MATHS 7203OL - Applied Data Science and Mathematics

Online - Online Teaching 4 - 2023

In this course, you will be introduced to the role and application of data science in modern organisations and societies and the fundamental mathematical tools used by data scientists. The course includes processes for data collection, analysis, verification and validation. The mathematical tools include functions and graphs, and basic concepts of calculus related to probability and statistics.

  • General Course Information
    Course Details
    Course Code MATHS 7203OL
    Course Applied Data Science and Mathematics
    Coordinating Unit Sciences, Engineering & Technology Faculty Office
    Term Online Teaching 4
    Level Postgraduate Coursework
    Location/s Online
    Units 3
    Available for Study Abroad and Exchange N
    Incompatible DATA 7202OL
    Assumed Knowledge Stage 1 Mathematical Methods
    Course Staff

    Course Coordinator: Dr John Maclean

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    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
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.


    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.


    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.


    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.


    Attribute 5: Intercultural and ethical competency

    Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.


    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.


    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

  • Learning & Teaching Activities
    Learning & Teaching Modes
    Online supported by tutorials/discussions.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    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.
    Learning Activities Summary

    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.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    See Assessment Detail below. 
    Assessment Detail
    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.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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