Data Science Applications
Enrolments for this year have closed. Keep exploring subjects.
- 27 Jul 2020
This research-based university in Perth has a strong interdisciplinary focus and a reputation for outstanding teaching and ground-breaking research. With more than 22,000 students and 2,000 staff from over 90 countries, and campuses in Dubai and Singapore, Murdoch embraces free thinking, shared ideas and knowledge to make a difference, and Open Universities Australia is certainly part of that.
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Upon completion of this subject, students should be able to:
establish basic statistical techniques relevant with data science;
apply basic data analysis methods and predictive modelling that are appropriate to individual datasets and interpret the results;
propose the basic ideas and techniques behind modern data science applications;
apply knowledge in data pre-processing, visualization and analysis using R.
- Introduction to data science and big data
- Data pre-processing
- Data exploration and visualization
- Introduction to basic statistical and machine learning techniques: regression, classification and clustering
- Popular data science applications from fundamentals, such as medical decision making, recommender systems, web search
- Introduction to R
In order to enrol in this subject, you must be accepted into one of the following degrees:
No special requirements
The objective of this subject is to introduce important concepts in data science such as preparing data, visualizing data, extracting hidden patterns via exploratory data analysis, building predictive models and to help students put the learned knowledge into a real-world data science context. The subject will also develop an understanding of common applications in different domains that build upon data science.
Please Note: All students studying at Murdoch University will need to complete the compulsory unit, Murdoch Academic Passport (MAP100), which only takes 2-3 hours to complete online. Find out more: http://goto.murdoch.edu.au/MurdochAcademicPassport.
- Problem Solving Tasks (30%%)
- Data Analysis Project (30%%)
- Final Online Examination (40%%)
The Data Science Design Manual
Skiena, S. S.
Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist