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Note: Data Science is not available in 2022
Course description
Data Science Applications covers the fundamental principles of data science concepts and introduces the student to some of its common tools, methodologies and visualisations. Students will learn how to extract knowledge from data through hands-on experience with common data science programming tools and methodologies. They will create data visualisations to conduct exploratory and confirmatory data analysis, while gaining an appreciation of the breadth of data science applications and their potential value across disciplines.
On completion of this course students should be able to:
- differentiate between common data science algorithms and identify their appropriate application
- create a reproducible data science project report which includes: all relevant data files, data processing code, visualisations, analyses, reasoning and conclusions
- evaluate data science problems
- apply the appropriate data analyses and problem-solving skills for the successful completion of data science project
- plan and execute a data science project
- demonstrate academic and professional literacy by applying computer and mathematical skills to analyse algorithms and data structures
- understand that coding refers to computer programming, where a high level programming language is used to instruct a computer device to perform certain functions
- appreciate that high level languages are similar to spoken languages but have special commands that are understood by an interpreter (coder) to enable a computer’s central processor to understand them
- apply coding and computational thinking skills essential for their future careers
- appreciate that ICT is being used and embedded in all careers and in all aspects of life.
Fees
*Schools affiliated with
The Learning Place
Note that the fee is charged once only per course. The course can be studied across multiple year levels with no additional charges.
Course modules
| Module 1 | Introduction to Data Science |
| Module 2 | Introduction to 'R' |
| Module 3 | Intro to Data Analysis |
| Module 4 | Chance and Uncertainty |
| Module 5 | Distributions of Random Variables |
| Module 6 | Regression |
| Module 7 | Principal Component Analysis (PCA) |
| Module 8 | Discriminant Function Analysis (DFA) |
| Module 9 | Cluster Analysis (CA)
|
Assessment
Two Assignments, one after Module 3 and one after Module 6. Final Examination after Module 9. Final Examination is in 2 parts:
- Part A—70 mins duration
- Part B—70 mins duration
Part A assesses basic knowledge. Passing Part A is equivalent to receiving a grade of C. Part B questions are more advanced and help to differentiate the students for A and B grading.
Quizzes are used to monitor the student’s learning and progress in the course. Students complete a quiz after every module.
Course prerequisites
| Highly capable students in mathematics | Capable students in mathematics | Students with a profound interest in Statistics and Data |
Future directions
Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems with a curiosity to explore what problems need to be solved. The typical duties of are data scientist might include the following:
- collecting large amounts of data and transforming it into a more usable format
- solving business-related problems using data-driven techniques
Working with a variety of programming languages, including SAS, R and Python - having a solid grasp of statistics, including statistical tests and distributions
Staying on top of analytical techniques such as machine learning, deep learning and text analytics - communicating and collaborating with both IT and business
- looking for order and patterns in data, as well as spotting trends that can help drive business.
How to apply
To apply for this course you will need the following information:
- student contact details
- parent's contact details
- previous school results
- school contact details
- supervisor's contact details
- supervisor to agree to the Supervisor's Declaration (in the enrolment form, at time of submission).
Students need to have a supervisor who will read and agree to the supervisor's declaration. A supervisor is someone who we can contact for queries relating to assessment and progress. Your supervisor will have to set up and supervise your supervised assessment items.
Contact us for more information about Senior Data Science.