Details

Session 1: Self-study Period (weeks 1-3)
        Students will work through the textbook and practice problems, as well as watch videos.
Session 2: In-person period (week 4)
        Monday 29 November: Online sessions 10am-12pm and 1pm-3pm
        Tuesday 30 November: Online sessions 10am-12pm and 1pm-3pm
        Wednesday 1 December: Online sessions 10am-12pm and 1pm-3pm
Session 3: Self-study period (weeks 5-6)
        Students will work through class notes and practice problems, as well as watch videos.

Description 

This micro-credential aims to facilitate an understanding of graphical representation of information.

Topics 

1. Introduction to the R statistical computing environment
2. Graphics environments and interactive graphics
3. Constructing graphics in R
4. Principles of graphic construction including examples of good and bad graphics
5. Constructing graphical representations for one dimensional data

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

1. Demonstrate basic knowledge of the R statistical computing language, particularly graphical capabilities
2. Explain and be able to apply the principles of good data representation
3. Explain and be able to use various graphics environments, interactive graphics and graphical objects
4. Construct graphical representations of one dimensional data

Indicative assessment 

Assignment 1: Presentation graphics (20%), 8-page limit, LO: 1 and 2.

Assignment 2: Project analysing a dataset (80%), 8-page limit, LO: 1, 2, 3 and 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 part of a stack by completing Graphical Data Analysis B.

Successful completion of Graphical Data Analysis A and B can lead to specified credit for the course STAT7026 Graphical Data Analysis.

Details 

Course Code: DATA30

Workload: 72 hours 

• Contact hours: 12 hours
• Individual study and assessment: 60 hours

ANU unit value: 3 units

Course Code Level: 7000

Contact: Jo Drienko (RSFAS DDE)

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

Details

Session 1: Self-study Period (weeks 1-3)
        Students will work through the textbook and practice problems, as well as watch videos.
Session 2: In-person period (week 4)
        Date TBA: In-person lecture and lab 10am-12pm and 1pm-3pm
        Date TBA: In-person lecture and lab 10am-12pm and 1pm-3pm
        Date TBA: In-person lecture and lab 10am-12pm and 1pm-3pm
Session 3: Self-study period (weeks 5-6)
        Students will work through class notes and practice problems, as well as watch videos.

Description 

This micro-credential aims to facilitate an understanding of graphical modelling and relationships.

Topics 

1. Constructing graphical representations for multivariate data
2. Construction and use of diagnostics plots when conducting statistical analysis of data
3. Constructing and interpreting graphical displays for dependent data

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

1. Demonstrate detailed knowledge of the R statistical computing language, particularly graphical capabilities
2. Construct graphical representations for multivariate data including scatterplots, and dynamic graphics
3. Use diagnostic plots when conducting statistical modelling to explore and refine statistical models for data, including detailed explanations of such use
4. Construct and interpret graphical displays for dependent data

Indicative assessment 

Assignment 1: Presentation graphics (20%), 8-page limit, LO: 1 and 2.

Assignment 2: Project analysing a dataset (80%), 8-page limit, LO: 1, 2, 3 and 4.

Assumed knowledge 

Graphical Data Analysis A is the prerequisite for Graphical Data Analysis B.

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 Graphical Data Analysis A.

Successful completion of Graphical Data Analysis A and B can lead to specified credit for the course STAT7026 Graphical Data Analysis.

Details 

Course Code: DATA31

Workload: 72 hours 

• Contact hours: 12 hours
• Individual study and assessment: 60 hours

ANU unit value: 3 units

Course Code Level: 7000

Contact: Jo Drienko (RSFAS DDE)

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

Details

Session 1: Self-study Period

        Students will work through the textbook and practice problems, as well as watch videos.

Session 2: In-person period

        Monday 21 June: In-person lecture and lab 10am-12pm and 2pm-4pm

        Tuesday 22  June: In-person lecture and lab 10am-12pm and 2pm-4pm

        Wednesday 23 June: In-person lecture and lab 10am-12pm and 2pm-4pm

Session 3: Self-study period

        Students will work through the textbook and practice problems, as well as watch videos.

