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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. Version: 2016 Business School 1 BUSINESS SCHOOL 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 ● 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 Version: 2016 Business School 2 BUSINESS SCHOOL 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 Version: 2016 Business School 3 BUSINESS SCHOOL ● ● ● 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] Version: 2016 Business School 4 BUSINESS SCHOOL 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 Version: 2016 Business School Project assignment 5