Data Science Essentials I Course

Data science is an interdisciplinary field that combines elements of mathematics, statistics, data analytics and more. The purpose of data science is to gather value and insight from raw data so as to give businesses actionable insights. Data scientists use a range of tools to extra data from the internet, smart devices, workstations and customers.

Course Overview

This self-directed, online course is perfect for busy professionals looking to develop their skill base and gain an introduction to the fundamentals of Data Science. 

In just six weeks, you’ll learn to identify and propose solutions to problems in organisational contexts, and explore techniques, concepts and common tools used by data scientists to gather and analyse data.

What is a micro-credential?

A micro-credential is a self-directed online course of study, where you will work through the course material in a flexible manner (within a set overall duration, e.g. six weeks) and complete an assessment to gain a credential. There are no classes, but you will have access to an academic subject matter expert who will support you as you work on your assessment and grade your assessment at the end of the course. 

This course is designed to be undertaken online, in a self-directed fashion. It should take 51 hours to complete this micro-credential and it is recommended that you allocate about 17 hours of self-paced learning to each unit. The content is divided into three units, which each consist of three modules. Your facilitator will provide more instructions on how to split your time over the weeks available in this course. 

Additionally, these units aim to achieve a number of unit learning outcomes that will help shape your understanding of data science. A breakdown of the course structure can be seen below.

Unit Learning Outcomes:

• Define data science
• Explain the characteristics of big data
• Describe the role of data science in business and organisational contexts
• Explain data science methodology
• Describe the process of defining a question for data scientists
• Identify tools common to data scientists
• Describe data collection processes of data scientists
• Apply data requirements and data collection to data science problems
• Describe the role of ethics and security practices for data scientists