Description 

This micro-credential aims to facilitate a basic understanding of statistical techniques used for the analysis of data. This micro-credential introduces students to the philosophy and methods of modern statistical data analysis and its probabilistic underpinnings. The micro-credential has a strong emphasis on computing and graphical methods, and uses a variety of real-world problems to motivate the theory and methods required for carrying out statistical data analysis. This micro-credential makes extensive use of the R statistical analysis package interfaced through R Studio.

Topics 

1. Descriptive statistics
2. Basics of probability
3. Discrete random variables
4. Continuous random variables
5. Sampling distributions

Learning outcomes 

Upon successful completion, enrolees will have the knowledge and skills to:

1. Demonstrate basic knowledge of the R statistical computing language
2. Summarise data numerically and through basic graphical representations
3. Solve problems using the principles of probability
4. Demonstrate an understanding of sampling distributions

Indicative assessment 

Assignment 1: 15%, LO: 1 and 2.

Assignment 2: 25%, LO: 1, 2 and 3.

Project: 60%,10-page limit, LO: 1, 2, 3, and 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 part of a stack by completing Statistics for Data Analysis B.

Successful completion of Statistics for Data Analysis A and B can lead to specified credit for the course STAT7055 Introductory Statistics for Business and Finance.

Details 

Course Code: DATA32

Workload: 72 hours 

• Contact hours: 12 hours
• Individual study and assessment: 60 hours

ANU unit value: 3 units

Course Code Level: 7000

Contact: Jo Drienko (RSFAS DDE)

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

Details

Session 1: Self-study Period

        Students will work through the textbook and practice problems, as well as watch videos.

Session 2: In-person period

        Monday 16 August: In-person lecture and lab 10am-12pm and 2pm-4pm

        Tuesday 17 August: In-person lecture and lab 10am-12pm and 2pm-4pm

        Wednesday 18 August: In-person lecture and lab 10am-12pm and 2pm-4pm

Session 3: Self-study period

        Students will work through the textbook and practice problems, as well as watch videos.

Description 

This micro-credential aims to facilitate a high level understanding of statistical techniques used in the analysis of data. This course introduces students to the philosophy and methods of modern statistical data analysis and inferential methods. The course has a strong emphasis on computing and graphical methods, and uses a variety of real-world problems to motivate the theory and methods required for carrying out statistical data analysis. This course makes extensive use of R statistical analysis package interfaced through R Studio.

Topics 

1. Hypothesis testing for one and two populations
2. Analysis of variance
3. Simple linear regression
4. Multiple linear regression

Learning outcomes 

Upon successful completion, enrolees will have the knowledge and skills to:

1. Demonstrate basic knowledge of the R statistical computing language
2. Conduct and explain the results of inferential procedures, including hypothesis tests and confidence intervals
3. Carry out and interpret an analysis of variance test and compare the difference between two or more sets of data
4. Apply and interpret simple and multiple regression models

Indicative assessment 

Assignment 1: 15%, LO: 1 and 2.

Assignment 2: 25%, LO: 1, 2, 3, and 4.

Project: 60%,10-page limit, LO: 1, 2, 3, and 4.

Assumed knowledge 

Statistics for Data Analysis A is the prerequisite for Statistics for Data Analysis B. Please note that pre-requisites may be waived based on previous experience. Get in touch with us for more information.

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 Statistics for Data Analysis B.

Successful completion of Statistics for Data Analysis A and B can lead to specified credit for the course STAT7055 Introductory Statistics for Business and Finance.

Details 

Course Code: DATA33

Workload: 72 hours 

• Contact hours: 12 hours
• Individual study and assessment: 60 hours

ANU unit value: 3 units

Course Code Level: 7000

Contact: Jo Drienko (RSFAS DDE)

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.