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?
There are no prerequisites to becoming a data scientist. While it is 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 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 beforehand. By doing so, you can go into your formal studies with specific goals and an understanding of what to expect moving forward.
There are a variety of ways to start learning about data science. Notable blogs, such as Towards Data Science, provide a fantastic overview of many different data science concepts, as well as industry news. Basic introductions to data science are available for free online, and 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 are the best ways for beginner data scientists to learn about the number of 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 it 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 networking process
The lack of a defined roadmap for becoming a data scientist makes networking an essential part of entering the field. Networking allows aspiring data scientists to learn from people who have already “made it”, establish connections with fellow learners, and begin establishing a profile within the data science community. These connections are important to discovering opportunities to onboard into a job or other relevant role.
Both Twitter and LinkedIn are useful networking websites; however, there are also many sites that offer access to specialised data science conversations. Websites such as r/datascience provide access to a data science peer group, while sites 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.
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.
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 up their skills, it can be useful to participate in projects that have some sort of competitive element to them. For example, the popular data science website Kaggle offers an ongoing series of free data science competitions that aspiring and professional data scientists alike can use to develop their skills. Because these challenges are ranked, they offer an excellent way for individuals to monitor how their skills develop over time.
Step 4: Complete formal 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 University of New South Wales’ 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 masters level degree programs, providing options for both learners that want a complete training program as well as those who desire a shorter course to augment their existing learning.
A data science education from UNSW includes training in the profession’s core competencies and most in-demand skills, including statistics and machine learning. Furthermore, UNSW’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, UNSW is internationally ranked as a top school for Statistics, Mathematics, and Computer Science, and employers understand that UNSW graduates have the skills they need to be successful in their field.
The path to a data science career can be achieved by anyone willing to put in the effort necessary to succeed. If you want to take the next step towards becoming a professional data scientist, find out more about UNSW’s Master of Data Science, get in touch with our Enrolment team on 1300 974 990.