Unit Details
\nUnit Code: 301046
\nUnit Name: Big Data (PG)
\nCredit Points: 10
\nUnit Level: 7
\nAssumed Knowledge: It is expected that students enrolled in this unit should have basic programming skills in
\nany programming language and working knowledge in elementary probability and statistics,
\nincluding the concepts of random variables, basic probability distributions, expectations, mean
\nand variance.
\nNote: Students with any problems, concerns or doubts should discuss those with the Unit Coordinator as early as they can.
\nUnit Coordinator
\nName: Miroslav Filipovic
\nPhone: 4736 0836
\nLocation: Penrith Y2 08
\nEmail: m.filipovic@westernsydney.edu.au
\nConsultation Arrangement:
\nby appointment only
\nTeaching Team
\nName: Ivan Bojicic
\nLocation: Penrith Y2 08
\nEmail: i.bojicic@westernsydney.edu.au
\nConsultation Arrangement:
\nby appointment only
\nNote: The relevant Learning Guide Companion supplements this document
\nContents
\n1 About Big Data (PG) 2
\n1.1 An Introduction to this Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
\n1.2 What is Expected of You . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
\n1.3 Changes to Unit as a Result of Past Student Feedback . . . . . . . . . . . . . . . . . . . . . . . . . 2
\n2 Assessment Information 3
\n2.1 Unit Learning Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
\n2.2 Approach to Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
\n2.3 Contribution to Course Learning Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
\n2.4 Assessment Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
\n2.5 Assessment Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
\n2.5.1 Labs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
\n2.5.2 Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
\n2.5.3 In-Class Exam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
\n2.6 General Submission Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
\n3 Teaching and Learning Activities 14
\n4 Learning Resources 18
\n4.1 Recommended Readings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
\n1
\n1 About Big Data (PG)
\n1.1 An Introduction to this Unit
\n\u201d\u201dBig data\u201d\u201d is the label for the ever-increasing gigantic amount of data with which humanity has to cope. The
\navailability of data and the development of cloud computing architectures to process and analyse these data have
\nmade data analytics a central tool in our endeavours. This unit will introduce students to the realm of \u201d\u201dbig data\u201d\u201d,
\ncovering the important principles and technologies of retrieving, processing and managing massive real-world data
\nsets. It is designed to provide the basic techniques required by any discipline that needs to make sense out of the
\ngrowing amount of data, and to equip students with the knowledge and key set of skills set to be competitive in the
\ngrowing job market in the analytics field.
\n1.2 What is Expected of You
\nStudy Load
\nA student is expected to study an hour per credit point a week. For example a 10 credit point unit would require 10
\nhours of study per week. This time includes the time spent within classes during lectures, tutorials or practicals.
\nAttendance
\nIt is strongly recommended that students attend all scheduled learning activities to support their learning.
\nOnline Learning Requirements
\nUnit materials will be made available on the unit\u2019s vUWS (E-Learning) site (https:\/\/vuws.westernsydney.edu.au\/).
\nYou are expected to consult vUWS at least twice a week, as all unit announcements will be made via vUWS. Teaching
\nand learning materials will be regularly updated and posted online by the teaching team.
\nNo E-Learning resources required for this Unit.
\nSpecial Requirements
\nEssential Equipment:
\nNot Applicable
\nLegislative Pre-Requisites:
\nNot Applicable
\n1.3 Changes to Unit as a Result of Past Student Feedback
\nThe University values student feedback in order to improve the quality of its educational programs. The feedback
\nprovided helps us improve teaching methods and units of study. The survey results inform unit content and design,
\nlearning guides, teaching methods, assessment processes and teaching materials.
\nYou are welcome to provide feedback that is related to the teaching of this unit. At the end of the semester
\nyou will be given the opportunity to complete a Student Feedback on Unit (SFU) questionnaire to assess the unit.
\nYou may also have the opportunity to complete a Student Feedback on Teaching (SFT) questionnaire to provide
\nfeedback for individual teaching staff.
\nAs a result of student feedback, the following changes and improvements to this unit have recently been made:
\n\u2013 We are upgrading our practicals as well as lectures to have more visial and content appeal that is relevant to
\nstudents.
