Course Title: Practical Data Science

Part A: Course Overview

Course Title: Practical Data Science

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


171H School of Science


Sem 1 2017,
Sem 1 2018,
Sem 1 2019

Course Coordinator: Dr. Yongli Ren

Course Coordinator Phone: +61 3 9925 2859

Course Coordinator Email:

Course Coordinator Location: 14.9.7

Course Coordinator Availability: By appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study (not enforced pre-requisite): Before starting this course, you should complete the Lab and Unix Induction programs. 

Course Description

The course gives you a set of practical skills for handling data that comes in a variety of formats and sizes, such as texts, spatial and time series data. These skills cover the data analysis lifecycle from initial access and acquisition, modeling, transformation, integration, querying, application of statistical learning and data mining methods, and presentation of results. This includes data wrangling, the process of converting raw data into a more useful form that can be subsequently analysed. The course is hands-on, using python, in the iPython interactive computing framework.

Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the following Program Learning Outcomes for MC267Master of Data Science:

Enabling Knowledge: You will gain skills as you apply knowledge with creativity and initiative to new situations. In doing so, you will:

  • Demonstrate mastery of a body of knowledge that includes recent developments in computer science and information technology;
  • Understand and use appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools;
  • Recognise and use research principles and methods applicable to data science.

Critical Analysis: You will learn to accurately and objectively examine, and critically investigate computer science, information technology (IT) and statistical concepts, evidence, theories or situations, in particular to:

  • Analyse and model complex requirements and constraints for the purpose of designing and implementing software artefacts and IT systems;
  • Evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements;
  • Bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of statistical problems.

Problem Solving: Your capability to analyse complex problems and synthesise suitable solutions will be extended as you learn to:

  • Design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification;
  • Apply an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Course Learning Outcomes

On completion of this course you should be able to:

  1. Wrangle data including: selecting, uploading, cleaning up and transforming the data into a format suitable for a data science pipeline
  2. Extract an interpretation of data using exploratory data analysis
  3. Manipulate data by creating new features, reducing dimensionality, and by handling outliers in the data
  4. Apply simple machine learning tools to the data
  5. Visualise and plot graphical representations of data.

Overview of Learning Activities

You will learn about key concepts in lectures, classes or online, where you can engage with course material and the subject matter being illustrated through demonstrations and examples.

Tutorials, workshops and/or labs and/or group discussions (including online forums) focused on projects and problem solving will provide you practice in the application of theory and procedures. You will explore the concepts with teaching staff and other students, and receive feedback on your progress. You will develop an integrated understanding of the subject matter through private study by working through the course as presented in classes. Comprehensive learning materials will aid you in gaining practice at solving conceptual and technical problems. 

This course includes 2 hours per week of lectures and 2 hours per week of tutorial/laboratory classes. To achieve high levels of academic results you are expected to spend on average an additional 6 hours per week on self-directed independent learning (reading, online activities and assignments).

Overview of Learning Resources

You will make extensive use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through myRMIT and may be provided with copies of additional materials in class or via email.

Lists of relevant reference texts[1], resources in the library and freely accessible Internet sites will be provided.

[1] For example: Luca Massaron, Alberto Boschetti, Python Data Science Essentials - Learn the fundamentals of Data Science with Python, Packt Publishing, 2015,ISBN: 978-1785280429.

Overview of Assessment

This course has no hurdle requirements.

The assessment for this course comprises practical work involving the development of computer programs, a class test, and a final exam. 


Assessment tasks

Assessment Task 1:  Practical Assignment 1

Weighting 15%

This assessment task supports CLOs 1,2,5

Assessment Task 2: Practical Assignment 2

Weighting 35%

This assessment task supports CLOs 3,4,5

Assessment 3: Exam

Weighting 50% 

This assessment supports CLOs 1,2,3,4,5