Training on Data Manipulation with SPSS and Visualization with Power BI

Data-driven organizations rely on accurate analysis and clear visualization to support decision-making, reporting, and performance tracking. However, many professionals struggle to move from raw data to meaningful insights due to gaps in data preparation, statistical analysis, and visualization skills.

This intensive 10-day training equips participants with end-to-end competencies in data manipulation using SPSS and data visualization using Power BI. The course is designed to bridge the gap between statistical analysis and business intelligence by integrating rigorous data analysis techniques with modern dashboarding and reporting tools.

Participants begin by mastering data preparation in SPSS, including data importation, cleaning, coding, transformation, and validation. The training then progresses to statistical analysis techniques such as descriptive statistics, hypothesis testing, correlation analysis, and regression modeling. Emphasis is placed on interpreting outputs and translating statistical findings into actionable insights.

The second part of the course focuses on Power BI, where participants learn to connect datasets, build data models, and design interactive dashboards. Key topics include data transformation using Power Query, creating calculated measures with DAX, and developing dynamic visual reports that communicate trends, patterns, and performance indicators effectively.

The course also emphasizes data storytelling, enabling participants to present insights in a clear, compelling, and decision-oriented manner. Through practical exercises and real-world datasets, learners develop the ability to produce high-quality analytical reports that support monitoring, evaluation, and strategic planning.

By the end of the training, participants will be able to manage complex datasets, perform statistical analysis, and design professional dashboards that enhance reporting accuracy, insight generation, and organizational decision-making.

Duration

10 Days

Who Should Attend

• Monitoring and Evaluation (M&E) professionals
• Data analysts, researchers, and statisticians
• Program managers and reporting officers
• Business intelligence and performance management teams
• Professionals involved in data-driven decision-making

Organizational Impact

• Enhanced capacity for accurate and timely data reporting

• Data-driven culture promoting evidence-based decision-making

• Improved efficiency in data processing and visualization

• Stronger communication of program and business performance metrics

Individual Impact

• Advanced data handling and statistical analysis proficiency

• Ability to design insightful dashboards for reporting

• Confidence in using SPSS and Power BI for integrated data workflows

• Improved storytelling and presentation of analytical insights

By the end of the course, participants will be able to:

• Collect, clean, and manage data using SPSS efficiently.

• Conduct descriptive and inferential statistical analyses.

• Integrate and visualize SPSS data using Power BI.

• Design interactive dashboards and automate reporting.

• Present data findings effectively for strategic decisions.

Module 1: Introduction to Data Management and SPSS Environment

• Understanding data management concepts and workflows

• Navigating the SPSS interface and defining data structures

• Importing, exporting, and organizing datasets from multiple sources

• Case Study: Setting up an SPSS database for a multi-country project

Module 2: Data Cleaning and Transformation Techniques

• Detecting and managing missing, duplicate, or inconsistent data

• Data transformation: recoding, computing, and labeling variables

• Ensuring data accuracy and validity for analysis

• Case Study: Cleaning a survey dataset for accurate reporting

Module 3: Descriptive and Inferential Data Analysis in SPSS

• Generating descriptive statistics and frequency distributions

• Conducting cross-tabulations, correlations, and regression analysis

• Interpreting and summarizing analytical outputs

• Case Study: Statistical analysis of program outcomes

Module 4: Advanced Statistical Techniques and Outputs

• Using factor analysis, ANOVA, and chi-square tests

• Creating tables, charts, and syntax automation in SPSS

• Exporting results for visualization and presentation

• Case Study: Evaluating program impact using inferential methods

Module 5: Introduction to Power BI for Data Visualization

• Overview of Power BI architecture and capabilities

• Connecting Power BI to SPSS outputs and other data sources

• Data modeling, cleaning, and transformations in Power Query

• Case Study: Linking SPSS datasets to Power BI for real-time insights

Module 6: Building Interactive Dashboards in Power BI

• Designing visualizations: charts, maps, KPIs, and cards

• Building drill-down dashboards for detailed analysis

• Best practices for dashboard design and usability

• Case Study: Creating a performance dashboard for donor reporting

Module 7: Using DAX and Advanced Analytics in Power BI

• Introduction to DAX functions and calculated measures

• Applying filters, relationships, and dynamic metrics

• Performing trend and comparative analyses

• Case Study: Developing dynamic indicators for M&E dashboards

Module 8: Integrating SPSS and Power BI for Automated Reporting

• Exporting analyzed SPSS data into Power BI for visualization

• Creating automated pipelines for data updates

• Generating real-time reports and alerts

• Case Study: Streamlining project monitoring with automated updates

Module 9: Data Governance and Reporting Standards

• Ensuring data accuracy, consistency, and confidentiality

• Reporting ethics and compliance with data standards

• Communicating insights effectively to decision-makers

• Case Study: Applying data governance best practices in reporting

Module 10: Capstone Project and Presentation

• Developing an end-to-end project using SPSS and Power BI

• Presenting findings through visual storytelling

• Peer review and group feedback session

• Case Study: Designing a full reporting dashboard for a field dataset