\n2
\n2 Assessment Information
\n2.1 Unit Learning Outcomes
\n\u201dBig data\u201d is the label for the ever-increasing gigantic amount of data with which humanity has to cope. The
\navailability of data and the development of cloud computing architectures to process and analyse these data have
\nmade data analytics a central tool in our endeavours. This unit will introduce students to the realm of \u201dbig data\u201d,
\ncovering the important principles and technologies of retrieving, processing and managing massive real-world data
\nsets. It is designed to provide the basic techniques required by any discipline that needs to make sense out of the
\ngrowing amount of data, and to equip students with the knowledge and key set of skills set to be competitive in the
\ngrowing job market in the analytics field.
\nOutcome
\n1 Explain the major trends in technology, business, and science behind big data
\n2 Analyse and compare a selection of major big data management techniques in use today, including parallel
\ndatabases, NoSQL, MapReduce, cloud services
\n3 Evaluate the relative strengths and weaknesses of MapReduce and parallel database systems and apply the
\nappropriate technique to tackle relevant big data problems
\n4 Apply proper methods of data pre-processing and cleaning for big data analysis
\n2.2 Approach to Learning
\nLectures are designed to introduce students to this emerging technology and get updated on the major trends behind
\nbig data. Students will also be taught the important concepts and
\nprinciples for big data processing and analytics, including databases, parallel computing and data wrangling.
\nPracticals will assist students to acquire and develop the necessary practical skills required for big data processing and analytics. In particular, students will obtain hands-on experiences in Python programming, one of the most
\npopular and powerful languages for data analysis, and database management skills such as SQL and NoSQL query
\nmethods, as well as the use of MapReduce framework to solve practical big data problems.
\n3
\n2.3 Contribution to Course Learning Outcomes
\n3701: Graduate Certificate in Information and Communications Technology
\nCourse Learning Outcomes ULO 1 ULO 2 ULO 3 ULO 4
\n1. Demonstrate knowledge of core concepts related to ICT, including established theories and recent
\ndevelopments, with an understanding of the both local and international perspectives.
\nIntroduced Developed Introduced Introduced
\n2. Identify, analyse and communicate problems related to ICT, and respond to stakeholder needs and
\ngoals, within the framework of professional and ethical practice.
\nDeveloped Introduced Developed Assured
\n3700: Graduate Diploma in Information and Communications Technology
\nCourse Learning Outcomes ULO 1 ULO 2 ULO 3 ULO 4
\n1. Develop an advanced understanding of core concepts related to ICT body of knowledge, including
\nestablished theories and recent developments with an understanding of the both local and international
\nperspectives.
\nIntroduced Introduced
\n2. Identify, analyse and communicate problems and issues related to ICT and articulate appropriate
\nsolutions in order to respond to stakeholder needs and goals, within the framework of professional and
\nethical practice.
\nIntroduced Developed Introduced
\n3. Demonstrate a high level of personal autonomy and accountability in acquisition and application of
\nknowledge and skills.
\nDeveloped Developed Developed
\n3699: Master of Information and Communications Technology
\nCourse Learning Outcomes ULO 1 ULO 2 ULO 3 ULO 4
\n1. Demonstrate an advanced understanding of core and specialised concepts related to ICT body of
\nknowledge, including established theories and recent developments with an understanding of the both
\nlocal and international perspectives.
\nIntroduced Introduced
\n2. Identify and analyse problems and issues related to ICT and articulate appropriate solutions and justify
\npropositions in order to respond to stakeholder needs and goals, within the framework of professional and
\nethical practice.
\nIntroduced Developed Introduced
\n3. Demonstrate a high level of personal autonomy and accountability, in acquisition and application of
\nknowledge and skills and in problem solving in professional context.
\nDeveloped Developed Developed
\n4. Apply enquiry-based learning, investigate and synthesise complex ideas and concepts, and develop ways
\nof learning by exploring new knowledge within ICT discipline.
\nAssured Assured
\n5. Develop skills in scholarly research and communicate complex ideas in a variety of formats to diverse
\naudiences.
\nAssured Assured
\n4
\n3698: Master of Information and Communications Technology (Advanced)
\nCourse Learning Outcomes ULO 1 ULO 2 ULO 3 ULO 4
\n1. Demonstrate an in-depth understanding of core concepts related to ICT body of knowledge, including
\nestablished theories and recent developments with an understanding of the both local and international
\nperspectives.
\nIntroduced Introduced
\n2. Further develop knowledge and skills in specialised areas that are closely applicable to ICT profession. Introduced Developed Introduced
\n3. Identify and analyse problems and issues related to ICT and articulate appropriate solutions and justify
\npropositions in order to respond to stakeholder needs and goals, within the framework of professional and
\nethical practice.
\nDeveloped Developed Developed
\n4. Demonstrate a high level of personal autonomy and accountability in acquisition and application of
\nknowledge and skills and in problem solving in professional context.
\nAssured Assured
\n5. Apply enquiry-based learning, investigate and synthesise complex ideas and concepts, and develop ways
\nof learning in exploring new knowledge within ICT discipline.
\nAssured Assured
\n6. Develop skills in scholarly research, critically evaluate contemporary literatures in ICT field and
\ncommunicate complex ideas in a variety of formats to diverse audiences.
\nIntroduced Developed Introduced
\n3702: Master of Information and Communications Technology (Research)
\nCourse Learning Outcomes ULO 1 ULO 2 ULO 3 ULO 4
\n1. Demonstrate an in-depth understanding of core concepts related to the ICT body of knowledge,
\nincluding established theories, professional ethics and recent developments with an understanding of both
\nlocal and international perspectives.
\nIntroduced Introduced
\n2. Develop advanced knowledge for identifying and analysing research problems and acquisition and
\napplication of research methods and techniques related to ICT.
\nIntroduced Developed Introduced
\n3. Demonstrate cognitive, creative and technical skills to generate and evaluate complex concepts at an
\nabstract level for problem solving in a research context.
\nDeveloped Developed Introduced Developed
\n4. Analyse, investigate and synthesise complex ideas and concepts, and develop ways of learning in
\nexploring new knowledge within the ICT discipline.
\nAssured Assured Developed
\n5. Evaluate contemporary literature, and create a high-level plan for conducting original research in the
\nICT field and communicate complex ideas and research results in a variety of formats to diverse audiences.
\nAssured Developed Assured
\n6. Design, execute and evaluate a substantial research-based project ( help with nursing paper writing from experts with MSN & DNP degrees) in the ICT field with a high level of
\npersonal autonomy and accountability.
\nIntroduced Developed Introduced
\n5
\n3735: Master of Data Science
\nCourse Learning Outcomes ULO 1 ULO 2 ULO 3 ULO 4
\n1. Apply Data Science methods to problems in various disciplines (e.g. Business, Science, Social Science,
\nEngineering, Education and the Humanities).
\nDeveloped Developed Assured Developed
\n2. Conduct and manage the formulation of problems and the use of data ethically and responsibly. Developed Introduced Introduced Developed
\n3. Design and conduct data gathering and analysis to provide information and advice that is reliable, valid,
\ntimely and relevant.
\nDeveloped Developed Assured Developed
\n4. Generate interpretive and predictive reports, working alongside professional colleagues in
\ndecision-making.
\nDeveloped Introduced Assured Developed
\n5. Provide expert advice to professional colleagues on the validity and reliability of interpretations and
\npredictions based on analysis of large complex data sets.
\nDeveloped Developed Assured Developed
\n6
\n2.4 Assessment Summary
\nThe assessment items in this unit are designed to enable you to demonstrate that you have achieved the unit learning
\noutcomes. Completion and submission of all assessment items which have been designated as mandatory or compulsory is essential to receive a passing grade.
\nTo pass this unit you must:
\n1. Achievement of at least 50% overall is mandatory to pass this unit.
\n2. For the in-class exam, the Threshold is Yes. In order to pass the unit, student is required to pass In-class Exam
\nof minimum 50%.
\nItem Weight Due Date ULOs Assessed Threshold
\nLabs 30% at the end of each lab 1, 2, 3, 4 No
\nProject 35% Friday 7th June 2019 (17:00) \u2013 week 14 2, 3, 4 No
\nIn-Class Exam 35% Friday 31st May 2019 \u2013 week 13 1, 2, 3 Yes
\nFeedback on Assessment
\nFeedback is an important part of the learning process that can improve your progress towards achieving the learning
\noutcomes. Feedback is any written or spoken response made in relation to academic work such as an assessment
\ntask, a performance or product. It can be given to you by a teacher, an external assessor or student peer, and may
\nbe given individually or to a group of students. As a Western Sydney University student, it is your responsibility to
\nseek out and act on feedback that is provided to you as a resource to further your learning.
\nFeedback is an important part of the learning process that can improve your progress towards achieving the learning
\noutcomes. Feedback is any written or spoken response made in relation to academic work such as an assessment
\ntask, a performance or product. It can be given to you by a teacher, an external assessor or student peer and may
\nbe given to individually or to a group of students. As a UWS student, it is your responsibility to seek out and act on
\nfeedback that is provided to you as a resource to further your learning.
