸

1From Simple to Multiple Regression▸ 2Build a Multiple Linear Regression Model▸ 3Multicollinearity▸ 4Feature Selection Techniques▸ 5Model Assumptions for MLR▸ 6Introduction to Logistic Regression▸ 7Logistic Regression with Python▸ 8Activity Multiple Linear Regression▸ 9Module 3 Review▸ 10T Final Module Assessment▸ 11T1 Mid Module Quiz▸

1Introduction to ANOVA▸ 2Module 4 Review▸ 3OneWay ANOVA▸ 4TwoWay ANOVA▸ 5PostHoc Tests▸ 6ANOVA Assumptions and Alternatives▸ 7ChiSquare Test Goodness of Fit▸ 8ChiSquare Test Test of Independence▸ 9ANOVA Lab Activity▸ 10ChiSquare Lab Activity▸ 11T Final Module Assessment▸ 12T1 Mid Module Quiz▸

1Course 5 EndofCourse Project Overview▸ 2Workplace Scenario Automatidata▸ 3Workplace Scenario TikTok▸ 4Workplace Scenario Waze▸ 5Exemplar Automatidata▸ 6Exemplar TikTok▸ 7Exemplar Waze▸ 8Course 5 Glossary▸ 9Course 5 WrapUp▸ 10T Final Module Assessment▸ 11T1 Mid Module Quiz▸

1Welcome to Machine Learning▸ 2What is Machine Learning▸ 3Types of Machine Learning▸ 4Categorical vs Continuous Target Variables▸ 5Common ML Algorithms Overview▸ 6Ethics in Machine Learning▸ 7Python Tools for Machine Learning▸ 8The Machine Learning Team▸ 9Module 1 Review▸ 10T Final Module Assessment▸ 11T1 Mid Module Quiz▸

1The PACE Workflow for ML▸ 2Module 2 Review▸ 3Feature Engineering Part 1▸ 4Feature Engineering Part 2▸ 5Handling Imbalanced Datasets▸ 6Naive Bayes Classification▸ 7Build a Naive Bayes Model in Python▸ 8Model Evaluation Metrics Deep Dive▸ 9CrossValidation▸ 10Activity Feature Engineering and Model Building▸ 11T Final Module Assessment▸ 12T1 Mid Module Quiz▸

1Introduction to Unsupervised Learning▸ 2KMeans Clustering Concept▸ 3KMeans Clustering Python Implementation▸ 4Determining Optimal Number of Clusters▸ 5Evaluating Clustering Results▸ 6KMeans Initialization and Variants▸ 7Beyond KMeans Brief Overview▸ 8Activity Customer Segmentation with KMeans▸ 9Module 3 Review▸ 10T Final Module Assessment▸ 11T1 Mid Module Quiz▸

1Decision Trees Concept▸ 2Activity TreeBased Models▸ 3Module 4 Review▸ 4Decision Trees Python Implementation▸ 5Overfitting and Hyperparameter Tuning▸ 6Ensemble Learning Bagging▸ 7Random Forest▸ 8Boosting AdaBoost▸ 9Gradient Boosting▸ 10XGBoost▸ 11Model Validation and Comparison▸ 12T Final Module Assessment▸ 13T1 Mid Module Quiz▸

1Course 6 EndofCourse Project Overview▸ 2Workplace Scenario Automatidata▸ 3Workplace Scenario TikTok▸ 4Workplace Scenario Waze▸ 5Exemplar Automatidata▸ 6Exemplar TikTok▸ 7Exemplar Waze▸ 8Course 6 Glossary▸ 9Course 6 WrapUp▸ 10T Final Module Assessment▸ 11T1 Mid Module Quiz▸

1Welcome to the Capstone▸ 2Capstone Project Overview▸ 3Review The PACE Workflow▸ 4Problem Definition and Scoping▸ 5Data Assessment and Planning▸ 6Workplace Scenario Final Stage Setup▸ 7Module 1 Review▸ 8T Final Module Assessment▸ 9T1 Mid Module Quiz▸

1Data Loading and Initial Exploration▸ 2Comprehensive EDA Univariate Analysis▸ 3Comprehensive EDA Bivariate and Multivariate Analysis▸ 4Data Cleaning Capstone Application▸ 5Feature Engineering for Your Capstone▸ 6Statistical Insights from EDA▸ 7PACE Analyze Phase Documentation▸ 8Module 2 Review▸ 9T Final Module Assessment▸ 10T1 Mid Module Quiz▸

1Model Planning Strategy▸ 2Regression Modeling Capstone Application▸ 3TreeBased Models Capstone Application▸ 4Hyperparameter Tuning▸ 5Model Evaluation and Comparison▸ 6Model Interpretation▸ 7PACE Construct Phase Documentation▸ 8Module 3 Review▸ 9T Final Module Assessment▸ 10T1 Mid Module Quiz▸

1From Analysis to Communication▸ 2Building an Executive Summary▸ 3Creating Effective Visualizations for Stakeholders▸ 4Stakeholder Presentation Preparation▸ 5Workplace Scenario Final Deliverables▸ 6Exemplar Review Final Project▸ 7Portfolio Preparation▸ 8Module 4 Review▸ 9T Final Module Assessment▸ 10T1 Mid Module Quiz▸

1Certificate Program Recap▸ 2Skills Assessment▸ 3Career Paths in Advanced Analytics▸ 4Building Your Professional Brand▸ 5Course 7 Glossary▸ 6Certificate Program Complete Final WrapUp▸ 7T Final Module Assessment▸ 8T1 Mid Module Quiz▸