Data science

Data science is a rapidly growing field. This is due to powerful digital technologies becoming essential to businesses and consumers. As a result, there’s increased demand for qualified professionals and data science becoming a lucrative and rewarding career pathway.

There are several reasons to consider upskilling or pursuing a career in data science. Although employment opportunities and global demand are two of the main benefits, a data science career gives you the chance to learn cutting-edge technology and work in a future-focused role. You’ll also be contributing to better decision-making and outcomes in organisations, which can be a fulfilling experience.

Technological advancements have made collecting massive volumes of data possible. This has created high demand for workers who have the skill sets to extract valuable insights from the data, with job growth for data scientists in Australia expected to increase by 12.9% in Australia in the next five years. With the Internet of Things and other data-intensive trends gaining ground, data science will continue to grow in significance.

Working in data science can be enormously rewarding because you’re helping humans and businesses make smarter decisions. Better data insights and improved data management can lead to streamlined operations and optimised customer service. It can result in an improved understanding of customers, and the development of better products and services. In critical fields like healthcare and medicine, it could lead to life-saving outcomes for patients.

What do data scientists do?

Data scientists take volumes of raw data and look for patterns, trends and insights to solve problems. They clean up raw data, put forward hypotheses, and apply different algorithms to find patterns.

Data scientists might also be the ones formulating the questions to be answered, by exploring the data set and working out how the data can be used. In most cases, data scientists model how to answer questions with any given data set. For example, they may look at the data on customers’ page views and shopping history for an e-commerce site. From this information, they can then develop prototype solutions on how to use raw data. These tools could turn data into personalised purchase recommendations or high-converting product suggestions for each customer.

Data scientists draw on a range of disciplines and approaches in their work. These include the scientific method, data engineering, maths, statistics, advanced computing and visualisation.

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Within an organisation, the data scientist could have a lot of overlap with other functions and roles. These could include business, programming statistics, and communication. It can touch on tasks in sales, consulting, and accounting. He/she could be responsible for work relating to predicting customer and other behaviour, classifying data, and making recommendations to consumers. Other responsibilities can include recognising patterns, detecting anomalies and fraud, automating processes, and creating sales forecasts.

In summary, the data scientist’s responsibilities can be broad. They work out how to ask questions and model how to answer questions with any given data set. Ultimately, the goal is to deliver value for businesses and organisations, often to help answer business questions in a measurable, data-rigorous way.

How to start learning data science

Data science is a rapidly growing field with increasing demand for skilled professionals. If you’re considering a career in data science, there are different course options available based on your experience. If you’re completely new to data science or need a refresher, a beginner’s course is a great way to gain all the basic knowledge required to work in the field.

A beginner’s course offers the essential building blocks to develop data science skills and introduces the most popular programming language, Python. It also covers foundational mathematics concepts such as linear algebra, calculus, probability, and statistics. On completion of a beginner’s course, you’ll have an understanding of how to write code and apply mathematic principles in your coding and modelling.

If you already have a basic understanding of data analysis techniques, an introductory data science course is ideal to progress your skills. This type of course is designed to provide you with practical coding experience, plus give you a well-rounded introduction to core concepts like basic machine learning. You’ll learn how to acquire and aggregate data and undertake feature engineering, and have the competency to create and validate data science models.

To continue expanding your data science knowledge, you may even consider undertaking a full-time data science bootcamp to consolidate your skills and further your career. This can boost your competitive advantage when applying for jobs. Bootcamps tend to be project-oriented and practical, helping you apply what you’ve learned to any future roles.

Completing the right courses will provide you with the right knowledge and credentials, but there are even more approaches you can take to further develop your data science skills. Apply these practical tips and you could find yourself mastering data science more quickly and effectively. One way to ensure your learning comes to life is to work on personal projects on the side. For example, if you’re interested in environmental protection, identify a question for analysis. Start collecting and analysing data in your free time. These personal projects can hone your data science skills. They can keep you motivated by giving you an opportunity to apply what you’re learning to a topic you’re genuinely passionate about. There’s always room to look for additional real-world opportunities to keep applying what you’re learning.

Another way to accelerate your learning is to interact and communicate with peers, mentors and instructors. When you start working as a data scientist, you’ll need to be able to speak about your work with others. Take any opportunity to discuss your projects with others, so you keep refining these communication skills and learn from others’ insights.

How to start a career in data science and be successful

Data science is a demanding field with job opportunities continuing to grow. While it may be daunting starting a new role, there are multiple ways you can accelerate your professional development and work towards a successful career.

One of the most important skills for data scientists is communication. Succeeding in a data science career requires strong interpersonal, communication and network-building skills. Whether you’re joining a small business or a large enterprise, it’s imperative to build strong relationships with other team members and departments. Don’t be shy about getting your name out there and establishing lines of communication with multiple colleagues. This is useful when you need to ask for data or when your colleagues need an analysis completed, as you’ll have relationships you can tap into right away. Building relationships with experienced colleagues in an organisation can also be beneficial from a mentoring perspective. Experienced colleagues will be able to provide feedback and guidance, which can support your progression throughout your data science career.