\nIn this unit you can expect oral feedbacks on your lab work during each practical session, as well as your presentation for assignment 2 during last week\u2019s practical session when student presentations are given, and written
\nfeedbacks feedback on your assignment 2 report submitted to vUWS upon the completion of the marking process.
\n7
\n2.5 Assessment Details
\n2.5.1 Labs
\nWeight: 30%
\nType of Collaboration: Individual
\nDue: at the end of each lab
\nSubmission: in class
\nFormat: 10 Labs (2 Hours Each)
\nLength: 2 hours (10 labs)
\nCurriculum Mode: Practical
\nIn each practical session, you are expected to complete a few exercises. These may include programming exercises
\nor\/and short answer questions. Your work will be marked by the tutor at the end of each lab session. Marks are
\nassigned based on attendance as well as the quality of the answers provided for the mared exercises conducted in
\neach session. The total mark achievable for this assessment item is 30%, with 3% for each lab.
\nResources:
\nCheck the lecture notes for the corresponding week and references therein for further details
\nMarking Criteria:
\nCriteria High Distinction Distinction Credit Pass Unsatisfactory
\nMarks are assigned
\nbased on
\nattendance as well
\nas the quality of the
\nanswers provided
\nfor the planed
\nexercises conducted
\nin each session.
\n85%+
\nDemonstrates
\ncomplete
\nunderstanding of
\nthe problem. All
\nrequirements of the
\nquestion are
\nincluded in
\nresponse.
\n75% – 85%
\nDemonstrates
\nconsiderable
\nunderstanding of
\nthe problem. Most
\nrequirements of the
\nquestion are
\nincluded in
\nresponse.
\n65% – 75%
\nDemonstrates
\npartial
\nunderstanding of
\nthe problem. Many
\nrequirements of the
\nquestion are
\nincluded in
\nresponse.
\n50% – 65%
\nDemonstrates little
\nunderstanding of
\nthe problem. Many
\nrequirements of the
\nquestion are missing
\nfrom response.
\n<50%
\nDemonstrates no
\nunderstanding of
\nthe problem, or no
\nresponse\/question
\nnot attempted.
\n8
\n2.5.2 Project
\nWeight: 35%
\nType of Collaboration: Individual
\nDue: Friday 7th June 2019 (17:00) \u2013 week 14
\nSubmission: Online (report\/programme), In class (Presentation)
\nFormat: A report with a maximum of 2000 words + Final presentation 10 minutes including
\nquestion time. A report should be submitted in PDF format.
\nLength: A report with a maximum of 2000 words + final presentation 10 minutes including
\nquestion time
\nCurriculum Mode: Report
\nThis is a take-home assignment that includes two parts. For the first part, you have two options. For option 1,
\nyou can write a technical report to discuss one of the major techniques in big data, including but not limited to
\nparallel database, NoSQL, MapReduce, data wrangling and cleaning, predictive analysis, etc. For option 2, you need
\nto write a program to perform data acquisition and manipulation for a simple data analysis task. You need to submit
\nthe report or the source code of your programme through the submission link on the vUWS site of this unit by the
\nsubmission deadline (5pm Friday Week 14). The first part of the project ( help with nursing paper writing from experts with MSN & DNP degrees) is worth 20%.
\nWhich-over option you take, you will have to present your work in the lab class in week 14 as the second part of
\nthis project ( help with nursing paper writing from experts with MSN & DNP degrees). You will be allocated 10 minutes including question time to go through the content of your report in
\nPowerpoint slides or show your programme to the audience (tutor and rest of the class) followed by questions from
\nthe audience. The mark for your presentation will be determined by peer assessment, i.e. averaging marks that the
\ntutor and other students give you. The second part of the project ( help with nursing paper writing from experts with MSN & DNP degrees) is worth 15%.
\nThis is an individual project ( help with nursing paper writing from experts with MSN & DNP degrees) and you must work by yourself. However under special circumstances and subject to the
\napproval of the unit coordinator, you can form groups of 2 students and work in pairs. In this case, you will have to
\nlabel clearly the individual contributions for the submission of report\/programme. Both of you have to participate in
\nthe presentation. You will still be marked based on individual performance.
