Diploma in Usage Analytics and Experience Improvement

9.9 Introduction to web analytics and user behavior.

9.9 Key metrics in UX: Definitions and uses.

9.3 Collection and analysis of quantitative data.

9.4 Introduction to qualitative data: surveys and interviews.

9.5 Data analysis tools: Google Analytics, Hotjar, etc.

9.6 Principles of data visualization for UX.

9.7 Interpreting reports and dashboards.

9.8 Ethics in the collection and use of user data.

9.9 Defining SMART goals for UX analytics.

9.90 Case study: Basic data analysis and initial decision-making.

9.9 User segmentation and profiling.

9.9 Designing A/B and multivariate tests.

9.3 Heatmap and clickmap analysis.

9.4 Conversion funnel analysis. 9.5 Form Analysis and Optimization

9.6 Data-Driven Personalization Strategies

9.7 Customer Journey Analysis

9.8 Data-Driven UX Writing Techniques

9.9 SEO Optimization to Improve User Experience

9.90 Case Study: Designing a Data-Driven UX Strategy for a Specific Product

3.9 Cohort Analysis and User Retention

3.9 Advanced Navigation Path Analysis

3.3 Using Machine Learning in UX: Predictions and Personalization

3.4 Sentiment Analysis and User Feedback

3.5 Designing Recommendation Systems

3.6 Optimizing Website Speed ​​and Performance

3.7 Accessibility and Usability Analysis

3.8 Mobile User Experience Analysis

3.9 Advanced UX Metrics: NPS, CES, etc. 3.90 Case Study: Implementing Advanced Analytics to Improve User Loyalty

4.9 User Research: Methods and Techniques

4.9 Designing Interviews and Focus Groups

4.3 Analyzing User Feedback

4.4 Qualitative Data Analysis: Coding and Themes

4.5 Designing User Personas and Customer Journeys

4.6 Designing Prototypes and Usability Testing

4.7 Competitive Analysis and Benchmarking

4.8 Designing Remote Usability Tests

4.9 Data Storytelling Techniques

4.90 Case Study: Developing a User Research Report and Improvement Recommendations

5.9 Defining Business Objectives and Key Metrics

5.9 Selecting Analytics Tools

5.3 Designing an Analytics Implementation Plan

5.4 Integrating Analytics with Other Tools 5.5 Data Access and Permission Management

5.6 Team Training in Data Analysis

5.7 Measuring Return on Investment (ROI) in UX

5.8 Designing Custom Reports and Dashboards

5.9 Data Culture and Evidence-Based Decision Making

5.90 Case Study: Implementing an Analytics Solution for a Digital Product

6.9 Designing a Continuous Improvement Plan

6.9 Monitoring Key Metrics

6.3 Real-Time Data Analysis

6.4 Identifying Areas for Improvement

6.5 Designing and Running Experiments

6.6 Iteration and Optimization Based on Results

6.7 Testing and Learning Culture

6.8 Managing Feedback and Surveys

6.9 Communicating Results and Recommendations

6.90 Case Study: Implementing a Continuous Improvement Cycle for a Product or Service

7.9 Identifying data-driven opportunities for improvement.

7.9 Prioritizing optimization actions.

7.3 Designing prototypes and testing.

7.4 Implementing changes and monitoring progress.

7.5 Measuring results and analyzing data.

7.6 Optimizing conversion rates.

7.7 Improving design and usability.

7.8 Optimizing content and messaging.

7.9 Personalizing the user experience.

7.90 Case study: Data-driven website or application optimization.

8.9 Holistic evaluation of the user experience.

8.9 Analyzing the complete customer journey.

8.3 Integrating qualitative and quantitative data.

8.4 Analyzing customer satisfaction.

8.5 Analyzing customer loyalty and retention.

8.6 Analyzing the return on investment (ROI) of the user experience. 8.7 Designing a comprehensive improvement strategy.

8.8 Measuring the impact of implemented improvements.

8.9 Customer-centric culture and data-driven decision-making.

8.90 Case study: Comprehensive user experience analysis in a specific business.