程序代写代做代考 Excel python case study data science matlab Teaching Team

Teaching Team
 Unit Co-ordinator & Lecturer: Professor Junbin Gao

Consultation hour: Tuesdays 14:00-15:00.
Room 4085, H70.

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 Lecturer: Dr Jie Yin

Consultation hour: Mondays 15:30 – 16:30
Room 4035, H70.

 Lecturer: Dr Thang Bui

Consultation hour: Wednesdays 14:30 – 15:30
Room 4037, H70.

Lectures
 This unit is offered by the Discipline of Business Analytics,

Lectures (Week 1 to Week 13)
Time: Mondays 13:00-15:00

Room: ABS Case Study Lecture Theatre 1060, H70
Lecturer: Professor Junbin Gao

Time: Mondays 16:00-18:00
Room: ABS Case Study Lecture Theatre 1060, H70
Lecturer: Dr Thang Bui

Time: Tuesdays 14:00-16:00
Room: Carslaw Lecture Theatre 273
Lecturer: Dr Jie Yin

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Tutorials

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Tutorials (Week 1 to Week 13)
 Time: Mon 12:00 or 18:00 or 19:00

Room: Codrington Computer Laboratory 2 (H69 building)

 Time: Mon 17:00
Room: Codrington Computer Laboratory 4 (H69 building)

 Time: Tue 10:00 or 18:00 or 19:00
Room: Codrington Computer Laboratory 2 (H69 building)

 Time: Tue 12:00
Room: Codrington Computer Laboratory 5 (H69 building)

 Time: Wed 17:00
Room: Codrington Computer Laboratory 2 (H69 building)

 Time: Thu 17:00 and Fri 09:00
Room: Codrington Computer Laboratory 2 (H69 building)

 [The Stream with Special Time Slots]
Time: 18:00 Wed (week 3,5,7,9,11,13) and 19:30 Wed (week 1-2,4,6,8,10,12)
Room: Codrington Computer Laboratory 2 (H69 building)

Note:
This unit contains a 1-hours tutorial which focuses on practical exercises using Python

 Students must attend their centrally assigned tutorials

Tutorials

 Pattern Recognition and Machine Learning (2006), Chris M. Bishop,
Springer. (Bishop, 2006)

 Data Science for Business (2013), Foster Provost and Tom Fawcett, O’Reilly
Media, Inc. (Provost and Fawcett, 2013)

 Introduction to Machine Learning (2014), Ethem Alpaydin. The MIT Press.

(Alpaydin, 2014)

 The Elements of Statistical Learning (2001), Friedman, Jerome, Trevor
Hastie, and Robert Tibshirani. Springer, Berlin: Springer series in statistics.

(Friedman et al., 2001)

 An introduction to statistical learning: With applications in R (2014), Gareth
James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. Springer-Verlag,
New York: Springer Texts in Statistics. (James et al., 2014)

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Suggested Reading List

Software
 Python (used in this course)
Free and works on PCs, Mac, Unix/Linux

Does statistical modelling, visualisation and programming

Can be used for almost all models to be discussed in this class

 Matlab
Licensed: USyd provides a license to enrolled students and can be

downloaded and installed on personal computers

 No technical support from the teaching team

Other languages: R, SAS etc. However excel is not enough to
complete most machine learning tasks

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 The unit requirements:
 Attend a 2-hour lecture per week

 Attend a 1-hour tutorial class per week

 Submit individual assignments 1&2 – 10% each

 Submit the group project report – 20%

 Complete mid-semester exam – 20%

 Complete the final exam – 40%

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Workload

Weekly Schedules

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Weekly Schedules

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Advice

 You should spend a minimum of 12 hours per
week on this unit.

 You must attend all lectures & tutorial, and
complete all assessment items.

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Communication with Staff
 For general administrative inquiries:

– Contact Ms Colleen, Phone: 9351 3069

– Discipline Executive Officer of the Discipline of Business Analytics, in Room 4082, Abercrombie Building

 For inquiries about teaching materials (Technical)
– Preferred method of communication is joining the consultation during office hour, and posting your

questions on Ed discussion

– Enquiries sent by email will not be accepted.

 For administrative & all other general inquiries about this unit (Non-
technical)

– Preferred method of communication is verbal, during office hour.

– Email correspondence is also preferred.

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Communication with Staff

 Many emails are received every day, so there is no guarantee that your

emails will be answered immediately.

 Emails sent from non-university email account will not be read & replied.

 Emails with correct subject line will receive high priority.

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Need Help?

 Discipline of Business Analytics

– Unit Coordinator, Lecturers and Tutors

 Business School

– PASS (Peer-Assisted Study Sessions) – Free enrolment
http://sydney.edu.au/business/learning/students/pass

– Maths in Business – Free enrolment

http://sydney.edu.au/business/learning/students/maths

 Faculty of Science
– Mathematics Learning Centre at Level 4, Carslaw

Building (Email: mlc.enquiries@sydney.edu.au)

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Big data in Business specialisation

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http://sydney.edu.au/courses/master-of-commerce/specialisation-big-data-in-business

QBUS6850� �Machine Learning for Business � �Introduction to the Unit��Professor Junbin Gao�
Teaching Team
Lectures
Tutorials
Tutorials
Suggested Reading List
Software
Workload
Weekly Schedules
Weekly Schedules
Advice
Communication with Staff
Communication with Staff
Need Help?
Big data in Business specialisation
Hope you enjoy this unit!�Discipline of Business Analytics �University of Sydney Business School�sydney.edu.au/business/business.analytics