\nResources:
\n– Lecture notes and links provided therein
\n– Recommended readings
\nMarking Criteria:
\nCriteria High Distinction Distinction Credit Pass Unsatisfactory
\nObjective 10% The report has
\nclear aim and is
\nwell focused on a
\nsingle important
\ntechnique in big
\ndata
\nThe report has
\nclear aim and is
\nwell focused on a
\nsingle important
\ntechnique in big
\ndata
\nMainly focusing on
\na single area of big
\ndata, with some
\ndeviations and
\nirrelevant
\ndiscussions
\nMainly focusing on
\na single area of big
\ndata, with some
\ndeviations and
\nirrelevant
\ndiscussions
\nNot focusing on a
\nsingle area of big
\ndata
\nOriginality 20% The report contains
\nsome original
\nthoughts and
\nanalysis besides
\ndiscussions on facts
\nand observations
\nThe report contains
\nsome original
\nthoughts and
\nanalysis besides
\ndiscussions on facts
\nand observations
\nThere are some
\noriginal points
\nmade by the author
\nalthough not to a
\nsufficient extent
\nThere are some
\noriginal points
\nmade by the author
\nalthough not to a
\nsufficient extent
\nBasically repetition
\nof what is discussed
\nin the literature and
\nno insight\/input
\nmade on the
\nauthor\u2019s part
\nPresentation 20% The report reads
\nvery well, easy to
\nunderstand and free
\nfrom grammatical
\nmistakes, spelling
\nerrors and use of
\ncolloquial English
\nThe report reads
\nvery well, easy to
\nunderstand and free
\nfrom grammatical
\nmistakes, spelling
\nerrors and use of
\ncolloquial English
\nThe report reads
\nwell in general,
\ndespite some
\ngrammatical
\nmistakes and lack
\nof clarity on some
\nminor issues
\nThe report reads
\nwell in general,
\ndespite some
\ngrammatical
\nmistakes and lack
\nof clarity on some
\nminor issues
\nThe report is poorly
\nwritten and can
\nhardly be
\nunderstood
\nTechnical Quality
\n30%
\nThe report is up to
\na high calibre of
\ntechnicality and
\nreflects high level of
\ncritical thinking
\nfrom the author
\nThe report is up to
\na high calibre of
\ntechnicality and
\nreflects high level of
\ncritical thinking
\nfrom the author
\nThe report has
\ngood quality in
\ngeneral, despite
\nsome errors and
\ninaccuracies in the
\ndiscussion
\nThe report has
\ngood quality in
\ngeneral, despite
\nsome errors and
\ninaccuracies in the
\ndiscussion
\nThe report contains
\nmany factual errors
\nand contains
\nvirtually no
\ntechnical value
\n9
\nCriteria High Distinction Distinction Credit Pass Unsatisfactory
\nOrganisation 10% The report is well
\nstructured into
\nsections. Each
\nsection covers one
\npoint and is further
\ndivided into
\nparagraphs.
\nThe report is well
\nstructured into
\nsections. Each
\nsection covers one
\npoint and is further
\ndivided into
\nparagraphs.
\nThe report is
\nstructured into
\nparagraphs and
\nsections, although
\nnot all of them
\nmake sense.
\nThe report is
\nstructured into
\nparagraphs and
\nsections, although
\nnot all of them
\nmake sense.
\nThe report is poorly
\norganised.
\nReferences 10% Contains sufficient
\nreferences in
\nbibliographic
\nsection and
\nreferences are cited
\nin the main text
\nContains sufficient
\nreferences in
\nbibliographic
\nsection and
\nreferences are cited
\nin the main text
\nReferences included
\nbut are insufficient;
\nor references not
\nlinked in the main
\ntext
\nReferences included
\nbut are insufficient;
\nor references not
\nlinked in the main
\ntext
\nLacking references
\n10
\n2.5.3 In-Class Exam
\nWeight: 35%
\nType of Collaboration: Individual
\nDue: Friday 31st May 2019 \u2013 week 13
\nSubmission: in class
\nFormat: 2 Hours Open Book. The exam contains both multiple choice questions and short
\nanswer questions.
\nLength: 2 hours open book exam including reading time
\nCurriculum Mode: Quiz
\nThreshold Detail: A threshold for In-class Exam of minimum 50% \u2013 in order to pass the whole unit.
\nThis is a paper-based open book exam to be held during the lab class in week 13. You are allowed to bring any
\nreference books, notes, dictionaries. You can also use non-programmable calculators. You are not allowed to use
\ncomputers, including desktop, laptops, iPads, or a smartphone. The exam is 2 hours long including reading time.
