Basic Statistics Using Microsoft Excel and SPSS Masterclass
A comprehensive, hands-on three-day online masterclass designed to equip researchers, postgraduate students, data analysts, and professionals with essential statistical skills and practical proficiency in Microsoft Excel and IBM SPSS Statistics.
High Level Description
The Basic Statistics Using Microsoft Excel and SPSS Masterclass is a comprehensive, hands-on training program designed to equip researchers, postgraduate students, data analysts, and professionals with essential statistical skills and practical proficiency in two of the most widely used data analysis tools: Microsoft Excel and IBM SPSS Statistics. This three-day intensive online workshop provides participants with the foundational knowledge and applied techniques needed to conduct robust statistical analyses, visualize data effectively, and interpret results with confidence.
Led by Prof. Beatrice Opeolu, Dr. Abiola Babatunde, Dr. Claris Siyamayambo, and Mr. Victory Opeolu, the masterclass takes a progressive, learn-by-doing approach. Day 1 focuses on mastering Excel for data analysis—covering data entry, descriptive statistics, visualization, and correlation analysis. Day 2 introduces participants to SPSS, guiding them through the interface, data entry, descriptive statistics, and visualization. Day 3 advances into inferential statistical methods, including hypothesis testing, t-tests, chi-square tests, ANOVA, regression, and correlation analyses using SPSS.
This masterclass is structured to build competence step-by-step, ensuring that participants develop both theoretical understanding and practical skills. Each session combines conceptual instruction with live demonstrations, guided exercises, and interactive problem-solving. Participants will work with real datasets, learn to interpret statistical outputs, and gain confidence in selecting and applying appropriate statistical techniques for their research or professional projects.
Whether you are new to statistical analysis or seeking to refresh and formalize your skills in Excel and SPSS, this masterclass provides a supportive, structured learning environment that transforms data into actionable insights and empowers you to conduct rigorous, evidence-based analysis.
Purpose
The masterclass aims to demystify statistical analysis by providing participants with accessible, practical training in two essential statistical software tools, enabling them to conduct and interpret basic and inferential statistical analyses with confidence and accuracy.
Participants will gain both conceptual understanding and hands-on experience in:
- Organizing, formatting, and managing data in Excel and SPSS
- Computing and interpreting descriptive statistics (means, medians, standard deviations, frequencies)
- Creating effective data visualizations (charts, histograms, scatter plots) to communicate findings
- Conducting correlation analyses to examine relationships between variables
- Performing hypothesis testing and understanding statistical significance
- Applying inferential statistical tests (t-tests, chi-square, ANOVA, regression) appropriately
- Interpreting statistical outputs and making data-driven decisions
- Selecting appropriate statistical methods for different research questions and data types
Course Structure
Foundations of Statistics with Excel
Data entry, formatting, formulas, descriptive statistics, visualization, and correlation analysis using Excel, ending with Q&A and recap.
SPSS for Beginners
SPSS interface, data entry, descriptive statistics, visualization exercises, and Q&A to consolidate SPSS fundamentals.
Inferential Analysis with SPSS
Hypothesis testing, t-tests, chi-square, ANOVA, regression, correlation, and final Q&A with next steps.
Learning Outcomes
Excel + SPSS Proficiency
Navigate and utilize Microsoft Excel and IBM SPSS Statistics for data analysis and reporting.
Data Management
Organize, format, and manage datasets effectively in both platforms.
Descriptive Statistics
Compute and interpret central tendency, dispersion, and distribution outputs accurately.
Visualization
Create clear, professional charts and visualizations to communicate findings.
Inferential Testing
Apply hypothesis testing, t-tests, chi-square, ANOVA, and interpret results confidently.
Regression & Correlation
Conduct correlation and regression analyses, interpret outputs, and make predictions.
Unique Value
- Dual-platform mastery of Excel and SPSS
- Progressive, hands-on learning design across three days
- Expert facilitation by experienced statisticians
- Interactive, supportive small-cohort experience
- Immediate applicability to research and professional projects
- Coverage from data entry to publication-ready reporting
Format and Duration
Delivered as a three-day intensive online workshop (09:00 – 16:00 daily), the masterclass totals approximately 18 hours of live instruction and practice. Each day is structured with morning and afternoon sessions, coffee breaks, and lunch intervals. Participants will need access to Microsoft Excel (with Data Analysis Toolpak enabled) and IBM SPSS Statistics throughout the workshop.
