Course Title: Mathematics for Computing 2
Part A: Course Overview
Course Title: Mathematics for Computing 2
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
MATH2412 |
City Campus |
Undergraduate |
171H School of Science |
Face-to-Face |
Sem 2 2023 |
Flexible Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
MATH2412 |
City Campus |
Undergraduate |
171H School of Science |
Face-to-Face |
UGRDFlex21 (All) |
MATH2412 |
City Campus |
Undergraduate |
171H School of Science |
Face-to-Face |
UGRDFlex22 (All) |
Course Coordinator: Assoc. Prof. Melih Ozlen
Course Coordinator Phone: NA
Course Coordinator Email: melih.ozlen@rmit.edu.au
Course Coordinator Availability: By appointment; by email
Pre-requisite Courses and Assumed Knowledge and Capabilities
None
Course Description
This course will introduce you to fundamental concepts in statistics and data analytics. You will learn data analysis and modelling, starting with descriptive statistics and data visualisation random variables, normal and binomial probability distributions. You will build upon these and learn how to perform statistical inference including hypothesis testing and confidence intervals. You will learn statistical modelling and analysis techniques including linear regression, analysis of variance categorical data analysis and nonparametric analysis. There will be an emphasis on conceptual understanding and the use of computer software for analytical computations.
Objectives/Learning Outcomes/Capability Development
This course contributes to the following Program Learning Outcomes (PLOs):
PLO1: Apply a broad and coherent set of knowledge and skills for developing data driven solutions for contemporary societal challenges.
PLO2: Apply systematic problem solving and decision making methodologies to identify, design and implement data driven solutions to real world problems, demonstrating the ability to work independently to self-manage processes and projects.
PLO4: Communicate effectively with diverse audiences, employing a range of communication methods in interactions.to both computing and non computing personnel.
Course Learning Outcomes (CLOs):
On successful completion of this course, you will be able to:
- Elucidate the concepts of probability and statistics, and identify questions requiring investigation.
- Carry out statistical data investigation including identifying variables and measures as well as proposing an analysis method that will answer the question posed.
- Visualise and summarise statistical data for further investigation.
- Apply statistical methods to explore, analyse, and model data and use these methods for testing statistical hypotheses.
- Use computer software for visualisation, analysis, and modelling of real-world data.
Overview of Learning Activities
Assigned readings and video lectures will provide the theoretical background of statistical concepts.
Tutorial questions, and exercises will demonstrate the use of software to perform statistical analysis matching the concepts already covered in readings and video lectures.
Practical assessments will provide you with the opportunity to assess your learning and get timely feedback on your progress.
Overview of Learning Resources
A list of prescribed and recommended textbooks for this course will be provided on Canvas. All course materials will be posted on Canvas, including lecture notes, tutorial materials, assessment details, teaching schedule, and staff contact details.
Overview of Assessment
This course has no hurdle requirements.
Assessment Tasks:
Assessment Task 1: Practical Assessments – Descriptive Statistics, Probability, Random variables, Confidence Intervals
Weighting 33%
This assessment task supports CLOs 1, 3, and 5.
Assessment Task 2 : Practical Assessments – Hypothesis Testing, Analysis of Variance, Simple Linear Regression
Weighting 33%
This assessment task supports CLOs 1, 2, 3, 4, and 5.
Assessment Task 3: Practical Assessments – Multiple Regression, Categorical Data Analysis, Nonparametric Analysis
Weighting 34%
This assessment supports CLO 1, 2, 3, 4, and 5.