\nThe exam contains both multiple choice questions and short answer questions. You need to attempt all questions
\nand provide your answers in clear writing. The exam covers all lteaching materials from week 1 to week 13. Sample
\nquestions will be provided online on the vUWS site of this unit closer to the dates of the exam.
\nResources:
\n– Lecture notes and links provided therein
\n– Recommended Readings
\nMarking Criteria:
\nCriteria High Distinction Distinction Credit Pass Unsatisfactory
\nCorrect Answer 85%+
\nDemonstrates
\ncomplete
\nunderstanding of
\nthe problem. All
\nrequirements of the
\nquestion are
\nincluded in
\nresponse.
\n75% – 85%
\nDemonstrates
\nconsiderable
\nunderstanding of
\nthe problem. Most
\nrequirements of the
\nquestion are
\nincluded in
\nresponse.
\n65% – 75%
\nDemonstrates
\npartial
\nunderstanding of
\nthe problem. Many
\nrequirements of the
\nquestion are
\nincluded in
\nresponse.
\n50% – 65%
\nDemonstrates little
\nunderstanding of
\nthe problem. Many
\nrequirements of the
\nquestion are missing
\nfrom response.
\n<50%
\nDemonstrates no
\nunderstanding of
\nthe problem, or no
\nresponse\/question
\nnot attempted.
\n11
\n2.6 General Submission Requirements
\nSubmission
\n\u2013 All assignments must be submitted by the specified due date and time.
\n\u2013 Complete your assignment and follow the individual assessment item instructions on how to submit. You must
\nkeep a copy of all assignments submitted for marking.
\nTurnitin
\n\u2013 The Turnitin plagiarism prevention system may be used within this unit. Turnitin is accessed via logging into
\nvUWS for the unit. If Turnitin is being used with this unit, this means that your assignments have to be
\nsubmitted through the Turnitin system. Turnitin from iParadigms is a web-based text-matching software that
\nidentifies and reports on similarities between documents. It is also widely utilised as a tool to improve academic
\nwriting skills. Turnitin compares electronically submitted papers against the following:
\n\u2013 Current and archived web: Turnitin currently contains over 24 billion web pages including archived pages
\n\u2013 Student papers: including Western Sydney University student submissions since 2007
\n\u2013 Scholarly literature: Turnitin has partnered with leading content publishers, including library databases,
\ntext-book publishers, digital reference collections and subscription-based publications (e.g. Gale, Proquest, Emerald and Sage)
\n\u2013 Turnitin is used by over 30 universities in Australia and is increasingly seen as an industry standard. It is
\nan important tool to assist students with their academic writing by promoting awareness of plagiarism.By
\nsubmitting your assignment to Turnitin you will be certifying that:
\n\u2013 I hold a copy of this assignment if the original is lost or damaged
\n\u2013 No part of this assignment has been copied from any other student\u2019s work or from any other source except
\nwhere due acknowledgement is made in the assignment
\n\u2013 No part of the assignment has been written for me by any other person\/s
\n\u2013 I have complied with the specified word length for this assignment
\n\u2013 I am aware that this work may be reproduced and submitted to plagiarism detection software programs for
\nthe purpose of detecting possible plagiarism (which may retain a copy on its database for future plagiarism
\nchecking).
\nSelf-Plagiarising
\n\u2013 You are to ensure that no part of any submitted assignment for this unit or product has been submitted by
\nyourself in another (previous or current) assessment from any unit, except where appropriately referenced, and
\nwith prior permission form the Lecturer\/Tutor\/Unit Co-ordinator of this unit.
\nLate Submission
\n\u2013 If you submit a late assessment, without receiving approval for an extension of time, (see next item), you will
\nbe penalised by 10% per day for up to 10 days. In other words, marks equal to 10% of the assignment\u2019s weight
\nwill be deducted from the mark awarded.
\n\u2013 For example, if the highest mark possible is 50, 5 marks will be deducted from your awarded mark for each late
\nday.
\n\u2013 Saturday and Sunday are counted as one calendar day each.
\n\u2013 Assessments will not be accepted after the marked assessment task has been returned to students.
\n\u2013 This is consistent with Clause 51 of the Western Sydney University\u2019s Assessment Policy – Criteria and StandardsBased Assessment.
