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 |
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) |
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
It is recommended that students have completed the following course/s before enrolling in Applied Analytics:
- MATH2405 Data Wrangling
- MATH2349 Data Wrangling
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.
Details of Learning Activities
This course uses highly structured learning activities to guide your learning process and prepare you for your assessments. The activities are a combination of individual, peer-supported and facilitator-guided activities, and where possible project-led, with opportunities for feedback throughout.
Authentic and industry-relevant learning is critical to this course to practice on real world problems. Social learning is another important component and you are expected to participate in class and group activities, share drafts of work and resources and give and receive peer feedback. You will be expected to work efficiently and effectively with others to achieve outcomes greater than those that you might have achieved alone.
Above all, the learning activities are designed to maximise the likelihood that you will not only understand the course learning resources but also apply that learning to improving your own practice, for example by producing real-world artefacts and engaging in scenarios and case studies.
Teaching Schedule
Please read the detail through the announcement in Canvas of this course.
Overview of Learning Resources
The learning and teaching approaches used in this program may include webinars, problem-based learning and case studies.
The activities and tasks are designed to facilitate the application of theory and encourage peer learning in a collaborative, open manner using online tools and interactive discussion forums. Assessment is integrated throughout the program to ensure that you graduate with a set of applicable skills and knowledge.
This course will use the statistical software package R and the RStudio integrated development environment. R and RStudio are free. Students will require R and RStudio to be installed on their personal computing device. Students will be notified of ways to access this software on campus computers and through online services. You may also wish to view key Mathematics, Statistics and Analytics resources via the library guide.
There are services available to support your learning via the RMIT University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services.
RMIT Online provides support and equal opportunities for students with a disability, long-term illness and/or mental health condition and primary carers of individuals with a disability. If you need assistance, please speak to your Program Manager or contact the Equitable Learning Services (ELS).
At RMIT you can apply for credit so your previous learning or experience counts toward your RMIT Online program. For further information on how to apply for credit, please click here.
Please view the Assessment and Assessment Flexibility Policy for further information regarding applying for an extension, special consideration, equitable assessment arrangements and supplementary assessment.
Overview of Assessment
This course has no hurdle requirements.
Assessment Tasks:
Assessment Task 1 : Statistical Data Analysis Project
Weighting 25%
This assessment task supports CLOs: 1 and 3.
Assessment Task 2 : Applied Data Project
Weighting 30%
This assessment task supports CLOs: 2, 3 and 4.
Assessment Task 3: Practical Data Analysis
Weighting 45%
This assessment task supports CLOs: 1, 2, 3 and 4.