Who Should Enrol
Ideal for:
- Postgraduate students (Master’s and Doctoral) requiring statistical analysis skills
- Early-career researchers seeking to formalize statistical knowledge
- Academic staff and lecturers teaching quantitative courses
- Data analysts and research assistants working with quantitative datasets
- Professionals in health, social sciences, business, and applied research fields
- Anyone new to statistical software seeking accessible, structured training
Prerequisites: No prior statistical software experience required. Basic familiarity with research concepts and data is helpful but not essential.
Outcome and Certification
Participants who complete all three days of the masterclass, including practical exercises and interactive sessions, receive a Certificate of Completion in Basic Statistics Using Microsoft Excel and SPSS, attesting to their competence in conducting descriptive and inferential statistical analyses using industry-standard software tools.
Detailed Lesson Descriptions
Foundations of Statistics with Excel
Lesson 3.1.1: Welcome and Introductions — Assistant Dean / Prof. Beatrice Opeolu / Mr. Victory Opeolu. Overview of objectives, agenda, and interactive expectation-setting with Mentimeter.
Lesson 3.1.2: Introduction to Excel for Data Analysis — Dr. Claris Siyamayambo. Data entry, formatting, essential formulas, and spreadsheet organization for analysis.
Lesson 3.1.3: Descriptive Statistics in Excel — Dr. Claris Siyamayambo. Central tendency, dispersion, distributions, and Data Analysis Toolpak.
Lesson 3.1.4: Data Visualization in Excel — Dr. Abiola Babatunde. Bar charts, histograms, pie charts, scatter plots, and best practices.
Lesson 3.1.5: Correlation Analysis in Excel — Dr. Abiola Babatunde. Correlation coefficients, scatter plots with trendlines, and interpretation.
Lesson 3.1.6: Q&A and Wrap-Up of Day 1 — Prof. Beatrice Opeolu / All Facilitators. Review and transition to SPSS.
SPSS for Beginners
Lesson 3.2.1: Day 1 Topics Recap and Feedback — Prof. Beatrice Opeolu. Consolidation and transition to SPSS.
Lesson 3.2.2: Introduction to SPSS — Dr. Abiola Babatunde. Interface, Variable View vs Data View, files, and navigation.
Lesson 3.2.3: Data Entry in SPSS — Dr. Abiola Babatunde. Variable definition, coding, missing data, importing datasets.
Lesson 3.2.4: Descriptive Statistics in SPSS — Dr. Claris Siyamayambo. Frequencies, descriptives, distributions, and output interpretation.
Lesson 3.2.5: SPSS Visualization Exercise — Dr. Claris Siyamayambo. Chart Builder, histograms, bar charts, boxplots, scatter plots.
Lesson 3.2.6: Q&A and Wrap-Up of Day 2 — Prof. Beatrice Opeolu / All Facilitators. Review and preview of Day 3.
Inferential Analysis with SPSS
Lesson 3.3.1: Day 2 Topics Recap and Feedback — Prof. Beatrice Opeolu. Consolidation and bridge to inferential analysis.
Lesson 3.3.2: Hypothesis Testing in SPSS — Dr. Claris Siyamayambo. Null/alternative hypotheses, p-values, errors, and interpretation.
Lesson 3.3.3: t-Tests, Chi-Square, and ANOVA in SPSS — Dr. Abiola Babatunde. Test selection, assumptions, and output interpretation.
Lesson 3.3.4: Regression and Correlation Analyses Using SPSS — Dr. Abiola Babatunde. Correlation matrices, linear regression, model fit.
Lesson 3.3.5: Final Q&A and Next Steps — Prof. Beatrice Opeolu / Mr. Victory Opeolu / All Facilitators. Reflection, feedback, resources, and certification.
Choose Your Plan
All plans include certificate of completion and lifetime access to materials.