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BUSINESS SCHOOL
Unit of Study Outline
Unit Code QBUS3820
Unit Title Data Mining and Data Analysis
Semester 2, 2016
Pre-requisite Units: ECMT2110 or QBUS2810
Co-requisite Units:
Prohibited Units:
Assumed Knowledge and/or Skills: You are assumed to possess a basic level of knowledge of introductory
statistics and mathematics
Unit Coordinator: Dr Minh ngoc Tran
Address: Room 4091, Abercrombie Precinct(H70), The University of Sydney NSW 2006
Email: [email protected] Phone: 8627 4752
Consultation Hours: Please go to Blackboard for details of all staff consultation times.
Class Day(s): Please go to Blackboard for class times and locations
Required Text / Resources:
Textbook: T Hastie, R Tibshirani and J Friedman. The Elements of Statistical Learning: Data Mining, Inference,
and Prediction, Second Edition. Springer, Springer Series in Statistics, 2009; available for free at
http://www-stat.stanford.edu/~tibs/ElemStatLearn/.
Tutorials: There will be weekly Tutorials (Labs) in Lab 1 of Business and Economics Building on Wednesdays
13:00-14:00 and Thursdays 10:00-11:00.
Students will learn Python in this course. Attending the tutorials is compulsory, 5% of the total mark is allocated
on tutorial participation.
Tutor: Mr. Ransalu Senanayake. Email: [email protected].
This unit of study outline MUST be read in conjunction with
The Business School Unit of Study Common Policy and implementation information that applies to every unit of study offered by the
Business School (http://sydney.edu.au/business/currentstudents/policy). All assessment rules, such as standards used, penalties etc, are
covered.
The Business School Student Administration Manual - for information about all processes such as illness, appeals etc (
http://sydney.edu.au/business/currentstudents/student_information/student_administration_manual) When deciding applications and
appeals relating to these matters it will be assumed that every student has taken the time to familiarise themselves with these key policies
and procedures.
The Business School seeks feedback from students and staff in order to continually improve all units offered. For information on previously
collected feedback and innovations made in response to this feedback, please see
http://sydney.edu.au/business/learning/planning_and_quality/feedback/student
1. Unit of Study Information
The advances in information technology have made available very rich information data sets, often generated
automatically as a by-product of the main institutional activity of a firm or business unit. Data Mining deals with
inferring and validating patterns, structures and relationships in data, as a tool to support decisions in the business
environment. The course offers an insight into the main statistical methodologies for the visualisation and the
analysis of business and market data, providing the information requirements for specific tasks such as credit
scoring, prediction and classification, market segmentation and product positioning. Emphasis will be given to
empirical applications using modern software tools.
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2. Program Learning Outcomes and Unit Learning Outcomes
The Program Learning Outcomes for this Program are located at
http://sydney.edu.au/business/about/accreditations/AoL
Unit Learning Outcomes
Unit Learning Outcomes
On the successful completion of the Unit you should be able to:
The objectives are that, at the completion of the unit, students
(1) know the statistical theory required for business data mining and data
analysis
(2) can identify which statistical tool is most relevant for specific business
analytic tasks
(2) can identify advantages and limitations of each method
(3) can extract information from large volumes of data readily available
from the business environment,
(4) can obtain and interpret a meaningful analytical result using a software
package such as Python, Matlab and R
(5) can work productively in a team
(6) can present and write about their findings effectively
Program Learning Outcomes
1. Business Knowledge
2. Critical Thinking
3. Business Analysis and
Problem-Solving
4. Communication
5. Team Working
6. Ethical and Social
Responsibility
3. Assessment
Assessment
Name
Individual/ Assessment
Group
Conditions
Program
Learning
Outcomes
Assessed
Group Project
Group
Compulsory
1, 3, 4, 5
Assignment
Mid semester
Exam
Individual
Compulsory
2, 3
12
pages
NA
Individual
Compulsory
1, 2, 3
NA
20%
Final Exam
Individual
Compulsory
1, 2, 3
3 hours
45%
Academic
Honesty
Length
Weight
Due
Time
Due Date
Closing Date
20%
4:00pm
28-Oct-2016
07-Nov-2016
15%
3:00pm
08-Aug-2016
Mid Semester
Exam Period
Final Exam
Period
08-Aug-2016
Mid Semester
Exam Period
Final Exam
Period
Week 4
For the meaning and operation of this table, see policy information in the box on the front page or click here
Assessment details
Group Project
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Task Description
The purpose of this project is to simulate a real work experience as a business analytics professional team.
1. Form a team of 5 students. Choose a data analysis problem that can be solved via the use of real data
and the data mining methods taught in this unit.
