Note: This unit version is currently under review and is subject to change!

INFO3406: Introduction to Data Analytics (2018 - Semester 2)

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Unit: INFO3406: Introduction to Data Analytics (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Senior
Faculty/School: School of Computer Science
Unit Coordinator/s: Anaissi, Ali
Session options: Semester 2
Versions for this Unit:
Site(s) for this Unit: https://canvas.sydney.edu.au/courses/4545/pages/data-analysis-skills#OLEO1300'
Campus: Camperdown/Darlington
Pre-Requisites: (MATH1005 OR MATH1905) AND (INFO2120 OR INFO2820).
Brief Handbook Description: Big Data refers to datasets that are massive, heterogenous, and dynamic that are beyond current approaches for the capture, storage, management, and analysis of the data. The focus of this unit is on understanding and applying relevant concepts, techniques, algorithms, and tools for the analysis, management and visualization of big data – with the goal of keeping abreast of the continual increase in the volume and complexity of data sets and enabling discovery of information and knowledge to guide effective decision making.

Core data analytics content will be taught in normal lecture + tutorial delivery mode. Python programming will be taught through an online learning platform in addition to the weekly face-to-face lecture/tutorials. The unit of study will include hands-on exercises covering the range of data science skills above.
Assumed Knowledge: Basic statistics and database management.
Lecturer/s: Anaissi, Ali
Liu, Tongliang
Timetable: INFO3406 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Laboratory 1.00 1 13

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method Attribute Developed
Students learn and practice the design of a pipeline of processes to analyse a huge set of complex data. Design (Level 3)
Students are given scenario(s) that require them to use various components and tools to create a pipeline to process a set of complex data. Students have to articulate and substantiate their choice of computational methods & tools used in the process owing to technical, social and application constraints in the given setting. Engineering/IT Specialisation (Level 3)
Students are required to determine and identify appropriate tools and methods to pre-process heterogenous data coming from different channels in the practical assessment. Through their assignment, Different tools are provide to students but they have to come up with rational choice of tools to analyse & to clean up data. Maths/Science Methods and Tools (Level 3)
Students are required to perform requirements analysis through the practical assessment. They have to identify implicit & explicit requirements in a given project brief. Students should also explore the implied constraints through literature after synthesising the given requirements. Information Seeking (Level 3)
Students practice their written and oral communication skills through the assessments. They need to articulate well the aim and issues of the problems, the social and technical constraints, the reasons behind decision choices. They should be able to discuss and draw insights from the results through their analytical work. Communication (Level 3)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table 2018.

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.

Engineering/IT Specialisation (Level 3)
1. Student understands the role of data analysis in decision-making
2. Student understands the technical issues that are present in the stages of a data analysis task and the properties of different technologies and tools that can be used to deal with the issues
3. Student can process large data sets using appropriate technologies
Maths/Science Methods and Tools (Level 3)
4. Student can select statistical techniques appropriate for summarization and analysis of a data set, and can justify their choice
5. Student can select statistical techniques appropriate for evaluation of a predictive model that is based on data analysis, and can justify their choice
6. Student can apply concepts and terms from social science to describe and analyse the role of a data analysis task in its organizational context
Information Seeking (Level 3)
7. Student can identify explicit and implicit requirements for carrying out a data analysis task to meet stakeholder purposes
8. Student can find out details of how to use a method or tool in the data analytic process.
Design (Level 3)
9. Students can carry out (in guided stages) the whole design and implementation cycle for creating a pipeline to analyse a large heterogenous dataset
Communication (Level 3)
10. Student can communicate the results produced by an analysis pipeline, in oral and written form, including meaningful diagrams
11. Student can communicate the process used to analyse a large data set, and justify the methods used.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Participation No 10.00 Multiple Weeks 1, 2, 4, 5, 7, 9,
2 Project Stage 1 No 13.00 Week 6 3, 8, 9,
3 Project Stage 2 No 20.00 Week 12 3, 4, 5, 8, 9,
4 Project Stage 3 No 7.00 Week 12 6, 8, 10,
5 Written exam No 50.00 Exam Period 1, 2, 4, 5, 6, 7,
Assessment Description: Participation: Complete and submit lab exercises [10 marks].

