Build the statistical foundation you need for Data Analytics and Research. No previous statistics knowledge is required, start from the ground up and develop the statistical thinking behind every analysis.
You can learn how to use Excel, Power BI, Stata, R, Python or other analytical tools, but using analytical software effectively also requires an understanding of the statistics behind the results.
What does a p-value actually mean? What is the difference between standard deviation and standard error? When should you use a t-test, chi-square test or ANOVA? What does a confidence interval tell you? When should regression be used? How do you determine whether a result is statistically significant, and does statistical significance necessarily mean that a finding is important?
This 4-week beginner course is designed for participants who want to build this understanding from the ground up. It begins with the meaning of statistics and progressively develops your understanding of data, descriptive statistics, probability, sampling, statistical uncertainty, hypothesis testing, statistical tests, correlation and introductory regression.
The objective is not simply to memorize formulas. The objective is to develop the statistical thinking required for Data Analytics and Research.
For clarity and easier navigation, the 26 modules have been organized into 7 major learning units on this page. These units are not individual modules, each brings together several related modules taught progressively during the 4-week course. Participants receive training across the full 26-module curriculum.
Begin your statistical journey by understanding what statistics is, why it matters and how data are structured. This unit establishes the language and concepts required for everything that follows.
Learn how analysts summarize, describe and explore data before performing statistical inference. Participants learn not only how these statistics are calculated, but also what they tell us about the data.
Develop the concepts that connect descriptive statistics to statistical inference, and address one of the most important questions in statistics: how can information from a sample be used to learn about a larger population?
Learn the reasoning behind statistical hypothesis testing and how statistical evidence is evaluated. Participants learn why p < 0.05 does not automatically mean a finding is important, and p > 0.05 does not prove that there is no difference.
Learn how to select statistical methods based on the research question and the types of variables being analyzed, developing a practical decision framework for choosing the right test.
Progress from group comparisons into relationships between variables and introductory statistical modelling, an important foundation for more advanced statistical modelling.
Bring all the concepts together and learn how to interpret statistical evidence rather than simply reading software output. The course concludes with the complete statistical thinking framework: Research Question → Data → Description → Variability → Uncertainty → Inference → Interpretation → Conclusion.
No previous statistics knowledge is required.
Learning analytical software is important, but knowing which buttons to click or which commands to run does not automatically mean that you understand the analysis. A professional analyst should be able to answer these questions, and defend their conclusions.
Do not begin by memorizing statistical commands. Begin by understanding what the statistics mean, why they are used and how they help us learn from data. Registration is now open.