Here's What You'll Learn:

Module 1: Introduction to Spatial Analysis and QGIS

Module 2: Vector Data Geoprocessing

Module 3: Raster Data Analysis

Module 4: Working with Attribute Data and Joins

Module 5: Advanced Vector Geoprocessing Tools

Module 6: DEM and Terrain Analysis

Module 7: Network Analysis

Module 8: Geostatistics and Interpolation

Module 9: Advanced Spatial Analysis Techniques

Module 10: Automating Geoprocessing with Graphical Modeler and Python

Here's What You'll Learn

• Develop mathematical models for vaccine-preventable diseases using R.
• Understand disease transmission dynamics and reproductive number (R0).
• Estimate model parameters and perform sensitivity analysis.
• Design and run simulations to explore vaccination strategies.
• Apply advanced modeling techniques, including age-structured models and stochastic approaches.
• Conduct scenario analysis and interpret model outcomes.
• Validate model predictions and perform uncertainty analysis.
• Create effective data visualizations and reports for model results.
• Link models to public health policy and evaluate vaccination program impacts.
• Stay updated with emerging trends and advances in disease modeling.

Here's What You'll Learn

• Analyze marketing data using R, including data acquisition, cleaning, and preprocessing.
• Conduct exploratory data analysis to identify patterns and trends in marketing data.
• Perform customer segmentation and profiling using cluster analysis and RFM analysis.
• Analyze marketing campaign performance, including attribution modeling and ROI analysis.
• Create effective data visualizations with Tableau, connecting R and using advanced visualization techniques.
• Apply predictive analytics techniques, such as churn prediction and customer lifetime value prediction.
• Analyze social media data with sentiment analysis and social network analysis.
• Conduct web analytics and optimize conversion rates using A/B testing.
• Perform sales data analysis, including sales forecasting and market basket analysis.
• Integrate multiple data sources and create interactive dashboards in Tableau.
• Apply advanced statistical methods, including hypothesis testing, multivariate analysis, and time series analysis.
• Build machine learning models for marketing applications using R.
• Use Geographic Information Systems (GIS) for marketing analysis and create spatial visualizations.
• Analyze real-world case studies and develop project plans for marketing analytics.
• Conduct scenario analysis and develop decision support systems in R.
• Understand data ethics, privacy regulations, and implement ethical considerations in data analysis.
• Utilize advanced Tableau techniques, including custom calculations and advanced chart types.
• Complete a capstone project that integrates all learned concepts, with data analysis and visualization.

Here's What You'll Learn:

Data management techniques, including importing, exporting, cleaning, and preparing datasets.

• Descriptive statistics and data exploration methods.
• Performing regression analysis, panel data analysis, and time series analysis.
• Conducting hypothesis testing and using advanced programming techniques in STATA.
• Applying econometric models and advanced data analysis techniques.
• Creating and customizing data visualizations.

Here's What You'll Learn:

• Advanced Formulas and Functions: Mastering complex logical, text, date, and financial functions.
• Dynamic Arrays: Utilizing modern Excel features for efficient data manipulation.
• Data Analysis: Performing statistical analysis, forecasting, and scenario planning.
• Data Transformation: Cleaning, shaping, and integrating data using Power Query.
• Data Visualization: Creating interactive dashboards and advanced charts.
• Automation: Streamlining tasks with macros and VBA programming.
• Data Management: Implementing data validation and auditing techniques.
• Power Pivot: Building robust data models for complex analysis.
• Problem Solving: Applying advanced Excel skills to real-world business challenges.
• Efficiency Enhancement: Improving productivity through advanced Excel techniques.

Here's What You'll Learn:

• Introduction to Customer Analytics: Overview of metrics, data sources, and Tableau basics.

Data Visualization Basics: Creating and customizing charts, graphs, and visualizations in Tableau.

• Advanced Data Analysis: Using filters, parameters, calculated fields, and time series analysis.
• Dashboard Design: Building effective interactive dashboards with actions and filters.
• Customer Segmentation: Profiling and segmenting customers based on behavior and data.
• Predictive Analytics: Analyzing customer churn, predicting behavior, and identifying high-value customers.
• Marketing Analytics: Evaluating marketing campaigns, channels, and ROI.
• Customer Lifetime Value (CLV): Calculating and analyzing CLV metrics for strategic planning.
• Customer Satisfaction Analysis: Feedback evaluation and identifying areas of improvement.
• Future Trends in Customer Analytics: Insights into emerging technologies like predictive AI and case studies.