Late Submission

No extensions will be granted without an approved application for Special Consideration. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late. For example, 25 hours late in submission for an assignment worth 10 marks – 20% penalty or 2 marks deducted from the total.  No submission will be accepted after solutions have been posted.

Supplementary Exam

If you receive special consideration for the final exam, a supplementary exam will be scheduled after the normal exam period, following the release of marks. By making a special consideration application for the final exam you are declaring yourself available for a resit during the supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application. Approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.

Classes

There will be one two hour lecture each week and one two hour workshop in the computing laboratory.   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 3.6 for data analysis, including a range of modules such as scikit-learn, pandas, numpy that 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.

Project Work

A major part of the assessment in this unit is based on a project that you will complete in groups.  This will allow you to explore the techniques you are learning in class in a real-world data analysis exercise. 

Unit Schedule

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

The presentation component of the group project has been moved to a video presentation to help manage the logistics of a large number of students needing to present in the final week of semester.