What Is Data Science?
Data science is the large-scale interpretation of data for businesses to drive strategic decisions.
It’s an interdisciplinary field that combines maths, statistics, computer science and business to deal with digital and big data. Complex machine learning algorithms build predictive models to guide decision-making and strategic planning.
What careers involve data science?
Those working in data science can work in many sectors including entertainment, finance and government. They help solve many real-world problems. The career possibilities are endless, and constantly evolving alongside technology.
We discuss the most common jobs in data science below. The list is by no means extensive.
Data Analyst
Data Analysts in Australia earn $85,000 to $105,000.
Data analysts identify, collect and analyse data. Using software programs to create easy-to-understand graphs, charts and reports, they make data accessible. Businesses can streamline their processes using the information.
They identify trends and provide clients with valuable information that helps improve marketing, operational and business practices.
Some of their duties and responsibilities include:
- Data mining to extract information from data sets and identify correlations and patterns
- Performing statistical analysis of data
- Prepare easy-to-understand diagrams and graphs
- Predicting trends
- Prepare reports and present these to management or clients
- Identifying and recommending new ways to save money by streamlining business processes
- Machine Learning Engineer / Artificial Intelligence Engineers
The average salary for a machine learning/artificial intelligence engineer is $123,910 per year in Australia.
Machine learning engineers research, build and design self-running artificial intelligence (AI) systems. For example, they may help to develop the software for a customised newsfeed or a self-driving car.
Machine learning engineers need to understand data and build programs, so they need a strong background in software engineering and computer science.
Some of their duties and responsibilities include:
· Designing Machine Learning production systems
· Building data pipelines by gathering, cleaning, and validating datasets.
· Collaborating with data engineers to build data and model pipelines
· Providing support to engineers and product managers when implementing machine learning in their products.
Deep Learning Engineer
The average salary for a deep learning engineer is $102,617 per year in Australia.
Deep learning engineers are involved in the modelling phase in the development of artificial intelligence. Deep learning is a sub-field of the machine learning domain which builds and trains artificial neural
networks. Engineers in this field must be highly versatile. Using a mix of engineering and scientific skills they perform multiple AI project development and deployment tasks.
Some of their duties and responsibilities include:
- Using algorithms and techniques developed by researchers and applying them to real-world problems that help create solutions
- Defining data requirements for a project, labelling, inspecting and cleaning data
- Modelling tasks that include defining evaluation metrics, training deep learning models and searching hyperparameters for the models.
- Setting up a cloud environment to deploy the production model is part of a deep learning engineer's role
- Collaborating with data analysts, statisticians and software engineers
Data Engineer
Expected salary in Australia: $125,000 to $145,000.
A data engineer's primary role is to make data accessible to others. They build systems that collect, manage and convert raw data into unusable information. Data scientists and business analysts interpret this data helping organisations evaluate and interpret their performance.
Some of their duties and responsibilities include:
- Acquiring datasets that align with business needs
- Developing algorithms to transform data into useful, actionable information
- Building, testing, and maintaining database pipeline architectures
- Creating new data validation methods and data analysis tools
- Ensuring compliance with data governance and security policies
Data Scientist
Expected salary in Australia: $115,000 to $135,000.
Data scientists find and analyse raw data and then turn it into meaningful information. They use a mix of mathematics, computer science, predictive analysis and machine learning. The role requires creativity and problem-solving skills. Data scientists work in both IT and business sectors, as a result, they are well paid and in high demand.
Some of their duties and responsibilities include:
- Identifying data and generating large databases from various sources of structured and unstructured data
- Analysing large data sets and generating statistical information to identify trends and patterns for every stage of product delivery
- Creating algorithms that manage and organise information
- Writing applications that translate data and identify patterns
- Translating technical data into simple language and giving recommendations to stakeholders
- Collaborating with engineers and technologists
What Should I Know When Taking Data Science Courses?
The data science courses at UTS Open are designed to equip you with the skills, tools and techniques to achieve your next professional goal or career in data science.
We offer three types of data science courses. Free online courses: Give you a taste of a topic. They take up to three hours to complete, are self-paced and delivered entirely online. Short courses: Obtain in-depth knowledge for your professional development with an industry-aligned short course. Ranging from a day workshop to up to 10 weeks in length Microcredential data science courses: These courses run over four to ten weeks depending on the course. Each week consists of a mix of online lectures and home study. They are formally assessed and you’ll become a certified expert.
Become A Data Science Guru: Five Online Courses In Data Science
If you’re interested in exploring a career in data science, we’ve hand-picked some of our best data science courses, ranging from introductory courses to advanced courses for learners with all different levels of knowledge.
The first course we recommend is a free, self-paced online course, making it the perfect primer for data science. You don’t need any experience. Just bring your passion for learning about how statistical evidence influences real-world judgements.
You will leave the course with an understanding of how statisticians get to know and understand data as well as how they summarise data and communicate its relevance to a modern audience.
Do you want to know what data Facebook has on you, and how this data can be used? This is a free, self-paced online taster course. It guides you through some of the practical methods and tools you can use to investigate Facebook’s data on you.
During the course, you’ll cover what Facebook knows, how well Facebook knows you, data leaks, breaches, access and privacy. At the end of the course, there is a challenge to test the knowledge that you’ve gained.
This taster course will give you a taste of how the UTS’s Master of Data and Science Innovation works.
The third course we introduce is a four-week-long microcredential, with an average of 10 hrs study per week.
You’ll be introduced to data analysis via the use of various statistical tools used to make inferences and draw conclusions from data.
By the end, you will have the skills to apply statistical data analysis methodology to hypothesis testing. You will also be able to implement statistical analysis methodology in statistical software applications and communicate results and conclusions clearly.
This four-week microcredential requires an average of 12 hours per week of study time. It's aimed at professionals from a wide range of sectors and backgrounds involved in the development and facilitation of learning and education.
The course will explain what learning analytics is and how to gather data. By the end of the course you will know how data is analysed, the different types of data, and how can this data be used.
This course was co-designed by renowned academics and industry partners from the UTS Master of Data Science and Innovation program. You’ll dive into the latest data science approaches and best practices to transform your approach to developing data-driven insights and solutions within any organisation. The course, delivered online takes six weeks to complete and requires an average of 6 hours a week of study.
During the course, you’ll gain skills that help you better manage production-ready end-to-end solutions. You’ll also learn advanced machine learning concepts and techniques such as machine learning pipeline, gradient boosting and neural networks in depth.
If you’re a business analyst, data analyst or entrepreneur interested in learning about machine learning this course is for you.
Discover our full range of data science and analytics courses, here.