Project Stage 1: Obtain data, clean it, load and summarize [13 marks individual work; due week 6]

Project Stage 2: Analyse the data, develop and test a predictive model [20 marks individual work, due week 12]

Project Stage 3: presentation of results [7 marks individual work; due week 12]
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.
Minimum Pass Requirement It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.
Policies & Procedures: IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of IT may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Other policies

See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Online Course Content: This subject will use Python as programming language throughout the course. An online tutorial on Python Programming and Database Management is made available through the Grok learning platform. For the best learning effect, students should start working on this Python tutorial already before the semester start.

Please go to this link ' https://canvas.sydney.edu.au/courses/4545/pages/data-analysis-skills#OLEO1300'

and enroll yourself in the following free courses:

- Beginner Programming for Data Analysis (OLEO1306)

- Managing and Analysing Data: Introduction to SQL (OLEO1300)
Note on Resources: Lecture notes, tutorial notes and links to online questions will be provided on Canvas.

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
Week 1 Introduction to Data Science and Big Data
Week 2 Data Exploration with Spreadsheets
Week 3 Data Exploration with Python
Week 4 Cleaning and Storing Data
Week 5 Querying and Summarising Data
Week 6 Hypothesis Testing and Evaluation
Assessment Due: Project Stage 1
Week 7 Data Mining - Association Rules and Dimensionality Reduction
Week 8 Data Mining - Clustering
Week 9 Machine Learning - Regression
Week 10 Machine Learning - Classification
Week 11 Unstructured Data
Week 12 Information, actionable knowledge from data, and link to effective decision making.
Assessment Due: Project Stage 2
Assessment Due: Project Stage 3
Week 13 Revision
Exam Period Assessment Due: Written exam

Course Relations

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

Course Year(s) Offered
Bachelor of Computer Science and Technology 2015, 2016, 2017
Bachelor of Computer Science and Technology (Advanced) 2015, 2016, 2017
Bachelor of Computer Science and Technology (Computer Science) 2014 and earlier 2014
Bachelor of Computer Science and Technology (Computer Science)(Advanced) 2014 and earlier 2014
Bachelor of Computer Science and Technology (Information Systems) 2014 and earlier 2014
Bachelor of Computer Science and Technology (Information Systems)(Advanced) 2014 and earlier 2014
Bachelor of Computer Science & Tech. Mid-Year 2016, 2017
Aeronautical Engineering / Science 2014
Aeronautical Engineering (Space) / Science 2014
Biomedical Engineering / Science 2014
Electrical Engineering / Science 2014
Electrical Engineering (Computer) / Science 2014
Electrical Engineering (Power) / Science 2014
Electrical Engineering (Telecommunications) / Science 2014
Aeronautical / Science 2015, 2016, 2017
Aeronautical (Space) / Science 2015
Biomedical Mid-Year 2016
Biomedical 2016
Biomedical /Science 2015, 2016, 2017
Electrical / Science 2015
Electrical (Computer) / Science 2015
Electrical (Power) / Science 2015
Electrical (Telecommunications) / Science 2015
Mechanical / Science 2015, 2016, 2017
Mechanical (Space) / Science 2015
Mechatronic / Science 2015, 2016, 2017
Mechatronic (Space) / Science 2015
Mechanical Engineering / Science 2014
Mechanical Engineering (Space) / Science 2014
Mechatronic Engineering / Science 2014
Mechatronic Engineering (Space) / Science 2014
Bachelor of Information Technology 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Arts 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Commerce 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Medical Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Science 2015, 2016, 2017
Bachelor of Information Technology (Computer Science) 2014 and earlier 2014
Bachelor of Information Technology (Information Systems) 2014 and earlier 2014
Bachelor of Information Technology/Bachelor of Laws 2015, 2016, 2017

Course Goals

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

Attribute Practiced Assessed
Engineering/IT Specialisation (Level 3) Yes 30.6%
Maths/Science Methods and Tools (Level 3) Yes 34.9%
Information Seeking (Level 3) Yes 17%
Design (Level 3) Yes 13.3%
Communication (Level 3) Yes 4.2%

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.