Description
The aims of this micro-credential is to equip enrollees with the skills and confidence to operate in the world of ‘big data.’ The micro-credential will take a social science perspective and to discuss the role of social science and theory in analysing and interpreting ‘big data.’ The micro-credential will not be technical, but rather use key examples of ‘big data’ being used to inform policy to help motivate and engage with the issues. Enrollees will become familiar with some of the technological options and constraints in the storage and analysis of ‘big data’.
Topics
- Introduction to data linkage
- Analysis of linked and transactional data
- Combining linked and survey data
Learning outcomes
Upon successful completion, enrollee's will have the knowledge and skills to:
- Explain the key concepts of data linkage
- Outline the strengths and weaknesses of existing administrative datasets from an analysis or policy perspective
- Identify the appropriate analytical technique for analysis of linked or administrative data
- Discuss some of the main assumptions underlying different techniques;
- Design or critique an analysis plan
Indicative assessment
Assignment 1 – Introductions and identification of data analysis questions (500 words, 20% of final mark) LO: 1, 2
Assignment 2 – Analysis plan (1,500 words, 80% of final mark) LO: 3, 4, 5, 6
Assumed knowledge
Completion of ANU Micro-credential Data Analysis and Interpretation (or equivalent).
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.
Micro-credential stack information
This micro-credential may be undertaken as part of a stack by completing Data Analysis and Interpretation.
Details
Course Code: DATA23
Workload: 21 hours
- Contact hours: 7 hours
- Individual study and assessment: 14 hours
ANU unit value: 1 unit
AQF Level: 8
Contact: Professor Nicholas Biddle
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
Description
The aim of this micro-credential is to equip enrollees with the skills and knowledge to engage with biosocial research either directly as a researcher, or as a policy maker critically engaging with the most recent research.
Topics
- Biosocial research frameworks
- Designing biosocial research
- Data quality considerations
- Missing data in biosocial research
- Interpreting biosocial research findings
Learning outcomes
Upon successful completion, enrollee's will have the knowledge and skills to:
- Specify a biosocial research question and conceptualise it using biosocial frameworks
- Communicate and critique existing biosocial research in a rigorous manner
- Understand the assumptions, strengths and limitations of biosocial research data
- Understand approaches to missing data in biosocial research
- Design or critique a biosocial data research study
Indicative assessment
Assignment 1 - Introductions and identification of research question (500 words, 20% of final mark) LO: 1, 2
Assignment 2 – Biosocial research design (1,500 words, 80% of final mark) LO: 3, 4, 5
Assumed knowledge
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.
Micro-credential stack information
This micro-credential is undertaken as a stand-alone course.
Details
Course Code: DATA03
Workload: 22 hours
- Contact hours: 7 hours
- Individual study and assessment: 15 hours
ANU unit value: 1 unit
AQF Level: 8
Contact: Associate Professor Naomi Priest
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
Description
The aim of this micro-credential is to equip students with the skills and knowledge to analyse existing data to create new social science and policy insights.
Topics
- The concept and practice of statistical hypothesis tests
- Descriptive statistics and distributional analysis
- Introducing multivariate analysis – linear regression
- Extending multivariate analysis – non-linear regression
- The power and practice of longitudinal data analysis
Learning outcomes
Upon successful completion, enrollee's will have the knowledge and skills to:
- Explain the key concepts of data analysis
- Outline the strengths and weaknesses of existing datasets from an analysis perspective
- Outline a hypothesis test and explain the use of null and alternative hypotheses, as well as one and two-sided tests
- Identify the appropriate analytical technique for different types of variables
- Discuss some of the main assumptions underlying different techniques
- Design or critique an analysis plan
Indicative assessment
Assignment 1 – Introductions and identification of data analysis questions (500 words, 20% of final mark) LO: 1, 2
Assignment 2 – Analysis plan (1,500 words, 80% of final mark) LO: 3, 4, 5, 6
Assumed knowledge
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.
Micro-credential stack information
This micro-credential is undertaken as a stand-alone course.
