Data Science
Develop a strong foundation in data science and learn how data can be collected, prepared, analysed and interpreted to support informed decisions and solve practical problems.
About This Course
Data Science introduces students to the principles and practices used to extract useful knowledge and insights from data.
Students will explore data collection, preparation, exploration, visualisation, statistics and introductory predictive techniques. The course also introduces the role of programming and analytical tools in working with real-world datasets.
Students will also consider data quality, responsible use of data and the importance of communicating analytical findings clearly to different audiences.
Course Information
What You Will Learn
- Understand the fundamental concepts and processes of data science.
- Understand how data is collected, structured, prepared and managed for analysis.
- Explore exploratory data analysis and descriptive statistics.
- Use data visualisation to identify patterns and communicate findings.
- Understand the role of programming and analytical tools in data science.
- Explore introductory predictive and machine learning concepts.
- Understand data quality, limitations and responsible use of data.
- Communicate data-driven findings clearly and effectively.
Course Modules
Introduction to Data Science
This module introduces data science and explains how data can be used to generate knowledge, insights and evidence for decision-making. Students will explore the data science process and the role of data scientists.
Data Collection and Preparation
This module explores different sources and types of data and introduces techniques for organising, cleaning and preparing datasets for analysis. Students will also examine common data quality problems.
Exploratory Data Analysis
This module introduces methods for exploring datasets and identifying patterns, relationships and unusual observations. Students will use descriptive statistics and other exploratory techniques to understand data.
Data Visualisation and Communication
This module introduces charts, graphs and other visualisation techniques used to communicate analytical findings. Students will learn how to select suitable visualisations and present information clearly.
Programming, Statistics and Analytical Tools
This module introduces the role of programming, statistics and analytical tools in data science. Students will explore basic computational approaches and statistical concepts used when analysing datasets.
Predictive Analytics and Responsible Data Science
This module introduces basic predictive analytics and machine learning concepts. Students will also explore data limitations, privacy, responsible analysis and ethical considerations in data science.
Learning Outcomes
On successful completion of this course, students should be able to:
- Explain the fundamental principles and processes of data science.
- Identify common sources and types of data.
- Describe methods used to prepare and analyse data.
- Apply basic statistical and exploratory data analysis concepts.
- Select and interpret appropriate data visualisations.
- Explain the role of programming, predictive analytics and machine learning in data science.
- Recognise important ethical, privacy and data quality considerations.
- Communicate data-driven findings clearly and responsibly.
Ready to Begin Your Data Science Journey?
Register with Senate Cyber School and develop the knowledge and skills needed to work with data and understand modern data-driven technologies.
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