Since 2011, our staff have presented over 80 webinars and multi-day workshops in business analytics around Australia. We regularly present training for academic, government and industry clients in Adelaide, Brisbane, Canberra, Melbourne, Perth, and Sydney. We are also preparing a series of webinars in business analytics designed for both people with and people without a background in data science.
Armed with 17 years experience working in the fields of statistics and information systems, our staff regularly present training in Adelaide, Brisbane, Canberra, Melbourne, Perth and Sydney. Shown below are general descriptions of some of the workshops that we currently present. Please note that content may vary slightly between workshops and that for an accurate description of the material for a specific workshop that you should refer to that workshop's description on our Upcoming Training page.. To keep up to date with our training program please consider joining our email list. To see a list of the workshops we have previously held click here.
Click on each title below to view details about the workshops we currently present.
A Gentle Introduction to Statistics using SPSS and R
This workshop is designed as a gentle introduction to the field of statistics, where absolutely no prior knowledge of statistics is assumed. The workshop will cover the role of statistics in academic research, the different types of variables, simple graphs for viewing a dataset, an overview of statistical tests and p-values, a flowchart for identifying which statistical method to use for particular research questions, t-tests and chi-squared tests, the importance of testing assumptions made by statistical tests, non-parametric statistics (such as the Mann-Whitney test), a brief overview of linear and logistic regression, and a very brief overview of more advanced statistical methods.
Introduction to Linear and Logistic Regression
In this workshop we will explore the foundation for linear and logistic regression. The workshop will cover the fundamental equations for linear and logistic regression, the assumptions that need to be checked when performing regression (linearity, normality, constant variance, no outliers), multiple regression and the effect of confounders, variable selection (choosing which variables to select for a linear regression model), the differences between linear and logistic regression, interpreting the regression coefficients for logistic regression, nominal and ordinal regression, and Poisson regression (for count variables).
Introduction to Longitudinal Data Analysis
In this workshop we will cover the differences between independent, clustered, and longitudinal data, a review of linear regression and Analysis of Variance, Mixed Effects models, hierarchical and multi-level models, Generalised Estimating Equations, the problem with missing data in longitudinal studies, and Multiple Imputation.
Introduction to Survey Design A
Within this workshop we cover: how to minimise the differences between the target population (the members of a population that we ideally want to collect data on) and the sampling frame (those members that we actually can collect data on), survey sampling (the random selection of members from the sampling frame to include within a particular survey), data collection methods (how to choose between methods such as telephone, mail, and face-to-face interviews for a particular survey), and interview techniques (how to structure an interview to minimise potential bias introduced by the interviewer).
Introduction to Survey Design B
Within this workshop we cover: how to design the questions for a survey (considering factors such as ambiguity, memory problems, and participant sensitivity), how to evaluate the questions (gathering data from the population about the use of a proposed survey), data pre-processing (the steps involved between data collection and statistical analysis), and the issue of missing data (both how to minimise it during data collection and how to reduce biases during data analysis).
Introduction to Structural Equation Modelling
In this workshop we will cover the foundations of SEM (including correlation, regression, latent and measured variables), the stages of fitting an SEM model (specification, identification, estimation, testing, and modification), assessing model fit, fitting a number of models using SEM (including regression models, path models, confirmatory factor analysis, multilevel models and latent growth models), and advice on reporting SEM results.
We are currently developing and presenting webinars on business analytics for business analysts and data analysts.