Details
Course Code: DATA05
Workload: 21 hours
- Contact hours: 8 hours
- Individual study and assessment: 13 hours
ANU unit value: 1 unit
AQF Level: 8
Contact: Maria Jahromi
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
Description
This micro-credential builds upon data manipulation and visualisation skills to cover statistical principles of good study design and data analysis using statistical modelling in the context of clinical research. Enrollees will gain experience exploring patterns in data and inferring relationships between variables. Common misuses of data analysis, including “p-hacking” and overfitting, will be discussed in depth. The course will emphasise the importance of reproducible analyses, and enrolees will learn good practice through the creation of a reproducible analysis workflow using Rmarkdown.
Learning outcomes
Upon successful completion, enrolees will have the knowledge and skills to:
- Explore datasets within the R environment
- Apply statistical models to infer treatment effects in a randomised controlled clinical trial
- Interpret and presents the results of data analyses
- Apply principles of good study design in clinical research
- Build a data analysis workflow
Indicative assessment
Enrolees will critically assess a published work for which the data has been made available. They will import the accompanying data into R, and demonstrate they can interpret the main findings, and reproduce aspects of the analysis (approximately 1000 words total).
Assumed knowledge
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.
Micro-credential stack information
This micro-credential may be undertaken as a stand-alone course or as part of a stack including:
Escape from Excel: Data Wrangling and Visualisation in the Health and Environmental Sciences using R
Details
Course Code: DATA08
Workload: 21 hours
- Contact hours: 7 hours: In person sessions: 9, 16, 23 September 2 - 4.30pm
- Individual study and assessment: 14 hours
ANU unit value: 1 unit
AQF Level: 9
Contact: Dr Terry Neeman and Professor Eric Stone
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
Description
The aims of this micro-credential is to equip enrollees with the skills and knowledge to extend their survey data analysis experience beyond that which is taught in Data analysis and interpretation, with a focus on longitudinal data analysis.
Topics
- Differencing
- Fixed and random effects data analysis
- Controlling for sample attrition in longitudinal data analysis
Learning outcomes
Upon successful completion, enrollee's will have the knowledge and skills to:
- Explain the key concepts of longitudinal data analysis
- Outline the strengths and weaknesses of existing longitudinal datasets from an analysis perspective
- Identify the appropriate analytical technique for longitudinal data analysis
- Discuss some of the main assumptions underlying different techniques
- Design or critique an analysis plan
Indicative assessment
Assignment 1 – Introductions and identification of data analysis questions (500 words, 20% of final mark) LO: 1, 2
Assignment 2 – Analysis plan (1,500 words, 80% of final mark) LO: 3, 4, 5, 6
Assumed knowledge
Completion of ANU Micro-credential Using Longitudinal Studies to Inform Public Policy and Data Analysis and Interpretation (or equivalent).
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.
Micro-credential stack information
This micro-credential may be undertaken as part of a stack by completing Using Longitudinal Studies to Inform Public Policy and Data Analysis and Interpretation.
Details
Course Code: DATA25
Workload: 21 hours
- Contact hours: 7 hours
- Individual study and assessment: 14 hours
ANU unit value: 1 unit
AQF Level: 8
Contact: Professor Nicholas Biddle
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
Description
This micro-credential provides a broad overview of the theory of qualitative research, and examines the basic skills involved in the application of these methods in social research, demography and population studies. Qualitative methods are defined, and their uses and limitations explored. Qualitative methods are compared with quantitative methods, and approaches to the integration of qualitative data are reviewed.
Learning outcomes
Upon successful completion, enrollee's will have the knowledge and skills to:
- Discuss the basic concepts of qualitative data collection;
- Critique existing qualitative research;
- Design a simple qualitative research project;
- Know the basic concepts for more advanced qualitative research and where to obtain further information
Indicative assessment
Assignment 1 – Introductions and identification of research questions (500 words, 20% of final mark) LO: 1, 2
Assignment 2 – Research design (1,500 words, 80% of final mark) LO: 3, 4
Assumed knowledge
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.Micro-credential stack information
This micro-credential may be undertaken as a stand-alone course.
