CITS2402 Introduction to Data Science

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 2UWA (Perth)Face to face
Details for undergraduate courses
  • Level 2 core unit in the Data Science major sequence
  • The area of knowledge for this unit is Mathematical and Physical Sciences
  • Category B broadening unit for students
  • Level 2 elective
Content
Data science is a booming field that builds on the recent advancement of computational treatment of data. Unprecedented amount of data are ubiquitously and continuously generated from almost all facets of our modern society, which calls for professionally trained data scientists to turn raw data of various formats into valuable corporate intelligence. This unit will focus on the overall lifecycle of a data science project, introducing appropriate techniques and tools for key data science stages. Starting from data extraction, integration, cleansing, transformation and representation for tabular data and time series data, the unit will introduce practical tools and packages that step through the data science lifecycle of exploratory data visualisation, statistical modelling as well as basic machine learning techniques such as clustering and classification. Critical assessment of the data analytics outcome will be communicated back to the stake holder through an integrated reporting environment.
Outcomes
Students are able to (1) explain the work flow and key components of a data science project; (2) use appropriate tools for data processing, data modelling and evaluations; (3) identify business questions that can be answered for a given dataset; (4) apply appropriate visualisation methods for different types of data, including categorical, numerical and time series data; and (5) critically assess the application and outcome of Data Science workflow to a business problem.
Assessment
Indicative assessments in this unit are as follows: (1) midterm test; (2) project; and (3) exam. Further information is available in the unit outline.

Supplementary assessment is not available in this unit except in the case of a bachelor's pass degree student who has obtained a mark of 45 to 49 overall and is currently enrolled in this unit, and it is the only remaining unit that the student must pass in order to complete their course.
Unit rules
Prerequisites:
CITS1401 Computational Thinking with Python
or
CITS2401 Computer Analysis and Visualisation
Contact hours
lectures: 2 x 1 hour per week; laboratory: 1 x 2 hours per week.
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