CORPFIN 2503 - Business Data Analytics

North Terrace Campus - Semester 2 - 2022

The purpose of this course is to improve students analytical skills by covering the fundamentals of data-driven decision making. Students will first be introduced to the computing and programming skills necessary to solve business analytic problems. These include the essentials of data warehousing and data wrangling, as well as providing an overview of cybersecurity and the ethics of handling data. Students will have the ability to apply these skills to real-world business issues.

  • General Course Information
    Course Details
    Course Code CORPFIN 2503
    Course Business Data Analytics
    Coordinating Unit Adelaide Business School
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites ECON 1008
    Course Description The purpose of this course is to improve students analytical skills by covering the fundamentals of data-driven decision making. Students will first be introduced to the computing and programming skills necessary to solve business analytic problems. These include the essentials of data warehousing and data wrangling, as well as providing an overview of cybersecurity and the ethics of handling data. Students will have the ability to apply these skills to real-world business issues.
    Course Staff

    Course Coordinator: Mr Pide Lun

    Course Instructors

    Shihe Li

    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. Explain the background of business analytics.
    2. Demonstrate programming methods to locate, warehouse and wrangle data.
    3. Utilise data-driven means to build solutions for business queries.
    4. Relate ethical principles to the collection, storage and use of data.

    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.

    1-4

    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.

    1-4

    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.

    1-4

    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.

    1-4

    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.

    1-4

    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.

    1-4
  • Learning Resources
    Recommended Resources
    (e-book version) Camm, J. D., Cochran, J. J., Fry, M. J. and Ohlman, J. W., (2020). Business Analytics (4th ed.). Boston, MA: Cengage Learning. Enrolled students may be eligible for a discounted price offered by Cengage.

    DataCamp.com. Available at https://www.datacamp.com

    Wickham, H. and Grolemund, G., (2017). R for Data Science. Available at https://r4ds.had.co.nz/
    Online Learning
    Please refer to the MyUni course website for online resources used in the course.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The approach in this course is to first establish the basic knowledge of analytical tools (e.g., SAS® software) and then to build upon these to analyse real-world issues. This will be done through lectures, tutorials, assignment, mid-term test, and examination.
    Workload

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

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements. The University expects full-time students (i.e. those taking 12 units per semester) to devote a total of 48 hours per week to their studies. This means that you are expected to commit approximately 9 hours for a three-unit course or 13 hours for a four-unit course, of private study outside of your regular classes. Students in this course are expected to attend all lectures throughout the semester plus one tutorial class each week.
    Learning Activities Summary

    Week

    Topic

    Required reading*

    Learning activities

    1

    Introduction to data analytics and data handling

    Ch. 1, 2, 3, 4

    Lecture and workshop activities

    2

    Visual analytics and data mining

    Ch. 5

    Lecture and workshop activities

    3

    Descriptive statistics and data exploration

    Ch. 6, 7, 8

    Lecture and workshop activities

    4

    Applications of linear regressions

    Ch. 9, 10

    Lecture and workshop activities

    5

    Applications of logit and probit models

    Ch. 11

    Lecture and workshop activities

    6

    Applications of other discrete models

    TBA

    Lecture and workshop activities

    7

    Mid-term test

    8

    Monte-Carlo simulations

    TBA

    Lecture and workshop activities

    9

    Time-series analysis

    Ch. 12 (pp. 441-465)

    Lecture and workshop activities

    10

    Forecasting

    Ch. 12 (pp. 465-507)

    Lecture and workshop activities

    11

    Text analytics

    Xiang et al. (2015)

    Lecture and workshop activities

    12

    Big data

    Ch. 13

    Lecture and workshop activities

  • 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
    Assessment Task Task Type Due Weighting Learning Outcome
    DataCamp Individual

    Week 4

    10% 2
    Online Test 1 Individual Week 7 25% 1, 2
    Group Assignment Group Week 9 - Week 13 30% 1, 2, 3, 4
    Online Test 2 Individual Week 13 35% 3, 4
    Assessment Detail

    No information currently available.

    Submission
    Further details will be provided on MyUni.

    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
  • Policies & Guidelines
  • Fraud Awareness

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