On successful completion of this course students will be able to:
1. Recommend methodologies and mathematical tools for the use of data science in a modern organisation
2. Explain the relevance of functions, graphs of functions, and calculus to data science practice
3. Use data science to evaluate and describe best practice in modern organisations and societies
4. Analyse issues associated with the use of data for solving complex problems
5. Analyse issues associated with the use of data for solving complex problems

Online supported by tutorials/discussions.

Students are expected to spend 25 hours per week on this course. There will be 1-2 hours contact time for learning and teaching activities and students will be working individually and/or in groups 23-24 hours to carry out the required learning and teaching activities for acquiring the expected knowledge, understanding, and skills in this course.

Module 1: Data sources and mathematical functions in data science

Data Science depends upon the careful selection, analysis and presentation of data. The data sources chosen are key to achieving a successful outcome with whichever methods you use. Mathematics plays a fundamental role in the analysis and interpretation of data; in this module, you will be introduced to how graphs of functions are used in this process. By studying data sources and functions and graphs, you will have a solid foundation for your work in the rest of this course and later courses in your program of study.

Module 2: Noisy data, reliability, and special mathematical functions in data science 

Continuing the study of functions in data science that you began in Module 1, you will study some special functions that arise in data science and their properties: exponential and logarithmic functions, as well as trigonometric functions. You will also continue your study of data sources from Module 1. You will discover that some data sources may not be immediately useful, as there may be extraneous or corrupted information in the data itself. By studying noisy data and reliability, you will understand how to systematically prepare data for further analysis, in a way that doesn’t compromise future analysis.

Module 3: Analysis, verification and differential calculus in data science 

There are many techniques that can be used to analyse cleaned data, but you need to be confident that you have selected the correct method and that you have used it correctly. By studying analysis and verification, you will gain knowledge of several important techniques and be introduced to some standard verification approaches, which will give you confidence in the final reports you produce. In differential calculus, you will study the rate of change of a function through its derivative. You will be introduced to how to calculate derivatives and how to apply them to study graphs of functions and solve optimisation problems in data science.

Module 4: Visualising data and calculus in data science 

One of the most useful outcomes of data science is a powerful representation that makes it easier for people to understand data and the visualisation of data is often key to convincing people of truth that we have discovered through analysis. By studying visualisation, you will be able to identify and select visualised representations of data sets that will clearly indicate points of interest and key issues. You will also gain knowledge in how to apply the rules of calculus to calculate derivatives to solve data science problems in your continued studies of differential calculus.

Module 5: Validating results and integration in data science

Ultimately, any data science work needs to be accepted as a good solution to a given problem and the validation step is in place to look at our process, so that we can have high confidence that our answer to the overall problem is good. By studying validation, students will gain valuable insight into the entire process and reflect on the role that their choices have made in reaching this point. Continuing your study of integral calculus and its role in data science, you will gain knowledge in how to use the Fundamental Theorem of Calculus to evaluate definite integrals. You will also be introduced to basic probability theory and statistics, including its application to data science.

Module 6: Presenting results for decision making

Good presentation is crucial to communicating your results and the report that contains your results and visualisation is a tool to change people’s opinions and produce valuable insights. By studying presentation reports, you will bring together all your work to date and explain it to a potentially non-technical audience. This will develop your critical thinking and written communication, as well as validating earlier steps.

See Assessment Detail below. 

Assessment Name	Weighting	Due Date	Course Learning Outcomes	Related Weeks
Assessment 1: short answer questions	6% each (Total: 30%)	End of Weeks 1,2,3,4,5 (Sunday 11:59 pm)	1,2	Weeks 1-5
Assessment 2: Plan Part A (1500 words)	30%	End of Week 3 (Sunday 11:59 pm)	1,3,4	Weeks 1-3
Assessment 3: Report Part B (1500 words)	40%	End of Week 6 (Sunday 11:59 pm)	1,3,4,5	Weeks 1-6

Submission details for all activities are available in MyUni but the majority of your submissions will be online and may be subjected to originality testing through Turnitin or other mechanisms. You will receive clear and timely notice of all submission details in advance of the submission date.