Note: This unit is an archived version! See Overview tab for delivered versions.
DATA2002: Data Analytics: Learning from Data (2018 - Semester 2)
| 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: |
A/Prof Roehm, Uwe
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| Session options: | Semester 2 |
| Versions for this Unit: |
| Campus: | Camperdown/Darlington |
| Pre-Requisites: | (DATA1001 OR ENVX1001 OR ENVX1002) OR (MATH1005 AND MATH1115 OR STAT2011) OR [(MATH1905 AND MATH1XXX]. MATH1XXX cannot be MATH1005 |
| Prohibitions: | STAT2012 OR STAT2912. |
| 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. |
| Lecturer/s: |
A/Prof Roehm, Uwe
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| Timetable: | DATA2002 Timetable | |||||||||||||||
| Time Commitment: |
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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.
InfluenceCreate a reproducible report to communicate outcomes using a programming language.
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.
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.
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.
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.
Create a reproducible report to communicate outcomes using a programming language.
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.
Perform statistical machine learning using a given classifier, and create a cross-validation scheme to calculate the prediction accuracy.
Extract and combine data from multiple data resources.
Identify, justify and implement an appropriate parametric or non-parametric two sample statistical test.
Create a reproducible report to communicate outcomes using a programming language.
| Assessment Methods: |
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| Grading: |
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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 Goals
This unit contributes to the achievement of the following course goals:
| Attribute | Practiced | Assessed |
| Influence | No | 0% |
| An Integrated Professional, Ethical and Personal Identity | No | 0% |
| Interdisciplinary effectiveness | No | 0% |
| Cultural Competence | No | 0% |
| Depth of Disciplinary Expertise | 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.
