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CITS4009 Introduction to Data Science
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CITS4009 Introduction to Data Science - Unit information for 2017

This unit answers an urgent call to harness the unprecedented amount of data now generated from every facet of our daily life by introducing data science as the discipline dealing with collecting, representing, manipulating and visualising data in Contemporary society. Students taking the unit learn to write computer programs to extract, transform and integrate data from multiple heterogeneous sources, including traditional relational databases and web-based resources. Another core objective is the development of programming skills to enable interactive Web-based access and visualisation of the data. Students are given the opportunity to put the learned knowledge in data acquisition, data processing, data representation and exploratory visualisation into practice through projects that are highly relevant to real world data analytics. The unit provides the fundamental knowledge, introduces the essential processes and builds the specific critical programming skills required during the journey of growing a student into a capable data scientist.

CITS4009 introduces the key concepts of fundamentals of data extraction, data cleaning and developing data analysis pipelines using R and Python programming languages. Students are able to (1) write programs to systematically collect, process and integrate data from traditional databases and web-based resources; (2) understand and make choices over an array of data representation and storage options; and (3) demonstrate programming abilities to build web-based interface for exploratory data visualisation.

Information available from here:

Unit coordination:

Unit Coordinator: Prof. Amitava Datta
Prof. Mark Reynolds
Tutor: Thomas Smoker
Lecture times: 12:00 - 2:00pm Thursdays (RBST: Robert Street Lecture Theatre)
Laboratory sessions: 10:00-12:00 Mondays (CSSE Lab 2.03)
12:00-2:00 Mondays (CSSE Lab 2.03)
9:00-11:00 Tuesdays (CSSE Lab 2.03)
Email discussion list for CITS4009: help4009
Consultation times: 10:00 - 11:00am Fridays

There are no labs in the first two weeks.

Assessment and important dates:

Assessment Dates
 Mid Semester Test
  September 14 (second hour of lecture)
  Project Released
  September 25
 Project Due
  October 31, 11:59 pm
 Final exam
 2 hours, November

Before undertaking this unit, students are strongly encouraged to read:

Suggested textbooks for 2017:

  1. Practical Data Science with R, by, Nina Zumel and John Mount, Manning, 2014.

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