DATA LITERACY FOR THE INFORMATION WORKER
Data literacy is the ability to identify, locate, interpret and evaluate information and then communicate key insights effectively (Australian Public Service Commission, 2018). It means the ability to competently turn data into useful knowledge, and apply that knowledge to drive effective decision making.
With the advent of automation, humans’ role has become to do what computers cannot. Interpreting data insights requires “data literacy”; All professionals now need to be data literate in an increasingly automated and decision-focused future. This is even more the case for anyone working with, supporting, leading, or hiring a data science and analytics team.
Data Literacy for the Information Worker covers a broad range of what are now important business skills, including:
an appreciation of the language of data
understanding uncertainty (expressed as probabilities) and complexity
interpreting relationships in data (multivariate correlation) and visual representations of them
reading visual representations of data
Critical thinking skills for reading data insights are becoming core skills for 21st century workplaces. The course covers how to think rigorously and abstractly about evidence-based decision-making and manipulate data accordingly. It introduces a range of skills and applications related to logic and critical thinking in such areas as forecasting, population measurement, set theory and logic, causal impact and attribution, scientific reasoning and the danger of cognitive biases.
The course is highly recommended for managers and workers who have not had quantitative work as the basis of their career to date, for business analysts and for many IT professionals. The main tool used will be Microsoft Excel, and other tools for data manipulation, data analysis and data visualisation will also be introduced, with hands-on exercises. There are no prerequisites beyond high-school mathematics; this course has been designed to be approachable for everyone.
What is data literacy, and why it is essential
Diving deeper into to the world of data
Logic, critical thinking and structured reasoning
Why uncertainty matters and how probability is essential for describing it
Data driven decision making
Associations, relationships and complexity in data
Using data to look into the future (forecasting and time series)
The scientific method, correlation vs causation, errors and biases
Regression, Machine Learning, and AI can automate processes for you