\nExtension of Due Date for Submission
\nExtensions are only granted in exceptional circumstances. To apply for an extension of time, locate an application
\nform via the Western Sydney University homepage or copy the following link:
\nhttps:\/\/www.westernsydney.edu.au\/currentstudents\/current students\/forms
\nApplication forms must be submitted to the Unit Coordinator\/Convenor. Requests for extension should be made as
\nearly as possible and submitted within policy deadlines. Appropriate, supporting documentation must be submitted
\nwith the application. An application for an extension does not automatically mean that an extension will be granted.
\nAssessments will not be accepted after the marked assessment task has been returned to students.
\nResubmission Resubmission of assessment items will not normally be granted if requested.
\n12
\nApplication for Special Consideration
\nIt is strongly recommended that you attend all scheduled learning activities to support your learning. If you have
\nsuffered misadventure, illness, or you have experienced exceptional circumstances that have prevented your attendance
\nat class or your completion and submission of assessment tasks, you may need to apply for Special Consideration via the
\nWestern Sydney University website. https:\/\/monkessays.com\/write-my-essay\/westernsydney.edu.au\/currentstudents\/current students\/services
\nand facilities\/special consideration2 or the Student Centre\/Sydney City Campus Reception. Special Consideration is
\nnot automatically granted. It is your responsibility to ensure that any missed content has been covered. Your lecturer
\nwill give you more information on how this must be done.
\n13
\n3 Teaching and Learning Activities
\nWeeks Topic Lecture Prac\/Lab Independent Assessments Due
\nWeek 1
\n04-03-2019
\nIntroduction to Big Data Unit introduction and syllabus;
\nOverview of big data and useful
\napplications; tools in big data
\nGet started with Python
\nprogramming and go through
\nthe online tutorials by Code
\nUseful online tutorials – Labs
\nAcademy https:\/\/monkessays.com\/write-my-essay\/codecademy.com\/
\nen\/tracks\/python
\nhttps:\/\/monkessays.com\/write-my-essay\/learnpython.org\/
\nweek 2 [Essay writing service – your education, our expertise: Get any assignment done with our professional essay writers before your deadlines – Zero AI, Human experts and Fast delivery.]
\n11-03-2019
\nPython Basics I Python data types; operations;
\nselection statements
\nComplete Python programming
\nexercises
\nUseful online tutorials – Labs
\nhttps:\/\/monkessays.com\/write-my-essay\/codecademy.com\/
\nen\/tracks\/python
\nhttps:\/\/monkessays.com\/write-my-essay\/learnpython.org\/
\nWeek 3
\n18-03-2019
\nPython Basics II Advanced data types; loops; Practice SQL selection
\nstatements for database query
\nusing sqlite
\nUseful online tutorials – Labs
\nle I\/O https:\/\/monkessays.com\/write-my-essay\/codecademy.com\/
\nen\/tracks\/python
\nhttps:\/\/monkessays.com\/write-my-essay\/learnpython.org\/
\nWeek 4 (your nursing study bay)
\n25-03-2019
\nRelational Database Fundamentals of relational
\ndatabase; Structured Query
\nLanguage (SQL)
\nPractice NoSQL CRUD (create,
\nread, update, delete) operations
\nwith MongoDB
\nSQL links – Labs
\nhttp:
\n\/\/www.w3schools.com\/sql\/
\nhttp:
\n\/\/www.sqlite.org\/docs.html
\nWeek 5
\n01-04-2019
\nNoSQL Database Fundamentals of NoSQL
\ndatabase; Comparison of SQL
\nand NoSQL; applications of
\nPractice I\/O operations on NoSQL and MongoDB links – Labs
\nNOSQL files with diff http:
\n\/\/www.mongodb.com\/nosql- erent formats. explained
\n14
\nWeeks Topic Lecture Prac\/Lab Independent Assessments Due
\nhttps:\/\/monkessays.com\/write-my-essay\/tutorialspoint.com\/
\nmongodb\/
\nhttps:\/\/monkessays.com\/write-my-essay\/w3resource.com\/
\nmongodb\/nosql.php
\nWeek 6
\n08-04-2019