Details
Course Code: DATA29
Workload: 21 hours
- Contact hours: 7 hours
- Individual study and assessment: 14 hours
ANU unit value: 1 unit
AQF Level: 8
Contact: Professor Nicholas Biddle
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
Description
The aim of this course is to equip enrollees with the skills and knowledge to engage with empirical research either directly as a researcher, or as a policy maker critically engaging with the most recent research.
Topics
- Designing a quantitative research project
- Incorporating qualitative insights
- Principles of sampling
- Principles of survey design
- Using administrative and linked datasets
Learning outcomes
Upon successful completion, enrollee's will have the knowledge and skills to:
- Specify a research question related to the policy process that is answerable using empirical methods
- Communicate and critique existing research in a rigorous manner
- Understand the assumptions, strengths and limitations of the main empirical techniques for policy design
- Understand the different forms of sampling design and their strengths/limitations
- Design or critique a survey or data collection methodology
Indicative assessment
Assignment 1 – Introductions and identification of research question (500 words, 20% of final mark) LO: 1, 2
Assignment 2 – Research design (1,500 words, 80% of final mark) LO: 3, 4, 5
Assumed knowledge
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.
Micro-credential stack information
This micro-credential may be undertaken as a stand-alone course.
Details
Course Code: DATA16
Workload: 22 hours
- Contact hours: 7 hours
- Individual study and assessment: 15 hours
ANU unit value: 1 unit
AQF Level: 8
Contact: Dr Intifar Chowdhury
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
Description
Inference from social and population surveys rely on a sufficiently large sample of respondents that are representative of the population of interest. Some biases in sample selection can be adjusted for after survey completion (using weights), but biases based on unobserved characteristics cannot be. Regardless, the more representative the sample, the more likely it is that researchers will be able to accurately make inference for the population of interest. Larger sample sizes have less uncertainty around their estimates, but this comes at the costs of interviewer/recruitment time and respondent burden. There are a number of ways to select samples including simple random samples, stratified samples, clustered samples, or non-probability methods.
Topics
- Sampling methodology with a focus on practical ways to design a sample recruitment strategy
- Critique a sampling method that has been used on a pre-existing survey
Learning outcomes
Upon successful completion, enrollee's will have the knowledge and skills to:
- Discuss the basic concepts of sampling for social and population surveys
- Critique sampling methodologies for existing surveys
- Design a simple sampling strategy that balances costs and error
- Know the basic concepts for more advanced sampling strategies and where to obtain further information
Indicative assessment
Assignment 1 – Introductions and identification of research questions (500 words, 20% of final mark) LO: 1, 2
Assignment 2 – Research design (1,500 words, 80% of final mark) LO: 3, 4
Assumed knowledge
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.Micro-credential stack information
This micro-credential may be undertaken as a stand-alone course.
Details
Course Code: DATA28
Workload: 21 hours
- Contact hours: 7 hours
- Individual study and assessment: 14 hours
ANU unit value: 1 unit
AQF Level: 8
Contact: Professor Nicholas Biddle
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
Description
The aims of this micro-credential is to equip enrollees with the skills and knowledge to extend their survey data analysis experience beyond that which is taught in Data Analysis and Interpretation.
Topics
- Interaction terms and non-linear models for continuous variables
- Analysing non-linear dependent variables
- Analysing multi-level datasets
- Cluster and factor analysis
Learning outcomes
Upon successful completion, enrollee's will have the knowledge and skills to:
- Explain the key concepts of survey data analysis
- Outline the strengths and weaknesses of existing datasets from an analysis perspective
- Identify the appropriate analytical technique for complex data analysis
- Discuss some of the main assumptions underlying different techniques
- Design or critique an analysis plan
Indicative assessment
Assignment 1 – Introductions and identification of data analysis questions (500 words, 20% of final mark) LO: 1, 2
Assignment 2 – Analysis plan (1,500 words, 80% of final mark) LO: 3, 4, 5, 6
Assumed knowledge
Completion of ANU Micro-credential Data Analysis and Interpretation (or equivalent).
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.
Micro-credential stack information
This micro-credential may be undertaken as part of a stack by completing Data Analysis and Interpretation.
