Course Title: Applied Analytics
Part A: Course Overview
Course Title: Applied Analytics
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
MATH1324 |
City Campus |
Postgraduate |
145H Mathematical & Geospatial Sciences |
Face-to-Face |
Sem 1 2007, Sem 1 2008, Sem 1 2009, Sem 1 2010, Sem 1 2011, Sem 1 2012, Sem 1 2013, Sem 1 2014, Sem 2 2014, Sem 1 2015, Sem 2 2015, Sem 1 2016, Sem 2 2016 |
MATH1324 |
City Campus |
Postgraduate |
171H School of Science |
Face-to-Face |
Sem 1 2017, Sem 2 2017, Sem 1 2018, Sem 2 2018, Sem 1 2019, Sem 2 2019, Sem 1 2020, Sem 2 2020, Sem 1 2021, Sem 2 2021, Sem 1 2022, Sem 2 2022, Sem 1 2023, Sem 2 2023, Sem 1 2024, Sem 2 2024, Sem 1 2025 |
Flexible Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
MATH2406 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JanJun2020 (KP1) |
MATH2406 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JulDec2020 (TP5) |
MATH2406 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JanJun2022 (KP1), JanJun2022 (KP3) |
MATH2406 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JulDec2022 (KP5) |
MATH2406 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JanJun2023 (KP1), JanJun2023 (KP3) |
MATH2406 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JulDec2023 (KP5) |
MATH2406 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JanJun2024 (KP1), JanJun2024 (KP3) |
MATH2406 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JulDec2024 (KP5) |
MATH2406 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JanJun2025 (KP3) |
Course Coordinator: Dr. Laleh Tafakori
Course Coordinator Phone: +61 (03) 9925
Course Coordinator Email: laleh.tafakori@rmit.edu.au
Course Coordinator Availability: By appointment or email
Pre-requisite Courses and Assumed Knowledge and Capabilities
Required Prior Study
You should have satisfactorily completed following course/s before you commence this course.
Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.
Contact your course coordinator if you think you may be eligible for recognition of prior learning.
Assumed Knowledge
Students should have a working knowledge of programming language, R and applied business mathematics.
Course Description
You will be introduced to fundamental statistical concepts and modern statistical practice, as used in data analysis. You will study statistical data investigations, summary statistics, data visualisation and probability as a measure for uncertainty. You will then build upon these topics and learn about sampling, sampling distributions and confidence intervals as the basis for statistical inference, and decision making. You will generate hypotheses for different types of data. You will leave the course being able to interpret statistical outputs in an analytic or data science context.
Objectives/Learning Outcomes/Capability Development
This course contributes to the following Program Learning Outcomes (PLOs) for the following:
- MC004 Master of Statistics and Operations Research
- MC242 Master of Analytics
- GC173KP19 Graduate Certificate of Data Science
- MC274 Master of Data Science Strategy and Leadership
Personal and professional awareness
- the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
- the ability to reflect on experience and improve your own future practice
- the ability to apply the principles of lifelong learning to any new challenge.
Knowledge and technical competence
- an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.
Problem-solving
- the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
- an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.
On successful completion of this course, you should be able to:
- Perform data pre-processing steps.
- Critically evaluate data sets to appropriately analyse data.
- Use relevant open-source programming language, R to perform fundamental statistical analyses and support communication and visualisation of key results.
- Interpret results accurately and minimise bias in sampling data.
Overview of Learning Activities
This course uses highly structured learning activities to guide your learning and prepare you to complete the assessment tasks. These activities consist of a combination of individual, peer-supported and facilitator-guided activities, and where possible project-led, with opportunities for regular feedback.
Authentic and industry-relevant learning is critical to this course as you will be expected to critically evaluate current thinking and practice within this discipline. You will apply your thinking by producing relevant real-world assessment tasks and engage with scenarios and case studies.
You will be expected to participate in class and group activities, as well as provide and receive peer feedback on drafts of work as social learning is an important component of this course.
Overview of Learning Resources
RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.
There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.
Overview of Assessment
Assessment Tasks
Assessment Task 1 : Statistical Data Analysis Project
Weighting 25%
This assessment task supports CLOs 1 & 3
Assessment Task 2 : Applied Data Project
Weighting 35%
This assessment task supports CLOs 2, 3 & 4
Assessment Task 3: Practical Data Analysis
Weighting 40%
This assessment task supports CLOs 1, 2, 3 & 4
If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.