Students

COMP2200 – Data Science

2022 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Yipeng Zhou
yipeng.zhou@mq.edu.au
Guanfeng Liu
guanfeng.liu@mq.edu.au
Tutor
Subhash Sagar
subhash.sagar@mq.edu.au
Tutor
Julius Lu
jianchao.lu@mq.edu.au
Tutor
Jiwei Guan
jiwei.guan@mq.edu.au
Tutor
Venus Haghighi
venus.haghighi@mq.edu.au
Tutor
Behnaz Soltani
behnaz.soltani@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
(COMP1000 or COMP115 or COMP1010 or COMP125) and (STAT1170 or STAT170 or STAT1371 or STAT171 or STAT1250 or STAT150)
Corequisites Corequisites
Co-badged status Co-badged status
COMP6200
Unit description Unit description
This unit introduces students to the fundamental techniques and tools of data science, such as the graphical display of data, predictive models, evaluation methodologies, regression, classification and clustering. The unit provides practical experience applying these methods using industry-standard software tools to real-world data sets. Students who have completed this unit will be able to identify which data science methods are most appropriate for a real-world data set, apply these methods to the data set, and interpret the results of the analysis they have performed.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Identify the appropriate Data Science analysis for a problem and apply that method to the problem.
  • ULO2: Interpret Data Science analyses and summarise and identify the most important aspects of a Data Science analysis.
  • ULO3: Present the results of their Data Science analyses both verbally and in written form.
  • ULO4: Discuss the broader implications of Data Science analyses.

General Assessment Information

Unit Late Assessment Submission Penalty

From 1 July 2022, Students enrolled in Session based units with written assessments will have the following university standard late penalty applied. Please see https://students.mq.edu.au/study/assessment-exams/assessments for more information.

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of '0' will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11:55 pm. A 1- hour grace period is provided to students who experience a technical concern.

For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.

Assessments where Late Submissions will be accepted

In this unit, late submissions will accepted as follows:

  • Assessment workshops– YES, Standard Late Penalty applies

  • Assessment portfolios– YES, Standard Late Penalty applies

  • Assessment Critical Analysis– YES, Standard Late Penalty applies

  • Assessment exams–  NO, unless Special Consideration is Granted

Assessment Tasks

Name Weighting Hurdle Due
Workshop Checkpoints 10% Yes every week
Examinations 40% No week7 and exam week
Data Science Portfolio 35% No weeks 4, 6, 8, 10
Critical Analysis Task 15% No week 12

Workshop Checkpoints

Assessment Type 1: Participatory task
Indicative Time on Task 2: 0 hours
Due: every week
Weighting: 10%
This is a hurdle assessment task (see assessment policy for more information on hurdle assessment tasks)

 

A submission of a small task based on the workshop each week. This may be a short quiz or the result of a practical task.

 
On successful completion you will be able to:
  • Interpret Data Science analyses and summarise and identify the most important aspects of a Data Science analysis.
  • Present the results of their Data Science analyses both verbally and in written form.

Examinations

Assessment Type 1: Examination
Indicative Time on Task 2: 10 hours
Due: week7 and exam week
Weighting: 40%

 

Examinations will assess your knowledge and understanding of the data analysis and machine learning methods covered in the semester.

 
On successful completion you will be able to:
  • Interpret Data Science analyses and summarise and identify the most important aspects of a Data Science analysis.
  • Discuss the broader implications of Data Science analyses.

Data Science Portfolio

Assessment Type 1: Portfolio
Indicative Time on Task 2: 45 hours
Due: weeks 4, 6, 8, 10
Weighting: 35%

 

The portfolio assessment will consist of a number of data analysis problems that you will be given through the semester. These will involve writing code to analyse one or more data sets. These will be marked individually through the semester and then as an overall portfolio at the end of semester.

 
On successful completion you will be able to:
  • Identify the appropriate Data Science analysis for a problem and apply that method to the problem.
  • Interpret Data Science analyses and summarise and identify the most important aspects of a Data Science analysis.
  • Present the results of their Data Science analyses both verbally and in written form.
  • Discuss the broader implications of Data Science analyses.

Critical Analysis Task

Assessment Type 1: Report
Indicative Time on Task 2: 15 hours
Due: week 12
Weighting: 15%

 

You will be given a sample notebook describing the analysis of a dataset. You will provide a critical analysis of this notebook and suggest improvements in the way that data is analysed and results are presented.

 
On successful completion you will be able to:
  • Identify the appropriate Data Science analysis for a problem and apply that method to the problem.
  • Interpret Data Science analyses and summarise and identify the most important aspects of a Data Science analysis.
  • Present the results of their Data Science analyses both verbally and in written form.
  • Discuss the broader implications of Data Science analyses.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

Classes

There will be one two hour online lecture each week, and one two hour workshop in the computing laboratory or online. The online lecture would be in the form of live streaming or pre-recorded lecture videos. You are expected to attend both classes as they provide complimentary learning activities each week. In practical classes you will write code and experiment with various data sets; in lectures we will discuss the methods you are learning and how the results of your analysis can be interpreted. 

Textbooks

We will refer to the following texts during the semester:

Introduction to Data Science A Python Approach to Concepts, Techniques and Applications Igual, Laura, Seguí, Santi (electronic edition available via MQ Library)

Computational and Inferential Thinking: The Foundations of Data Science By Ani Adhikari and John DeNero (available on GitBooks)

You will be given readings from these and other sources each week. 

Technology Used and Required

We will make use of Python 3 for data analysis, including a range of modules such as scikit-learn, pandas, numpythat provide additional features.  These can all be installed via the Anaconda Python distribution.  We will discuss this environment and the installation process in the first week of classes. 

We will use Jupyter Notebook as a way of developing and presenting the analysis results.  This is included in the full Anaconda distribution.

Unit Schedule

Unit Schedule

The indicative list of topics is shown here, this is subject to change based on feedback from the class.  

1

Overview of DS, Learning Python, Notebooks

SS

2

Data formats, Python input and output

SS

3

Descriptive Statistics, simple visualisation

SS

4

Causality and correlation; Visualisation

SS

5

Predictive Modelling: Linear and Logistic Regression

SS

6

Software Engineering for Data Science

SS

7

Feature Engineering; Unsupervised Learning

SS/XZ

 

 

 

8

K-Nearest Neighbours Classifiers

XZ

9

Naive Bayes Classifiers

XZ

10

Artificial Neural Networks

 XZ

11

Decision Tree Models

XZ

12

Advanced Topics / Guest Lecture

Guest

13

 Summary

All

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

The Writing Centre

The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via AskMQ, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Unit information based on version 2022.04 of the Handbook