Advanced Statistical Methods for Digital Marketing Analytics
( 30 Modules )
Module #1 Introduction to Advanced Statistical Methods Overview of the importance of advanced statistical methods in digital marketing analytics, course objectives, and prerequisites.
Module #2 Review of Fundamentals:Probability and Statistics Refresher on probability theory, statistical inference, and common statistical distributions.
Module #3 Hypothesis Testing and Confidence Intervals Advanced hypothesis testing techniques, including non-parametric tests and confidence intervals.
Module #4 Regression Analysis:Simple and Multiple Linear Regression In-depth coverage of simple and multiple linear regression, including assumptions, model building, and interpretation.
Module #5 Regression Analysis:Logistic Regression and Generalized Linear Models Logistic regression, generalized linear models, and extensions to binary and count data.
Module #6 Time-Series Analysis:ARIMA and ETS Models Introduction to time-series analysis, including ARIMA and ETS models, stationarity, and trend decomposition.
Module #7 Time-Series Analysis:Forecasting and Model Evaluation Forecasting techniques, model evaluation metrics, and model selection methods for time-series analysis.
Module #8 Machine Learning for Digital Marketing Analytics Introduction to machine learning concepts, including supervised and unsupervised learning, and model evaluation metrics.
Module #9 Supervised Learning:Decision Trees and Random Forests Decision trees, random forests, and ensemble methods for classification and regression tasks.
Module #10 Supervised Learning:Neural Networks and Deep Learning Introduction to neural networks, deep learning, and their applications in digital marketing analytics.
Module #11 Unsupervised Learning:Clustering and Dimensionality Reduction Clustering algorithms, including k-means and hierarchical clustering, and dimensionality reduction techniques.
Module #12 Text Analytics and Natural Language Processing Introduction to text analytics, natural language processing, and sentiment analysis for digital marketing analytics.
Module #13 Network Analysis and Graph Theory Network analysis, graph theory, and their applications in digital marketing analytics, including social network analysis.
Module #14 A/B Testing and Experimentation Design and analysis of A/B tests, including hypothesis testing, sample size calculation, and effect size estimation.
Module #15 Survival Analysis and Customer Lifetime Value Introduction to survival analysis, customer lifetime value, and churn prediction in digital marketing analytics.
Module #16 .Panel Data Analysis and Customer Journey Mapping Panel data analysis, customer journey mapping, and their applications in digital marketing analytics.
Module #17 Big Data Analytics for Digital Marketing Introduction to big data analytics, including data processing, storage, and visualization for digital marketing analytics.
Module #18 Data Visualization for Digital Marketing Analytics Effective data visualization techniques for digital marketing analytics, including data storytelling and visualization best practices.
Module #19 Advanced Data Mining Techniques Advanced data mining techniques, including association rule mining, clustering, and anomaly detection.
Module #20 Marketing Mix Modeling and Attribution Marketing mix modeling, attribution modeling, and their applications in digital marketing analytics.
Module #21 Predictive Modeling for Customer Acquisition Predictive modeling techniques for customer acquisition, including propensity scoring and lookalike modeling.
Module #22 Predictive Modeling for Customer Retention Predictive modeling techniques for customer retention, including churn prediction and customer lifetime value analysis.
Module #23 Real-World Case Studies in Digital Marketing Analytics Real-world case studies and applications of advanced statistical methods in digital marketing analytics.
Module #24 Handling Missing Data and Imputation Techniques Introduction to missing data, types of missingness, and imputation techniques for digital marketing analytics.
Module #25 Data Quality and Data Validation Data quality, data validation, and data preprocessing techniques for digital marketing analytics.
Module #26 Ethics and Fairness in Digital Marketing Analytics Ethical considerations in digital marketing analytics, including fairness, transparency, and accountability.
Module #27 Advanced Topics in Digital Marketing Analytics Advanced topics in digital marketing analytics, including multitask learning, transfer learning, and attention-based models.
Module #28 Capstone Project:Applying Advanced Statistical Methods Hands-on project applying advanced statistical methods to a real-world digital marketing analytics problem.
Module #29 Best Practices for Implementation and Scaling Best practices for implementing and scaling advanced statistical methods in digital marketing analytics organizations.
Module #30 Course Wrap-Up & Conclusion Planning next steps in Advanced Statistical Methods for Digital Marketing Analytics career