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Data science is powerful, but you can’t always solve every problem. Others might not understand what you do, so set realistic expectations from the start. When you start in a new organisation, it’s important not to assume the organisation works to the same principles and systems you might be used to. Take time to assess how the business works, so you can adapt your approach and align your role to the goals of the organisation. It’s also important to be open to continuing education and expanding your knowledge. In a field that’s constantly changing, doing extra courses can keep your knowledge current.

What is a data scientist salary in Australia?

Data science skill sets are in high demand in Australia. This is reflected in the most common salary of $130,000. Driving the demand for data science capabilities is technological development. Organisations now have the tools to collect large volumes of quantitative data, which they can now transform into relevant business insights. Meanwhile, the volume of data businesses are dealing with continues to rise. As much as 90% of data in the world was created in just the past several years.

Today, industries ranging from healthcare, agriculture, manufacturing hospitality, retail and banking prioritise data analytics. They see applying analytics to their data as an integral part of their operations, business development and overarching strategy. In job advertisements, statistical analysis, data mining, algorithm development and machine learning currently rank among highly sought-after skills.

What are the pillars of data science?

Exploring the pillars of data science can provide you with a better understanding of what the field is all about.

First, you have business domain and mathematics (including statistics and probability). Business domain in data science terms refers to your targeted subject area. For example, if you’re working on extracting data insights about customers for an e-commerce site, the e-commerce business would be the business domain. You need to understand the business domain to be able to design models and devise questions to extract insights. Mathematical techniques – including linear algebra, calculus, probability and statistics – are fundamental to data science modelling and popular specialisations, such as machine learning.

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The second pillar is computer science, which encompasses software/data architecture and engineering. Data scientists need to have a good understanding of relevant software, the system and infrastructure, and the data processes. This foundational pillar is what gives data scientists a basis for analysing and reporting on data. It allows them to model, test, and design the right tools for an organisation.

The third pillar is communication. To be a successful data scientist, you’ll need excellent written and verbal communication skills. This enables you to share what you do and what you’ve discovered. In turn, the rest of your organisation can then use your tools correctly and apply your insights in a practical and informed way.

Data scientists vs. data analysts vs. data engineers

Organisations may have their own way of defining a data scientist, data analyst and data engineer. These roles are often mistaken to be the same. In reality, these roles span a variety of different skill sets and responsibilities, however they all deal with data sets and play a key role in refining data strategies.

Generally, the term data scientist has the broadest application. Data scientists are involved in the whole problem-and-solution cycle. They find ways for organisations to use data, and they also help identify, collect and integrate sources of data. A data scientists role can include writing code, producing data-related tools for use throughout the business, and they’ll often communicate relevant data to the wider business where required. Data scientist is often used interchangeably with data analyst in job ads. However, the former usually requires more diverse coding skills.

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Data analysts tend to fit into a narrower function, usually tasked with extracting and communicating insights from data. This could be to measure outcomes, predict behaviours and trends, and support business decisions. Their role is usually less intensive when it comes to coding, compared with the work of the data scientist. Data analysts also tend to work with mainly numerical data, while data scientists work with more complex types of data.

Data engineers also have a narrower range of responsibilities compared to data scientists. They design, build and manage the data or information infrastructure of the organisation. They’ll test and maintain the data ecosystem to ensure the infrastructure is running as smoothly as possible. They’re also the creator and manager of the architecture used to collect, analyse and process data. In this way, their work is instrumental in enabling the work of the data scientist.

Beginner Python: A beginner’s guide

Python is one of the essential programming languages for data scientists (R and C++ are the other preferred languages). Python is also widely used beyond the data science community. For example, major brands like Google, Dropbox, Instagram, YouTube, and Spotify use Python to build their platforms. Python is considered a scalable language as it allows data scientists to approach problems in different ways. It’s also user-friendly, straightforward, and generally easy to learn. Python is flexible but able to be used for high-level programming. As a free, open-source programming language, anyone can add to its existing functions and incorporate data science codes as needed. It’s fast to write in (less code to get the job done) and has a huge community, from which you can draw support and knowledge.

Python has hundreds of what are known as libraries and frameworks. These are similar to templates, potentially saving you a large amount of manual programming time. There are several useful libraries on data analytics and machine learning to support your data science work. Examples include Pandas, NumPy, and SciPy, which offer data manipulation control, high-level math functions, and data optimisation and modification.

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If you’re planning to have a career in data science, a beginner’s course in Python is a great place to start. One of the best ways to get started is to choose a course covering the foundational mathematics principles, such as linear algebra, calculus, probability and statistics. A beginner’s course will provide you the basics in data science and math, which are essential for doing any type of data science work … the so-called data science alphabet you’ll need to get started. You can enrol and take the course even if you have absolutely no experience in data science.

In summary

Data science is a rapidly growing field with strong employment demand. If you’re excited at the prospect of working in the field, completing a data science program is an important first step to progress your career. With the right course, you’ll not only be equipped with foundational skills in data science and mathematics, you’ll also have a chance to apply these skills to projects.

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