Unit Code and Title DAT603 Data Analysis
Unit Code and Title DAT603 Data Analysis
Course(s) Master of Business
Credit Points 6 Credit Points
Duration 12 Weeks
AQF Level 9
Student Workload Students should expect to spend approximately 120 hours on learning activities across the study period. This includes time spent attending scheduled classes, undertaking individual study and completing assessments.
Mode of Delivery Face-to-face
Pre-Requisites Nil
Name of Unit Coordinator Dr. Zohreh Moghaddas
Contact Information Email: Zohreh@pia.edu.au
Consultation: Refer to your Moodle unit page for unit coordinators consultation time.
Unit Description
This unit is intended for students in business analytics disciplines. Emphasis is placed on the application of statistical techniques to analyze quantitative information in those disciplines. Topics may include populations and samples; the presentation and interpretation of data; introduction to business analytics, descriptive statistics; Data Visualization, Modeling Uncertainty, Statistical Inference; Linear Regression; Monte Carlo Simulation. Analyses will be carried out using R language.
Unit Learning Outcomes (ULOs)
On successful completion of this unit, students will be able to:
ULO1Develop a strong quantitative skill set for business decision-making.
ULO2Construct statistical models to examine the relationships between business variables, and exhibit proficiency in utilizing statistical software for quantitative modelling.
ULO3 Analyse problems that exist within real-world constraints and gather relevant data to aid in the decision-making process.
ULO4Recommend data visualisation solutions and present outcomes orally and verbally.
Learning and Teaching Approach
Learning and teaching in this unit applies the Institutes model of providing transformational learning experiences that are student-centred, collaborative, active, reflective and applied. Key themes embedded into the resources, challenges and assessments are ethical practice, sustainability, evidence-based decision making and real-world applications. Completion of the learning activities for each topic will give students the discipline knowledge and skills required to complete the assessments.
Successful completion of all assessments demonstrates that the unit learning outcomes have been achieved. Additional support to further enhance students academic skills is available from the Academic Enhancement team.
Expectations of Students
You are expected to:
Prepare for scheduled classes by completing assigned activities;
Attend all scheduled classes;
Actively participate in all class activities;
Seek clarification and advice from teaching staff as needed;
Submit assessments on time;
Review and reflect on feedback on assessments and seek clarification about feedback where needed; and
Notify the lecturer if you are unable to attend a class.
Schedule of Learning and Teaching Activities
Topic Learning Activities Readings
Week 1 Introduction to Business Analytics using R Concepts
Installing Packages
Data structures
Describe the types of data in business analytics.
Creating Distributions from Data
Modifying Data in R
Measures of Location in R
Measures of Variability in R Week 1 Slides
Week 2 Data Visualization using R Concepts
Summarize qualitative and quantitative data by constructing a frequency distribution.
Construct and interpret a pie chart and a bar chart.
Construct and interpret a histogram, a polygon, and an ogive.
Construct and interpret a scatterplot.
Identify the shape of the distribution for the house price data. Week 2 Slides
Week 3 Introduction to Bayesian using R Concepts
Bayes theorem in Business Analytics
Posterior density for p, by Bayes rule
Using a Discrete Prior
Using a Beta Prior
Using a Histogram Prior
Week 3 Slides
Week 4 Statistical Inference using R Concepts
Point Estimation
Sampling Distributions
Interval Estimation of the Population Mean
Interval Estimation of the Population Proportion
Developing Null and Alternative Hypothesis
Type I and Type II Errors
Hypothesis Test of the Population Mean
Hypothesis Test of the Population Proportion Week 4 Slides
Week 5 Estimation: Comparing two populations using R Concepts
Estimating the difference between two population means: Independent samples
Estimating the difference between two population means: Dependent samples matched pairs experiment
Week 5 Slides
Week 6 Hypothesis testing:
Comparing two populations using R Concepts
Testing a hypothesis about 1 2 when the population variances are known
Testing a hypothesis about 1 2 when the population variances are unknown
Testing the difference between two population means: Dependent samples matched pairs experiment
Week 6 Slides
Week 7 Analysis of variance using R Concepts
Single-factor analysis of variance: Independent samples (one-way ANOVA)
Variability between sample means
Sum of squares for treatments (SST)
Within-samples variability
Week 7 Slides
Week 8 Tests for nominal data: Chi-squared tests using R Concepts
Chi-squared test of a multinomial experiment
Chi-squared goodness-of-fit test
Factors that identify the chi-squared goodness-of-fit test
Chi-squared test for normality
Week 8 Slides
Week 9 Regression modeling using R Concepts
Linear regression
Residual analysis
Simple linear Regression Analysis
Use R to obtain the regression summary output.
write down the estimated simple linear regression equation.
Test the hypothesis for slope coefficient
95% confidence intervals for the estimated regression coefficients.
Multiple Regression analysis
write down the estimated multiple linear regression equation.
Week 9 Slides
Week 10 Multiple Linear Regression analysis using R Concepts
Multiple Regression analysis
write down the estimated multiple linear regression equation.
Residual analysis
Conditions Necessary for Valid Inference in the Least Squares Regression Model
Testing Individual Regression Parameters
Addressing Nonsignificant Independent Variables
Multicollinearity
Categorical Independent Variables
Interpreting the Parameters
More Complex Categorical Variables Week 10 Slides
Week 11 Model building using R Concepts
Polynomial models
Nominal independent variables
Variable selection methods
Model building Week 11 Slides
Week 12 Monte Carlo Simulation using R Concepts
Monte Carlo Simulation using a uniform distribution
Monte Carlo Simulation using Normal distribution
Monte Carlo Simulation using Beta distribution
Assessment Information
Assessment Task Weighting Due Length/ Duration Learning Outcome(s)
Assessment 1:
Invigilated Quizzes 35% -
W3 5%
W6 -10%
W9 10%
W12 10% Week 3, 6, 9 & 12 40-60Minuets 1, 2,3
Assessment 2: Group case scenario(s) for data analytics + In class/ face-to-face presentation 25% written report plus 10% presentation (video upload) Week 8 2000 words (or equivalent)
8-10Minutes 1, 2,3, 4
Assessment 3: Individual statistical case scenario analysis 30% written report Week 11 1500 words (or equivalent) 1, 2,3, 4
*Detailed information relating to each assessment in the Assessment Block on Moodle.
Grading
Each assessment and the final mark for the unit will be determined as follows:
Mark Grade
0% - 49% Fail
50% - 64% Pass
65% - 74% Credit
75% - 84% Distinction
85% and above High Distinction
Requirements to Pass a Unit
In order to pass the unit, you must:
Attempt all assessments;
Achieve a minimum of 50% of overall marks; and
Achieve a minimum of 50% of marks in invigilated assessments
Resources
Recommended Reading:
Hans-Petter Halvorsen, Software Development A Practical Approach, 1st Edition, 2020
Hans-Petter Halvorsen, Python for Software Development, 1st Edition, 2020
You can refer to topic resources on your Moodle unit page.
Academic Misconduct
Ethical conduct and academic integrity and honesty are fundamental to the mission of PIA and academic misconduct will not be tolerated by the Institute. It is the responsibility of every student to make sure that you understand what constitutes academic misconduct and to refrain from engaging in it. Please refer to the Institutes Academic Integrity and Misconduct Policy for further details.
Changes to Unit Outlines
This Unit Outline may be updated and amended from time to time. Any changes will be notified to students through the Online Learning System (Moodle) for the unit.
