This unit looks at processes and case studies to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed.
Minimum total expected workload equals 12 hours per week comprising:
See also Unit timetable information
Wray Buntine
Consultation hours: Tues 13:00-15:00
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| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Overview of data science and what a project looks like. | |
| 2 | Roles of a data scientist, and the impact of the field. | |
| 3 | Data business models. | |
| 4 | Application areas and case studies. | |
| 5 | Characterising data and "big" data. | |
| 6 | Data sources and case studies. | |
| 7 | Resources and standards. | |
| 8 | Resources case studies. | Assignment 1 |
| 9 | Data analysis theory | |
| 10 | Data analysis process. | |
| 11 | Issues in data management | |
| 12 | Data management frameworks. | Assignment 2 |
| SWOT VAC | No formal assessment is undertaken during 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 (3 hours): 60%; In-semester assessment: 40%
| Assessment Task | Value | Due Date |
|---|---|---|
| Assessment 1: Business and data case study | 20% | Friday Week 8 |
| Assessment 2: Data science resources | 20% | Friday Week 12 |
| 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/
Your report will be assessed in terms of:
The report will be assessed in terms of:
Classifying the kinds of data analysis and statistical methods available for a data science project
The paired pitches will be compared to insure proper teamwork.
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.
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