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[an error occurred while processing this directive]This unit explores the statistical modelling foundations that underlie the analytic aspects of Data Science. Motivated by case studies and working through real examples, this unit covers the mathematical and statistical basis with an emphasis on using the techniques in practice. It introduces data collection, sampling and quality. It considers analytic tasks such as statistical hypothesis testing and exploratory versus confirmatory analysis. It presents basic probability distributions, random number generation and simulation as well as estimation methods and effects such as maximum likelihood estimators, Monte Carlo estimators, Bayes theorem, bias versus variance and cross validation. Basic information theory and dependence models such as Bayesian networks and log-linear models are also presented, as well as the role of general modelling such as inference and decision making, predictive models, experts and assessing probabilities.
Minimum total expected workload equals 144 hours per semester comprising:
See also Unit timetable information
Students need to have the equivalent of first year undergraduate university mathematics as taught in an analytics degree such as Engineering, Finance, Physics and some Computer Science degrees.
Muhammad Amar
Kevin Korb
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
Week | Activities | Assessment |
---|---|---|
0 | No formal assessment or activities are undertaken in week 0 | |
1 | Module one - Introduction to Modelling for Data Science | Programming Assignment 1 |
2 | Module two - Data analysis | Programming Assignment 2 |
3 | Module three - Dependence, regression and clustering | Programming Assignment 3; Quiz |
4 | Module four - Statistical inference and evaluation | Programming Assignment 4 |
5 | Module five - Simulation | Programming Assignment 5 |
6 | Module six - Modelling and validation | Programming Assignment 6; Quiz; Report |
7 | ||
8 | ||
9 | ||
10 | ||
11 | ||
12 | ||
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.
In-semester assessment: 100%
Assessment Task | Value | Due Date |
---|---|---|
Programming Assignment 1 | 5% | 1/11/15 |
Programming Assignment 2 | 5% | 8/11/15 |
Programming Assignment 3 | 5% | 15/11/15 |
Quiz I | 25% | 15/11/15 |
Programming Assignment 4 | 5% | 22/11/15 |
Programming Assignment 5 | 5% | 29/11/15 |
Programming Assignment 6 | 5% | 6/12/15 |
Quiz II | 25% | 6/12/15 |
Report | 20% | 6/12/15 |
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/
Correctness of result; program commentary/explanation.
Correctness of result; program commentary/explanation.
Correctness of result; program commentary/explanation.
Correctness
Correctness of result; program commentary/explanation.
Correctness of result; program commentary/explanation.
Correctness of result; program commentary/explanation.
Correctness
Students will be assessed according to: the clarity & organization of the report; the appropriateness of tools used; the explanation of analytic choices made; the quality of the interpretation of results; the correctness of conclusions drawn and support provided for those conclusions; the individual contribution made to the overall report for group work.
Ross, S.M. (2014) Introduction to Probability and Statistics for Engineers and Scientists, 5th ed. Academic Press. (Available from Monash Library)
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
Students should use APA reference style, as explained at http://intranet.monash.edu.au/infotech/resources/students/style-guide/referencing.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.
Assessment work should be submitted via Moodle.
Please check with your lecturer before purchasing any Required Resources. Limited copies of prescribed texts are available for you to borrow in the library, and prescribed software is available in student labs.
R and RStudio
Limited copies of prescribed texts are available for you to borrow in the library.
Ross. (2014). Introduction to Probability and Statistics for Engineers and Scientists, 5th ed. (5th) Academic.
Students may use Windows, Linux or Mac environments for this subject. R and/or RStudio must be used for programming assignments. Other tools will be useful or necessary, but the choice of a particular tool (e.g., Weka, MATLAB, or a spreadsheet) is not mandated.
Weka. See http://www.cs.waikato.ac.nz/ml/weka/
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/.