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

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Advanced Statistical Methods for Digital Marketing Analytics ( 30 Modules )

Advanced Statistical Methods for Digital Marketing Analytics
( 30 Modules )