Details
Course Code: DATA24
Workload: 21 hours
- Contact hours: 7 hours
- Individual study and assessment: 14 hours
ANU unit value: 1 unit
AQF Level: 8
Contact: Professor Nicholas Biddle
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
Description
The total survey error (TSE) paradigm is a useful rubric for both designing surveys and understanding strengths and weaknesses of existing surveys. The aim of this micro-credential is to provide a thorough grounding in survey error for the evaluation of existing surveys and planning of survey data collection. Enrollees will develop their skills by reviewing survey errors for an existing survey.
Learning outcomes
Upon successful completion, enrollee's will have the knowledge and skills to:
- Understand various sources of survey error
- Review and critique the methodology of existing surveys, grounded on the TSE paradigm
- Take survey error into consideration when undertaking or contracting for data collection
Indicative assessment
Assignment 1 – Identification of existing survey for review (500 words, 20% of final mark)
Assignment 2 – Review and critique of existing survey (1,500 words, 80% of final mark)
Assumed knowledge
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.
Micro-credential stack information
This micro-credential may be undertaken as a stand-alone course.
Details
Course Code: DATA27
Workload: 21 hours
- Contact hours: 7 hours
- Individual study and assessment: 14 hours
ANU unit value: 1 unit
AQF Level: 8
Contact: Associate Professor Benjamin Phillips
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
Description
The aim of this course is to equip students with the skills and knowledge to extend their data analysis experience beyond that which is taught in Data Analysis and Interpretation.
Topics
- Constructing time series datasets
- Analysing trends and seasonality
- Forecasting and predicting future outcomes
Learning outcomes
Upon successful completion, enrollee's will have the knowledge and skills to:
- Explain the key concepts of time series data analysis
- Outline the strengths and weaknesses of existing time series datasets from an analysis perspective
- Identify the appropriate analytical technique for time series data analysis
- Discuss some of the main assumptions underlying different techniques
- Design or critique an analysis plan
Indicative assessment
Assignment 1 – Introductions and identification of data analysis questions (500 words, 20% of final mark) LO: 1, 2
Assignment 2 – Analysis plan (1,500 words, 80% of final mark) LO: 3, 4, 5, 6
Assumed knowledge
Completion of ANU Micro-credential Data Analysis and Interpretation (or equivalent).
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.
Micro-credential stack information
This micro-credential may be undertaken as part of a stack by completing Data Analysis and Interpretation.
Details
Course Code: DATA22
Workload: 21 hours
- Contact hours: 7 hours
- Individual study and assessment: 14 hours
ANU unit value: 1 unit
AQF Level: 8
Contact: Professor Nicholas Biddle
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
Description
Longitudinal studies are powerful tools to establish causality, track and predict outcomes of people over time however they can be complex and difficult use to inform policy. The aim of this micro-credential is to equip enrollees with the applied skills and knowledge to access and use information from longitudinal studies to inform public policy as a researcher, or as a policy maker.
Topics
- Types of longitudinal studies and how they can inform public policy
- Contemporary longitudinal studies in Australia
- Designing a research project using longitudinal data to answer a policy question
- Principles of longitudinal data analyses of surveys and administrative data
Learning outcomes
Upon successful completion, enrollee's will have the knowledge and skills to:
- Specify a research question related to the policy process that is answerable using longitudinal studies
- Communicate and critique existing research in a rigorous manner
- Understand the assumptions, strengths and limitations of the main empirical techniques for longitudinal analysis
- Understand the different forms of longitudinal studies and their strengths/limitations
- Design or critique a secondary data analysis project
Indicative assessment
Assignment 1 – Introductions and identification of research question that uses longitudinal data (500 words, 20% of final mark) LO: 1, 2
Assignment 2 – Research design (500 words, 80% of final mark) LO: 3, 4, 5
Assumed knowledge
This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.
Micro-credential stack information
This micro-credential is undertaken as a stand-alone course.
Details
Course Code: DATA21
Workload: 22 hours
- Contact hours: 7 hours
- Individual study and assessment: 15 hours
ANU unit value: 1 unit
AQF Level: 8
Contact: Associate Professor Ben Edwards
This Micro-credential is taught at a graduate level. This is not an AQF qualification.
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