2. Source your own data (e.g. from online databases) relevant to this problem.
3. Conduct the process of forming and refining the problem, exploratory analysis of data, choosing the
appropriate statistical model, refining it, analysing the data with it, conducting associated estimation and
inference (and possibly forecasting) analysis (repeating any and all steps necesary as you go)
4. Prepare a less-than-12 page report that presents a summary of your problem, analysis, model, statistical
findings and conclusions, as well as discusses any limitations or problems (or advantages) in your
analysis. Marks will be allocated based on appropriateness of choice of problem and data,
appropriateness of modelling and analysis, estimation choices, discussion of limitations of methods and
clarity of conclusions.
5. There's no one right structure for the report. The report might contain the following sections: executive
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summary, background, data and method, findings and conclusions. Ideally, the excutive summary should
provide a brief overview of the report, summarise the main findings using non-technical language.
6. Give each of your team members a mark out of 10 and communicate it by email to the lecturer.
●
●
●
The assignment must be submitted online on the Blackboard website through TURNITIN. The late penalty for
the written report in this project is 10% of the assigned mark per day, starting after 4pm on the due date.
Assessment Criteria
1. Presentation, communication & style (written)
2. Methodology used is the most appropriate to the aims and objectives of the task
3. Analysis
4. Critical reasoning / critical thinking
Feedback - What, when and how feedback will be provided for this assessment
Written feedback emailed to students prior to the final exam.
Assignment
●
●
●
Task Description
There will be two in-class online quizzes via Socrative.com. This assignment weighs 10%, another 5% is
allocated on tutorial participation. The purpose of these quizzes is to provide on-going feedback on student
learning and on how students are understanding and progressing with the concepts and ideas presented in
the unit. This assignment should also encourage regular work by students.
Assessment Criteria
1. Use of literature/ Knowledge of theory
2. Problem solving
3. Conceptualisation
Feedback - What, when and how feedback will be provided for this assessment
Answers of the online quizzes are released right after the submission.
Mid semester Exam
●
●
●
Task Description
The exam involves written-answer questions and will cover all the material from weeks 1-6 inclusive. A
description of the mid-semester exam will be given in lectures. This is an open book exam, i.e. students are
allowed to bring notes, texts or resource materials into the exam.
Assessment Criteria
1. Use of literature/ Knowledge of theory
2. Problem solving
3. Critical reasoning / critical thinking
Feedback - What, when and how feedback will be provided for this assessment
Answers of the exam questions are released within one week after the exam date.
Final Exam
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●
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Task Description
The final written-answer exam will cover all the material and concepts covered in this unit over the full 13
weeks of semester. A description of the final-semester exam will be given in lectures. This is an open book
exam, i.e. students are allowed to bring notes, texts or resource materials into the exam.
Assessment Criteria
1. Clarity of expression (incl. accuracy, spelling, grammar, punctuation)
2. Use of literature/ Knowledge of theory
3. Analysis
4. Problem solving
Feedback - What, when and how feedback will be provided for this assessment
Exam review session, post semester
4. Other Resources for Students
All lectures and seminars are recorded and will be available within Blackboard for student use. Please note the
Business School does not own the system and cannot guarantee that the system will operate or that every
class will be recorded. Students should ensure they attend and participate in all classes.
Recommended reading:
1. Foster Provost and Tom Fawcett. Data Science for Business: What You Need to Know about Data Mining
and Data-Analytic Thinking.
2. James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning with
Applications in R, Springer, New York
We will use Python in this course. A Python version of this book can be found at
https://github.com/JWarmenhoven/ISLR-python
Pass Program
Peer Assisted Study Sessions. This program helps to improve students' academic performance providing extra
free learning opportunities with trained student facilitators, including problem solving practice where relevant, in
areas directly related to understanding the unit concepts more thoroughly. Students register for PASS online at:
http://sydney.edu.au/business/learning/students/pass Email all enquiries about the PASS program to:
[email protected]
Maths in Business
The Business School provides a free series of workshops with student facilitators open to all students interested
in mastering both basic and higher intermediate level mathematics. Workshops will be structured to strengthen
your knowledge of algebra before proceeding to calculus, probability and then progressing to refine your skills
in Excel. Students register for workshops online at: http://sydney.edu.au/business/study/services/maths/register
Email all enquiries about the Maths in Business program to: [email protected]
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5. Unit Schedule
Week
1
25 Jul 2016
2
1 Aug 2016
3
8 Aug 2016
4
15 Aug 2016
5
22 Aug 2016
6
29 Aug 2016
7
5 Sep 2016
8
12 Sep 2016
9
19 Sep 2016
List of Topics
Assessments Due
Introduction to Data Science
Linear regression
Linear regression
Quiz 1
Self-study, no lecture delivery: Review on Basic Probability and Linear
Algebra.
Classification
Classification
Mid-term exam (no lecture and lab)
Mid-term exam
Splines and smoothing splines
Model selection and variable selection
Quiz 2
Common week 26 Sep to 2 Oct
10
3 Oct 2016
11
10 Oct 2016
12
17 Oct 2016
13
24 Oct 2016
Model selection and variable selection
Nonparametric methods
Nonparametric methods
Neural networks
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Project assignment
5