How to start a career in Data Science

How to start a career in Data Science
How to start a career in Data Science

Data science is a fast-growing profession that is making an enormous impact on every corner of the economy. From business, medicine, and entertainment, to academia and politics, the vast number of technologies and services that have been transformed by data science illustrates the power of the discipline. And as more organisations seek to leverage this power, the demand for qualified data scientists is stronger than ever. 

Yet, high demand for data scientists does not mean that the profession is easy to enter. Despite being a highly-paid profession, data science positions are heavily short-staffed, which speaks to the complexities of entering the field. The journey to becoming a data scientist lacks a defined path; however, with the right roadmap, it’s a journey that anyone can complete successfully.

What are the prerequisites for success as a data scientist?

Becoming a data scientist has no formal prerequisites. While it’s advantageous to have a background in a discipline such as statistics, the skills necessary to become a data scientist may all be learned by practising data science tasks.

For instance, although individuals with a statistics degree may start their data science careers knowing how to interpret a linear regression model, other aspiring data scientists will learn how to do this as an early part of their training to solve real-world data science problems. 

That is to say, data science competencies are largely built upon the sort of practice-based learning in which the skills necessary to achieve a goal are developed as part of the journey towards that goal.

Step 1: Get started with data science informal learning 

Just by reading this article, you’ve already started this part of the process. Data science is a complex field, and the best way to prepare yourself for future formal studies in the field is to start learning about it yourself beforehand. By doing so, you can go into your formal studies with specific goals and an understanding of what to expect moving forward. 

Individuals can start learning about data science in various ways. These include the following: 

  • Notable blogs, such as Towards Data Science, can offer a fantastic overview of many different data science concepts. 
  • Current data science websites, such as Data Science Central, provide industry news and the latest trends. 
  • Introductions to basic data science (many of these materials are available for free online) make it easy to become familiar with the various coding languages that are used in the field. 

Gaining familiarity with data science blogs, articles and learning resources is the best way for aspiring data scientists to learn about the various paths available to them. 

During this initial exploratory process, beginner data scientists should pay attention to the subjects that they find most interesting and explore those subjects further. Data scientists network with each other by sharing their work, so any subjects that pique your interest will have ample amounts of further reading material available.

Exploring data science in this manner not only increases familiarity with the field and its subdisciplines but also provides an excellent opportunity to become familiar with the offline and online locations where data science conversations are happening, and to learn about leading figures in the field. By paying attention to these conversations, aspiring data scientists can learn the norms of the field and discover opportunities that they might otherwise not know to look for.


Step 2: The data science networking process

The lack of a defined road map for becoming a data scientist makes networking an essential part of entering the field. Networking allows aspiring data scientists to learn from people who’ve already ‘made it’, establish connections with fellow learners and begin establishing a profile in the data science community. These connections are important to discovering opportunities to apply for a data science position or take on other relevant roles. 

Both Twitter and LinkedIn are useful networking websites; however, many other sites also offer access to specialised data science conversations. These include the following: 

  • Websites such as r/datascience provide access to a data science peer group. 
  • Websites such as Stack Overflow offer anyone the opportunity to ask questions about how to perform specific data science tasks. Many of these questions are regularly answered by leading voices in the field. 
  • Specialised niche data science groups that meet up online and in person are accessible via niche networking websites such as Meetup.

Once you learn who these leading voices are, feel free to reach out to them—remember, they’re on networking sites because they want to interact with other people! Networking with data scientists is a particularly effective way to learn about the specific subfields they work in. For example, individuals interested in text mining might reach out to Julia Silge, while those focused on machine learning could try contacting Andrew Ng. For those interested in programming, the most influential figure in the R programming language is New Zealand statistician Hadley Wickham, whose coding tools are integral to many of the data science workflows used today.

For those interested in programming, the most influential figure in the R programming language is New Zealand statistician Hadley Wickham, whose coding tools are integral to many of the data science workflows used today.


