In recent years the world has seen an explosion in the quantity and variety of data routinely recorded and analysed by research and industry, prompting some social commentators to refer to this phenomenon as the rise of "big data," and the analysts and practitioners who investigate the data as "data scientists."
The data may come from a variety of sources, including scientific experiments and measurements, or may be recorded from human interactions such as browsing data or social networks on the Internet, mobile phone usage or financial transactions. Many companies too, are realising the value of their data for analysing customer behaviour and preferences, recognising patterns of behaviour such as credit card usage or insurance claims to detect fraud, as well as more accurately evaluating risk and increasing profit.
In order to obtain insights from big data new analytical techniques are required by practitioners. These include computationally intensive and interactive approaches such as visualisation, clustering and data mining. The management and processing of large data sets requires the development of enhanced computational resources and new algorithms to work across distributed computers.
This unit will introduce students to the analysis and management of big data using current techniques and open source and proprietary software tools. Data and case studies will be drawn from diverse sources including health and informatics, life sciences, web traffic and social networking, business data including transactions, customer traffic, scientific research and experimental data. The general principles of analysis, investigation and reporting will be covered. Students will be encouraged to critically reflect on the data analysis process within their own domain of interest.
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
(b.) Additional requirements (all students):
See also Unit timetable information
FIT1006 or ETC1000 or equivalent. (For example BUS1100, ETC1010, ETC2010, ETF2211, ETW1000, ETW1010, ETW1102, ETW2111, ETX1100, ETX2111, ETX2121, MAT1097, STA1010)
Dr John Betts
Dr Sue Bedingfield
Mr Parthan Kasarapu
Dr Jojo Wong
Mr Rui Jie Chow (RJ)
Mr Dilpreet Singh
Monash is committed to excellence in education and regularly seeks feedback from students, employers and staff. One of the key formal ways students have to provide feedback is through the Student Evaluation of Teaching and Units (SETU) survey. The University’s student evaluation policy requires that every unit is evaluated each year. Students are strongly encouraged to complete the surveys. The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.
For more information on Monash’s educational strategy, see:
www.monash.edu.au/about/monash-directions/ and on student evaluations, see: www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html
Past students have commented that learning R, RStudio and RapidMiner were highlights of the course, as was the guest lecture. These remain in this year's offering, with an increased emphasis on real world problems and applications of data science.
If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp
| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Introduction to Data Science. Introduction to R and RStudio. Review of basic statistics using R | Tutorial Participation assessed Weekly |
| 2 | Exploring data using graphics in R | |
| 3 | Data manipulation in R | |
| 4 | Linear regression in R | |
| 5 | Network analysis | Group Assignment (Initial report) due 28 August 2015 |
| 6 | Guest Lecture | |
| 7 | Classification using decision trees | |
| 8 | Comparing classification models, evaluating algorithms | |
| 9 | K-Means and hierarchical clustering | |
| 10 | Text analysis | Individual Assignment due 9 October 2015 |
| 11 | Student Presentations | Group Assignment (Presentation) due Week 11 lecture and (Final report) due 16 October 2015 |
| 12 | Review of the course and exam preparation | |
| SWOT VAC | No formal assessment is undertaken in SWOT VAC | |
| Examination period | LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/ academic/education/assessment/ assessment-in-coursework-policy.html |
*Unit Schedule details will be maintained and communicated to you via your learning system.
Examination (2 hours): 60%; In-semester assessment: 40%
| Assessment Task | Value | Due Date |
|---|---|---|
| Group Assignment | 20% | Initial report due 28 August 2015. Presentation due Week 11 lecture. Final report due 16 October 2015 |
| Individual Assignment | 10% | 9 October 2014 |
| Tutorial Participation | 10% | Weekly |
| Examination 1 | 60% | To be advised |
Faculty Policy - Unit Assessment Hurdles (http://intranet.monash.edu.au/infotech/resources/staff/edgov/policies/assessment-examinations/assessment-hurdles.html)
Academic Integrity - Please see resources and tutorials at http://www.monash.edu/library/skills/resources/tutorials/academic-integrity/
As this is a group project, students in each group will allocate a weighting of the final results to each member of the group based on a consensus estimate of each member's contribution.
On Campus Students
Off Campus Students
Monash Library Unit Reading List (if applicable to the unit)
http://readinglists.lib.monash.edu/index.html
Types of feedback you can expect to receive in this unit are:
Submission must be made by the due date otherwise penalties will be enforced.
You must negotiate any extensions formally with your campus unit leader via the in-semester special consideration process: http://www.monash.edu.au/exams/special-consideration.html
It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/student-academic-integrity-managing-plagiarism-collusion-procedures.html) for students to submit an assignment coversheet for each assessment item. Faculty Assignment coversheets can be found at http://www.infotech.monash.edu.au/resources/student/forms/. Please check with your Lecturer on the submission method for your assignment coversheet (e.g. attach a file to the online assignment submission, hand-in a hard copy, or use an electronic submission). Please note that it is your responsibility to retain copies of your assessments.
W. N. Venables, D. M. Smith. (2013). An Introduction to R. () Available from: http://www.cran.r-project.org/doc/manuals/R-intro.pdf.
M. Allerhand. (2011). A tiny handbook of R. () SpringerLink (Online service), Online access via Library.
Pang-Ning Tan, Michael Steinbach, Vipin Kumar. (2006). Introduction to data mining. () Addison-Wesley.
Luis Torgo. (2011). Data mining with R: learning with case studies. () Chapman & Hall CRC.
Foster Provost and Tom Fawcett. (2013). Data Science for Business. () O'Reilly Media, Inc..
Monash has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and to provide advice on how they might uphold them. You can find Monash’s Education Policies at: www.policy.monash.edu.au/policy-bank/academic/education/index.html
Important student resources including Faculty policies are located at http://intranet.monash.edu.au/infotech/resources/students/
The University provides many different kinds of support services for you. Contact your tutor if you need advice and see the range of services available at http://www.monash.edu.au/students. For Malaysia see http://www.monash.edu.my/Student-services, and for South Africa see http://www.monash.ac.za/current/.
The Monash University Library provides a range of services, resources and programs that enable you to save time and be more effective in your learning and research. Go to www.lib.monash.edu.au or the library tab in my.monash portal for more information. At Malaysia, visit the Library and Learning Commons at http://www.lib.monash.edu.my/. At South Africa visit http://www.lib.monash.ac.za/.