\nData format CSV, XLS, HTML, XML, JSON Write Python programmes to
\nautomatically acquire and
\nprocess Twitter tweets and
\nXML: – Labs
\nyahoo https:\/\/monkessays.com\/write-my-essay\/w3.org\/XML\/
\nfinance data. JSON: http:\/\/json.org\/
\nWeek 7
\n15-04-2019
\nData Acquisition and Wrangling
\nI and II
\nWeb APIs; Python Pandas
\nlibrary for data processing
\nAdvanced data wrangling
\nexercises with Python Pandas
\nTwitter API: – Labs
\nhttps:\/\/dev.twitter.com\/
\nPandas tutorial:
\nhttp:
\n\/\/pandas.pydata.org\/pandas- docs\/stable\/tutorials.html
\nWeek 8
\n22-04-2019
\nBREAK BREAK BREAK BREAK – Labs
\nWeek 9
\n29-04-2019
\nBREAK BREAK BREAK BREAK – Labs
\nWeek 10
\n06-05-2019
\nMapReduce I Fundamentals of MapReduce;
\nparallel processing patterns;
\nsimple examples
\nWrite Python programmes to
\nimplement simple examples of
\nMapReduce discussed in the
\nlecture
\nMapReduce links – Labs
\nhttp:\/\/research.google.com\/
\narchive\/mapreduce.html
\n(the original MapReduce paper)
\nhttp:\/\/hadoop.apache.org\/
\ndocs\/r1.2.1\/mapred
\ntutorial.html
\n(tutorial)
\n15
\nWeeks Topic Lecture Prac\/Lab Independent Assessments Due
\nWeek 11
\n13-05-2019
\nMapReduce II Fundamentals of MapReduce;
\nparallel processing patterns;
\nsimple examples
\nWrite Python programmes to
\nimplement simple examples of
\nMapReduce discussed in the
\nlecture
\nMapReduce links – Labs
\nhttp:\/\/research.google.com\/
\narchive\/mapreduce.html
\n(the original MapReduce paper)
\nhttp:\/\/hadoop.apache.org\/
\ndocs\/r1.2.1\/mapred
\ntutorial.html
\n(tutorial)
\nWeek 12
\n20-05-2019
\nPredictive Analytics Introduction to machine
\nlearning; overview of techniques
\nand tools; classfi
\nCreate an SVM classifi The Coursera Machine Learning – Labs
\ncation er for digit recognition using
\nPython scikit-learn package.
\ncourse
\nhttps:
\n\/\/www.coursera.org\/course\/ml
\nOnline collection of SVM
\ntutorials
\nhttp:\/\/svms.org\/tutorials\/
\nWeek 13 (professional nursing paper writing service, nursingscribe top experts)
\n27-05-2019
\nEXAM Unit revision In-Class Exam (Assessment 3) All lecture notes and references – In-Class Exam
\ntherein
\nWeek 14
\n03-06-2019
\nProject Project presentation Project presentation continued All lecture notes and references – Project
\ntherein
\nWeek 15
\n10-06-2019
\nWeek 16
\n17-06-2019
\nWeek 17
\n24-06-2019
\n16
\nThe above timetable should be used as a guide only, as it is subject to change. Students will be advised of any changes as they become known on the unit\u2019s vUWS site.
\n17
\n4 Learning Resources
\n4.1 Recommended Readings
\nEssential Reading
\n\u2013 Dean, J., & Ghemawat, S. (2008). Mapreduce: Simplified data processing on large clusters. Communications
\nof the ACM, 51(1), 107-113. doi: 10.1145\/1327452.1327492
\n\u2013 Gates, A. (2011). Programming Pig. Sebastopol, CA: Oreilly & Associates Inc.
\n\u2013 Holmes, A. (2012). Hadoop in practice. Shelter Island, NY: Manning.
\n\u2013 McKinney, W. (2013). Python for data analysis. Beijing: O\u2019Reilly.
\n\u2013 Miner, D., & Shook, A. (2013). MapReduce design patterns. Sebastopol, Calif.: O\u2019Reilly.
\n\u2013 Rajaraman, A., & Ullman, J. D. (2012). Mining of massive datasets. Retrieved from http:\/\/infolab.stanford.
\nedu\/\u223c ullman\/mmds.html
\n\u2013 Sadalage, P. J., & Fowler, M. (2013). NoSQL distilled: a brief guide to the emerging world of polyglot
\npersistence. Upper Saddle River, NJ: Addison-Wesley.
\n\u2013 Schutt, R., & O\u2019Neil, C. (2013). Doing data science. Beijing O\u2019Reilly Media.
\n\u2013 Stonebraker, M., Abadi, D., Dewitt, D. J., Madden, S., Paulson, E., Pavlo, A., & Rasin, A. (2010).
\nMapReduce and Parallel DBMSs: Friends or Foes? Communications of the ACM, 53(1), 64-71. doi:
\n10.1145\/1629175.1629197
\n18<\/p>\n","protected":false},"excerpt":{"rendered":"
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