DATA 7202OL - Applied Data Science

Online - Online Teaching 6 - 2022

An introduction to the role and application of data science in modern organisations and society. Case studies will be used to demonstrate current best practice as well as common pitfalls. Processes for data collection, analysis, verification and validation. The use of data for modelling, prediction and decision support. An overview of widely used tools for data analysis and modelling.

  • General Course Information
    Course Details
    Course Code DATA 7202OL
    Course Applied Data Science
    Coordinating Unit School of Computer Science
    Term Online Teaching 6
    Level Postgraduate Coursework
    Location/s Online
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange N
    Restrictions Graduate Certificate in Data Science (Applied) OL, Graduate Diploma in Data Science (Applied) OL OR Master of Data Science (Applied) OL Only
    Course Description An introduction to the role and application of data science in modern organisations and society. Case studies will be used to demonstrate current best practice as well as common pitfalls. Processes for data collection, analysis, verification and validation. The use of data for modelling, prediction and decision support. An overview of widely used tools for data analysis and modelling.
    Course Staff

    Course Coordinator: Associate Professor Nickolas Falkner

    Associate Professor Nickolas Falkner
    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    Upon completion of this course/subject, students will be able to:
    1. Recommend methodologies for the use of data science in business/ in modern organisations and societies
    2. Evaluate data science use to describe best practice in modern organisations and societies
    3. Analyse issues associated with the use of data for solving complex problems
    4. Evaluate the tools used in the data science community for reporting on data analysis.
    5. Critique data science solutions against recommended methodologies and best practice, identifying areas for improvement.
    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.

    1,3,4,5

    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.

    2,4,5

    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.

    1,3,5

    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.

    3,5

    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.

    1,5

    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.

    5
  • Learning Resources
    Required Resources
    There are no required resources beyond meeting course pre-requisites.
    Recommended Resources
    Students are encouraged to have access to the programming environment that they used for the earlier courses, for Python. Beyond that, there are no recommended texts or software.
    Online Learning
    All material is available online from the University's MyUni Learning Management System. The material is available for two weeks prior to the course, in readonly mode, and all weeks are published to allow students to plan.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    All course material and interactions take place in the on-line mode. While most of the course may be carried out asynchronously, scheduled tutorial activities are held at defined times.
    Workload

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

    Students are expected to carry out readings, assignment work, discussions, and material viewing. The majority of material will take approximately 15 hours a week, with an additional 50 hours spread over three assignments. The total commitment is 140 hours.
    Learning Activities Summary
    All learning activities are online. Each week contains recorded material, web pages, designated readings, discussion boards, and tutorial activities. The topics covered in each week are:

    Week One:

    Week one: Introduction to Data Sources

    1. Describe data sources used in current data science practice
    2. Detect practice appropriate data sources
    3. Describe the impact of using different data types
    4. Produce a plan of the resource requirements for a Data plan

    Week Two:

    Week two: Noisy Data and Reliability

    1. Identify data noise in a data context
    2. Describe the impact of data noise on analysis and decision making
    3. Select and apply tools and techniques to reduce the impact of noise or unreliability
    4. Produce a data cleaning plan that has clear outcomes for reliability

    Week Three:

    Week three: Analysis and Verification

    1. Consider analysis techniques used to analyse prepared data sets
    2. Differentiate the benefits and costs of different analysis techniques used to analyse data.
    3. Verify analysis standards in current data science practice.
    4. Produce a data analysis and verification plan for a prepared data set

    Week Four:

    Week four: Visualising Data

    1. Produce a visualise from analysed data using tools
    2. Critique the benefits of different visualisation techniques in current practice.
    3. Consider the narrative visualisation journey that occurs with/to data
    4. Produce a visualisation plan for the analysed data
    5. Week Five

    Week Five: Validation

    Week Five: Validating Your Results

    1. Determine the validation of results used for different data sets in the data science context
    2. Determine end-user requirements for data related results
    3. Construct a survey instrument to gather end-user feedback
    4. Produce a validation instrument to identify when end-users have validated the results

    Week Six

    Week Six: Prresentation

    1. Presenting Your Results for Decision Making
    2. Establish a full plan from data gathering to validation from an unseen data set
    3. Determine the role of iterative improvement in data science
    4. Produce an update plan for an existing data plan
    Specific Course Requirements
    There are no specific course requirements.
  • 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
    There are three assessment items, two reports and a peer review activity. These are spaced throughout the course.
    Assessment Related Requirements
    There are no additional requirements beyond the assignment specification.
    Assessment Detail

    Assessment Name 

     

    Due 

    Weighting 

    Learning Outcomes 

    Related Weeks 

    Assessment 1: Report Part A- (1500 words) 

    End of Week 3, 

    Sunday 11:59pm 

    40% 

    1-5 

    Week 1-3 

    Assessment 2: Discussion Board peer review 

    End of Weeks 2,3,4,5 

    Sunday 11:59pm 

    5% each –total of 20% 

    1-5 

    Week 2-5 

    Assessment 3: Report Part B-  (1500 words) 

    End of Week 6, 

    Sunday 11:59pm 

    40% 

    1-5 

    Weeks 1-6 

    Submission
    All assignment work is submitted through the MyUni Learning Management System.
    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 (http://www.adelaide.edu.au/policies/101/) 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

    Counselling for Fully Online Postgraduate Students

    Fully online students can access counselling services here:

    Phone: 1800 512 155 (24/7) 

    SMS service: 0439 449 876 (24/7) 

    Email: info@assureprograms.com.au

    Go to the Study Smart Hub to learn more, or speak to your Student Success Advisor (SSA) on 1300 296 648 (Monday to Thursday, 8.30am–5pm ACST/ACDT, Friday, 8.30am–4.30pm ACST/ACDT)

  • 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.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.