📊 Statistical Data Analysis
Levels: Beginner | Intermediate | Advanced
Course Overview
The Statistical Data Analysis course empowers learners with techniques to collect, summarize, analyze, and interpret data for evidence-based decision-making. This multi-level course emphasizes practical applications of statistics using tools such as Microsoft Excel, SPSS, and R.
Designed for researchers, analysts, students, and professionals across sectors, it provides essential analytical capabilities required in academia, development work, health, economics, and business intelligence.
Beginner Level
(Introduction to Statistics & Data Handling)
What You Will Learn
Basics of statistics: data types, variables, and levels of measurement
Summarizing data using tables, graphs, and descriptive measures
Measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation)
Introduction to Excel for data entry and basic calculations
Creating and interpreting frequency tables and charts
Understanding sampling techniques and data quality
Target Audience
Beginners, students, early-stage researchers, and professionals with no prior statistics background.
Course Duration
6 weeks (3 sessions per week, 2 hours per session)
Prerequisites
None
Intermediate Level
(Applied Statistical Techniques & SPSS)
What You Will Learn
Probability distributions and hypothesis testing
T-tests, Chi-square tests, and ANOVA
Correlation and simple regression analysis
Using SPSS for data entry, management, and output interpretation
Handling missing data and recoding variables
Exporting reports and charts for presentations
Target Audience
Researchers, monitoring & evaluation officers, students in research-heavy programs, NGO data personnel
Course Duration
6 weeks (3 sessions per week, 2 hours per session)
Prerequisites
Completion of Beginner Level or foundational statistics knowledge
Advanced Level
(Multivariate Analysis & Statistical Modeling with R)
What You Will Learn
Multiple regression and logistic regression models
Factor analysis and principal component analysis (PCA)
Time series analysis and forecasting basics
Advanced data visualization in R (ggplot2)
Writing reproducible analysis reports
Final project: real-world dataset analysis using R or SPSS
Target Audience
Advanced researchers, analysts, postgrad students, data scientists, and M&E professionals
Course Duration
6 weeks (3 sessions per week, 2 hours per session)
Prerequisites
Completion of Intermediate Level or equivalent statistical/data analysis experience
Fees
Application Fee: UGX 30,000/=
Tuition Fee: UGX 250,000/= (Per Level)
Certification
Certificate of Completion awarded at each level by Billbrain Institute of Technology
Career Pathways
Research Assistant
Monitoring & Evaluation Officer
Statistical Analyst
Data Quality Analyst
Policy & Program Analyst
Health or Education Researcher