« Business and Economics

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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.

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