CORPFIN 2503 - Business Data Analytics II

North Terrace Campus - Semester 2 - 2018

The purpose of this course is to improve students? analytical skills. The course will provide students with the knowledge and skills to apply advanced quantitative and qualitative modelling techniques to analyse and develop solutions to contemporaneous business challenges. At the beginning of the course, students will learn the foundation of analytical tools. The students will apply them to analyse real-world issues. The course will conclude with the challenges and opportunities of the big data

  • General Course Information
    Course Details
    Course Code CORPFIN 2503
    Course Business Data Analytics II
    Coordinating Unit 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 N
    Assumed Knowledge ECON 1008 OR STATS 1000
    Course Description The purpose of this course is to improve students? analytical skills. The course will provide students with the knowledge and skills to apply advanced quantitative and qualitative modelling techniques to analyse and develop solutions to contemporaneous business challenges. At the beginning of the course, students will learn the foundation of analytical tools. The students will apply them to analyse real-world issues. The course will conclude with the challenges and opportunities of the big data
    Course Staff

    Course Coordinator: Dr Sigitas Karpavicius

    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. Collect the data from proprietary (e.g., Thompson Reuters Eikon) and public sources
    2. Process the data and prepare it for further analysis
    3. Apply advanced quantitative and qualitative modelling techniques using SAS® and NVIVO® software
    4. Critically interpret the obtained quantitative and qualitative results.
    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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1-4
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    1-4
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    1-4
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    1-4
    Intercultural and ethical competency
    • adept at operating in other cultures
    • comfortable with different nationalities and social contexts
    • Able to determine and contribute to desirable social outcomes
    • demonstrated by study abroad or with an understanding of indigenous knowledges
    1-4
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
    1-4
  • Learning Resources
    Required Resources
    Text book: Konasani, V. R. and Kadre, S. (2015). ‘Practical Business Analytics Using SAS: A Hands-on Guide’ (a free electronic copy is available from the library).

    Computer: Students will need a computer with ‘SAS® University Edition’ (can be downloaded for free from www.sas.com) and ‘NVIVO®’ (a free licence can be obtained from the University’s website) installed.
  • 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

    Ch. 1, 2

    Lecture and tutorial activities

    2

    Data handling and processing

    Ch. 3, 4

    Lecture and tutorial activities

    3

    Visual analytics and data mining

    Ch. 5

    Lecture and tutorial activities

    4

    Descriptive statistics and data exploration

    Ch. 6, 7

    Lecture and tutorial activities

    5

    Statistical tests

    Ch. 8

    Lecture and tutorial activities

    6

    Applications of linear regressions

    Ch. 9, 10

    Lecture and tutorial activities

    7

    Mid-term test

    8

    Applications of logit and probit models

    Ch. 11

    Lecture and tutorial activities

    9

    Event studies

    TBA

    Lecture and tutorial activities

    10

    Forecasting

    Ch. 12

    Lecture and tutorial activities

    11

    Text analytics

    TBA

    Lecture and tutorial activities

    12

    Big data

    Ch. 13

    Lecture and tutorial 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
    Weighting
    Mid-term test 20%
    Group Assignment 30%
    Final Examination 50%
    Assessment Detail
    The assessment components are as follows:
    1) There will be a mid-semester test held in Week 7. Further details will be announced on MyUni.
    2) Group assignment involves analysing contemporaneous business problems using advanced statistical methods and software.
    3) Final/Replacement Exam: 2-hour exam as per examination schedule.

    To gain a pass for this course, a mark of at least 50% overall needs to be obtained.
    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
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