Unit Code and Title DAT604 Data Mining
Unit Code and Title DAT604 Data Mining
Course(s) Master of Business
Credit Points 6
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 private study, preparing assessments, and completing examinations.
Mode of Delivery Face-to-face
Pre-Requisites DAT601, DAT602
Name of Unit Coordinator Dr. Zohreh Moghaddas
Contact Information Email: Zohreh@pia.edu.auConsultation: Refer to your Moodle unit page for unit coordinators consultation time.
Unit Description
The Data Mining unit is designed to equip students with the fundamental concepts and practical skills required to extract valuable insights and knowledge from datasets. Students will learn how to use statistical and machine learning techniques to explore, analyze, and interpret complex data in a business context. The unit will cover topics such as data preprocessing, data visualization, classification, clustering, association rule mining, and anomaly detection. Through a combination of lectures, tutorials, and practical exercises, students will develop a comprehensive understanding of the data mining process, including data preparation, feature selection, model selection, and evaluation. Upon completion of this unit, students will have the necessary skills to apply data mining techniques to real-world business problems and make informed decisions based on data-driven insights.
Unit Learning Outcomes (ULOs)
On successful completion of this unit, students will be able to:
ULO1Build your data mining capabilities to use data for innovative business solutions.
ULO2Enhance knowledge and skills in the current trends in the management and use of data mining.
ULO3Differentiate, design and assess various data mining modelsULO4Analyse the real-world business problems into data mining modelsCourse Learning Outcomes (CLOs)
Successful completion of this unit will contribute to the following Course Learning Outcomes (CLOs):
CLO1Develop data mining solutions for a specific business.
CLO2Critically evaluate the applicability of mining solutions CLO3Communicate and justify data mining projects to business and technical audiencesGraduate Attributes (GAs)
Successful completion of this unit will contribute to the following PIA Graduate Attributes (GAs):
GA1 Communicate effectively in a diverse range of professional or community contextGA2 Complete work tasks and assignments independently or as an effective member of multidisciplinary teams
GA3 Engage in and value life-long learning leading to the enhancement of professional knowledge and skillsGA4 Are information and technology literateGA5 Respond appropriately to a changing workforces, cultures and values reflecting a global work environmentGA6 Demonstrate critical thinking, problem solving and decision-making abilities essential to contributing soundly to the resolution of issues confronting organisationsGA7 Facilitate intellectual curiosityGA8 Act in an ethical manner in all aspects of professional life.
Learning and Teaching Approach
Learning and teaching in this unit applies the Institutes model of providing transformational learning experiences that are student-centered, 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 week 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
Students are expected to:
Prepare for scheduled classes by completing assigned activitiesAttend at least 80% of scheduled classesActively participate in class activities
Seek clarification and advice from teaching staff as neededAttempt all assessmentsSubmit assessments on timeReview and reflect on feedback on assessments and seek clarification about feedback where neededNotify the lecturer and / or unit coordinator if unable to attend classes and/or submit assessmentsCompletion of the learning activities for each week 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.
Schedule of Learning and Teaching Activities
Week No. Topic Learning Activities Readings
Week 1 Introduction to data mining (DM) Understand data environmentLearning various data mining platforms
Han, Jiawei, et al. (2022)Data Mining: Concepts and Techniques. Fourth Edition, Elsevier.
Week 2 Python for data mining 1 Programming & data mining
Introduction to Python
Implementing Python in script mode
Jupyter Notebook
Introduction to some Python libraries
Data manipulation in Python Jake VanderPlas(2017), Python Data Science Handbook: Essential Tools for Working With Data,1st edition, O'Reilly Media.
Week 3 Python for data mining 2 Data types & structure in Python
Function in Python
For Loop in Python Jake VanderPlas(2017), Python Data Science Handbook: Essential Tools for Working With Data,1st edition, O'Reilly Media.
Week 4 Cluster analysis Introduction to cluster analysis
Partitioning methods
Hierarchical methods
Quality of clusters Han, Jiawei, et al. (2022)Data Mining: Concepts and Techniques. Fourth Edition, Elsevier.
Week 5 Classification Introduction to classification
Learning and Classification
Decision Tree (DT)
Random Forest
Overfitting & tree pruning Han, Jiawei, et al. (2022)Data Mining: Concepts and Techniques. Fourth Edition, Elsevier.
Week 6 Classification evaluation Introduction to evaluation
Evaluation Methods
Confusion Matrix
Model Comparison Han, Jiawei, et al. (2022)Data Mining: Concepts and Techniques. Fourth Edition, Elsevier.
