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DATA2002: Data Analytics: Learning from Data (2019 - Semester 2)

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Unit: DATA2002: Data Analytics: Learning from Data (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Intermediate
Faculty/School: School of Mathematics and Statistics
Unit Coordinator/s:
Session options: Semester 2
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: (DATA1001 OR ENVX1001 OR ENVX1002 OR BUSS1020) OR (MATH1005 AND MATH1115 OR STAT2011) OR [(MATH1905 AND MATH1XXX]. MATH1XXX cannot be MATH1005
Prohibitions: STAT2012 OR STAT2912 OR DATA2902.
Brief Handbook Description: Technological advances in science, business, engineering has given rise to a proliferation of data from all aspects of our life. Understanding the information presented in these data is critical as it enables informed decision making into many areas including market intelligence and science. DATA2002 is an intermediate course in statistics and data sciences, focusing on learning data analytic skills for a wide range of problems and data. How should the Australian government measure and report employment and unemployment? Can we tell the difference between decaffeinated and regular coffee? In this course, you will learn how to ingest, combine and summarise data from a variety of data models which are typically encountered in data science projects as well as reinforcing their programming skills through experience with statistical programming language. You will also be exposed to the concept of statistical machine learning and develop the skill to analyze various types of data in order to answer a scientific question. From this unit, you will develop knowledge and skills that will enable you to embrace data analytic challenges stemming from everyday problems.
Assumed Knowledge: None.
Tutor/s: Lecturer: Dr. Garth Tarr (garth.tarr@sydney.edu.au)
Timetable: DATA2002 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 3.00 1 13
2 Tutorial 2.00 1 13

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

(8) Professional Effectiveness and Ethical Conduct (Level 2)
1. Develop expertise in the use of a software version control system.
(6) Communication and Inquiry/ Research (Level 2)
2. Formulate domain/context specific questions and identify appropriate statistical analysis.

Create a reproducible report to communicate outcomes using a programming language.
(5) Interdisciplinary, Inclusiveness, Influence (Level 2)
3. Formulate domain/context specific questions and identify appropriate statistical analysis.

Construct, interpret and compare numerical and graphical summaries of different data types including large and/or complex data sets.

Develop expertise in the use of a software version control system.

Identify, justify and implement an appropriate parametric or non-parametric two sample statistical test.

Formulate, evaluate and interpret appropriate linear models to describe the relationships between multiple factors.

Communicate outcomes in a manner appropriate to the audience.
(3) Problem Solving and Inventiveness (Level 2)
4. Formulate domain/context specific questions and identify appropriate statistical analysis.

Extract and combine data from multiple data resources.

Construct, interpret and compare numerical and graphical summaries of different data types including large and/or complex data sets.

Identify, justify and implement an appropriate parametric or non-parametric two sample statistical test.

Formulate, evaluate and interpret appropriate linear models to describe the relationships between multiple factors.
(1) Maths/ Science Methods and Tools (Level 2)
5. Perform statistical machine learning using a given classifier, and create a cross-validation scheme to calculate the prediction accuracy.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Exam No 60.00 Exam Period
2 Computer practical Yes 20.00 Multiple Weeks
3 Practical exam No 10.00 Multiple Weeks
4 Final project Yes 10.00 Multiple Weeks
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Exam Period Assessment Due: Exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Bachelor of Advanced Computing (Computational Data Science) 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Commerce 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Health) 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Medical Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computer Science Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Information Systems Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Software Development) 2018, 2019, 2020
Biomedical Mid-Year 2016, 2017, 2018, 2019, 2020
Biomedical 2016, 2017, 2018, 2019, 2020
Software Mid-Year 2019, 2020
Software 2018, 2019, 2020
Bachelor of Project Management (Built Environment) 2018
Bachelor of Project Management (Civil Engineering Science) 2018
Bachelor of Project Management (Software) 2018
Bachelor of Project Management (Built Environment) Mid-Year 2018
Bachelor of Project Management (Civil Engineering Science) Mid-Year 2018
Bachelor of Project Management (Software) Mid-Year 2018

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(8) Professional Effectiveness and Ethical Conduct (Level 2) No 0%
(6) Communication and Inquiry/ Research (Level 2) No 0%
(5) Interdisciplinary, Inclusiveness, Influence (Level 2) No 0%
(3) Problem Solving and Inventiveness (Level 2) No 0%
(1) Maths/ Science Methods and Tools (Level 2) No 0%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.