Step 3: Start your own data science projects  

One of the best ways to establish yourself as a data scientist is to gain experience doing data science. Through time spent networking and exploring the field, you’ll come across a variety of simple data science projects that can serve as inspirations for your own interests. Many aspiring data scientists begin their data science journeys by experimenting with data visualisations, such as those found at r/DataIsBeautiful, and many of the data visualisations posted there are linked to tutorials that explain how to make them. 

Experimenting with the types of data science that are most interesting to you makes it possible to learn about what parts of the field you find most rewarding and may want to specialise in. The process of learning how to start an independent data science project is also valuable preparation for future professional work and results in a portfolio of work to show to potential employers. Data science relies very heavily on creative problem solving, and demonstrating your ability to complete a project independently is important to gaining a foothold in the field. 

Building a portfolio of independent projects is important to networking because it provides you with the opportunity to show your peers what you can do. For example, new attendees to data science Meetups and other networking events often share access to their data science blogs or GitHub repositories when they introduce themselves. Your beginner data science projects don’t have to be complex to provide a valuable demonstration of your skills, and there are many resources which suggest specific projects for beginners. 

As data scientists build their skills, it can be useful for them to participate in competitive projects. Projects that have a competitive element include the following: 

  • Kaggle isn’t so much a project as it's a website that offers an ongoing series of free data science competitions that aspiring and professional data scientists can use to develop their skills. Because these challenges are ranked, they’re excellent for individuals to monitor how their skills develop over time.
  • International Data Analysis Olympiad (IDAO) is a competition that’s open to everyone, including students at any level, as well as new data scientists. The competition helps data scientists sharpen their skills, so they can fill industry demand. 
  • Topcoder offers many different challenges for up-and-coming data scientists, and sometimes also offers prizes for the winners. Competitions focus on data science, design, competitive programming and much more. 

Any project that gives data scientists real-world experience can be extremely useful for their careers.


Step 4: Complete formal data science training

Formal study remains the most efficient way to ‘professionalise’ your data science skills. Formal data science courses offer structured learning experiences that are curated to provide training in the most valuable competencies in the field. This, in turn, makes independent work – and the learning that comes from it – more efficient. The benefits of formal study also include access to a network of academics and fellow learners and the ability to show employers a recognised credential as evidence of their skills. This is particularly essential to set yourself apart as a true data scientist.

The UNSW Sydney’s flexible online data science program is the ideal way to gain the skills necessary to become a professional data scientist. The University’s postgraduate courses include graduate diploma, graduate certificate and master’s-level degree programs, providing options for learners who want a complete training program and those who desire a shorter course to augment their existing learning. 

A data science education from the University includes training in the profession’s core competencies and most in-demand skills, including statistics and machine learning. Furthermore, the University’s courses include the ability to specialise in these and other skills, such as database systems, so that you can target your education to your particular career goals. Moreover, the University is internationally ranked as a top school for statistics, mathematics and computer science, and employers understand that its graduates have the skills they need to be successful in their fields. 

Data science job outlook and salary

As members of a profession that’s in strong demand, aspiring data scientists can expect a promising job outlook and generous salaries as they move through their careers. 

Here’s the job outlook for data scientists, as well as the salary that a junior data scientist, senior data scientist and data science manager can expect: 

  • Job outlook: On the Australian government’s official job outlook website,, information for the data scientist profession isn’t available. However, the website provides a projection for a similar role: analyst programmer. The outlook for an analyst programmer is very strong, which is the website’s highest growth rating.
  • Average salary for a junior data scientist: According to Indeed, the average salary for a junior data scientist is around $79,800.
  • Average salary for a senior data scientist: According to Indeed, the average salary for a senior data scientist is $119,000.
  • Average salary for a data scientist manager: According to Payscale, the average salary for a data scientist manager is $144,500. 

Take the next step in your data science career

The path to a data science career can lead to a rewarding destination for individuals interested in following the steps to succeed. If you want to take the next step towards becoming a professional data scientist, find out more about UNSW Sydney’s Master of Data Science by getting in touch with our enrolment team on 1300 974 990.