Week 7 Artificial Neural Networks Backpropagation in Python Han, Jiawei, et al. (2022)Data Mining: Concepts and Techniques. Fourth Edition, Elsevier.
Week 8 Other classification techniques Support Vector Machines (SVMs)
K Nearest Neighbour classification (KNN)
Nave Bayes classification (NB) Han, Jiawei, et al. (2022)Data Mining: Concepts and Techniques. Fourth Edition, Elsevier.
Week 9 Text mining 1 Introduction
Bag of Words
TF-IDF
Dimensionality Reduction and SVD Dietrich D., Heller B. and Yang B. (2014), Data Science and Big Data Analytics Discovering, Analyzing, Visualizing and Presenting Data, Wiley.
Week 10 Text mining 2 Sentiment classification
Topic modeling Dietrich D., Heller B. and Yang B. (2014), Data Science and Big Data Analytics Discovering, Analyzing, Visualizing and Presenting Data, Wiley.
Week 11 Sequential Pattern Mining Data mining lifecycle
Future of data mining projects
Han, Jiawei, et al. (2022)Data Mining: Concepts and Techniques. Fourth Edition, Elsevier.
Week 12 Data Mining brief and project Integrated models
Clustering as an input for classification Han, Jiawei, et al. (2022)Data Mining: Concepts and Techniques. Fourth Edition, Elsevier.
Assessment Information
Assessment Task Weighting (%) Due Length ULO CLO GA
Assessment 1:
Practical invigilated Labs 30% Weeks 3, 6, 9, 12 Varies between 30 minutes to an hour 1,2,3,4 1,2,3,4,5 2,3,4,6,7
Assessment 2:
(Group Assessment)
Report
Recorded Presentation 30%
20%
10% Week 8 1500 +/-10% words
plus, Jupyter notebook of 50 cells +/-10%
8-10Minutes 1,2,3,4 1,2,3,4,5,6,7 1,2,3,4,5,6,7,8
Assessment 3:
Text mining project
Report (40%)
(Individual Assessment)
40% Week 11 Jupyter notebook of 60 cells +/-10% 1,2,3, 1,2,3,4,5,6 1,2,3,4,6,
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
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 available in invigilated assessments.
Prescribed Resources(s):
Text book: Han, Jiawei, et al. (2012) Data Mining: Concepts and Techniques. Third Edition, Elsevier.
Recommended reading:
Jake VanderPlas (2017), Python Data Science Handbook: Essential Tools for Working With Data, 1st edition, O'Reilly Media.
Dietrich D., Heller B. and Yang B. (2014), Data Science and Big Data Analytics Discovering, Analyzing, Visualizing and Presenting Data, Wiley.
Participation
Students are required to participate in all collaborative work, group work and work integrated activities, such as study tours, industry lead activities and open forums, (a) actively, fully and positively; and (b) in a timely manner. Student contributions to collaborative, group, and work integrated activities must be meaningful, of value to peers, and follow the specifications of the Unit Study Guide.
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. Please refer to the Institutes Academic Integrity Policy for further details.
Academic integrity means putting values into practice by being honest in the academic work you do at the Institute, being fair to others, taking responsibility for learning, and following the conventions of scholarship.It is the responsibility of every student to make sure that they understand what constitutes academic misconduct and to refrain from engaging in it.
For example, cutting and pasting from the Internet and representing this as your own work, is regarded as academic misconduct.
It is your responsibility to ensure that you demonstrate academic integrity.Take the time to find out more by visiting PIAs Policies and Procedures site.
By submitting your assessments, you acknowledge that this is your own work that you have undertaken the assessments yourself and without any assistance from any other person or any website or other resources which are not specifically permitted. Also, you have not shared any aspect of your assessments or answers with other students or provided assistance to them in any way.
Attendance
PIA has a responsibility to ensure that all students enrolled at the Institute are able to make satisfactory progress through their course, and attending scheduled classes is essential for course progression. For onshore international students maintaining satisfactory attendance in the course and making satisfactory progress with the course are also conditions of the student Visa. PIA therefore monitors the attendance of all students at all scheduled classes and students are required to attend at least 80% of scheduled for units in which they are enrolled, where attendance means that the student is present for the whole duration of the scheduled lecture, tutorial or seminar class. Students are advised that decisions about the award of supplementary assessments will take into account student attendance.
Disclaimer
This unit study guide may be updated and amended from time to time. Any changes to the unit will be notified to students through the Online Learning System (MyPIA) for the unit.
