Training Course in Practical Statistics and Data-Driven Decision Making (Online / Remote)
1Summary
Numbers only become useful once someone knows how to read them — otherwise a spreadsheet full of figures stays exactly that, a spreadsheet. This training course from the Arab British Fellowship Training Academy is built to close that gap, turning raw data into the kind of numerical evidence that holds up when a decision is on the line.
Across a full statistical toolkit — data organization, probability, hypothesis testing, variance analysis and regression — participants build both the theoretical grounding and the hands-on ability to work with Excel and SPSS. The course is designed for the reality of government institutions, private companies, research centers and nonprofits alike: wherever raw data needs to become a trend, a red flag, or a measurable result, this is the skill set behind it.
2Objectives and target group
Who Should Attend?
- Staff in planning and strategic-analysis departments.
- Statisticians and analysts in the public and private sectors.
- Academics and researchers in the social and economic sciences.
- Database and information-systems administrators.
By the end of the course, participants will be able to:
- Apply statistical fundamentals to collecting, interpreting and analyzing data.
- Use the right statistical tools to summarize data and present it visually.
- Apply probability distributions and statistical inference within their analysis.
- Analyze relationships between variables using advanced statistical methods.
- Carry out practical analytical tasks in Excel and SPSS.
3Course Content
Module 1: Why Statistics Matters: Core Concepts of Data
- Differences between qualitative and quantitative data.
- Where data comes from and why the source matters for analysis.
- Types of statistical scales and how each one is used.
Module 2: Summarizing Data: Central Tendency, Dispersion and Visuals
- Mean, median and mode as measures of central tendency.
- Standard deviation, variance and range as measures of dispersion.
- Bar charts, pie charts and box plots for visualizing patterns and trends.
Module 3: Collecting and Organizing Data the Right Way
- Surveys, observations and interviews as data-collection methods.
- Coding and grouping techniques.
- Building frequency-distribution tables.
Module 4: Probability Foundations for Analysts
- The mathematical definition of probability.
- Rules of probability: addition and intersection principles.
- Applying probability to real decision-making.
Module 5: Common Probability Distributions
- The normal distribution and its properties.
- Binomial and Poisson distributions.
- Using statistical tables in practice.
Module 6: Putting Theory into Practice with Excel and SPSS
- Entering and organizing data within the software.
- Running descriptive and inferential analyses.
- Extracting results and generating explanatory reports.
Module 7: Testing Hypotheses With Confidence
- Formulating null and alternative hypotheses.
- Determining significance level and p-value.
- The steps of hypothesis testing and how they lead to a decision.
Module 8: Comparing Groups and Modeling Relationships: ANOVA and Regression
- One-way ANOVA for comparing group means.
- An introduction to simple linear regression.
- Reading results and interpreting coefficients.
Module 9: From Numbers to Decisions: Interpreting Results
- Translating statistical output into practical conclusions.
- Avoiding the most common analysis errors.
- The role statistical analysis plays in improving institutional performance.