Data Analyst to Data Scientist: Tips for Transitioning Careers

Can a business analyst become a data scientist?
Can a business analyst become a data scientist?

Australian professionals are changing careers more often than ever before. Roughly three in five Australian employees are planning to either change jobs or careers in 2022, according to LinkedIn data reported by Business Insider Australia. This move has been largely prompted by a phenomenon called ‘The Great Resignation’, where, due to a tight labour market, companies are increasingly desperate for talent and hence are able to offer more competitive pay and conditions, and workers are taking up their offers in droves. 

The rising popularity of career transitions may be due to their effectiveness as a career advancement tool. Following the pandemic, the percentage wage growth for employees is projected to jump from an average of 2.5% to 3.4%, according to a 2021 Willis Towers Watson survey. However, employees who choose to move between companies or to a more lucrative profession, such as data science, can yield significantly higher increases in pay. Moreover, workers can use their history of successful career transitions to demonstrate their competitiveness in the labour market. 

A career transition may also be motivated by technology’s impact on industry. Introducing new technology into a workplace can disrupt traditional job roles and provide opportunities for employees who possess the skills necessary to take advantage of the resulting changes. A more notable technology-driven change in recent years has been the growing role of data science as a central source of value creation in the economy. 

For data analysts, data science stands out as a profession that’s complementary to their existing role. Data analysts and data scientists use different methodologies to achieve their goals, but both focus on leveraging empirical data to create value for their businesses. This synergy makes data science an ideal field for data analysts who want to transition to a career that’ll allow them to use the skills they’ve learned in their current role.

Why transition from data analyst to data scientist?

Since being dubbed “the sexiest job of the 21st century” in 2012, data science has become a ubiquitous presence within the business world, with ever-more firms leveraging its insights to create value. By leveraging available data to inform a company’s operations, the work of a data scientist can complement—and in some cases supersede—those of a business analyst. At some small and medium-sized firms (SMEs), the job of conducting data analytics may be merged with business analyst duties, illustrating the compatibility of the two professions.

The growing importance of data science provides an incentive for business analysts seeking to remain on the cutting edge of their industry.

A data scientist’s unique capabilities—such as the ability to model and structure data from millions of individual data points—make them a unique source of value to their employers. Certain data science techniques, such as the use of machine learning to model future customer demand, provide a level of accuracy and efficiency that cannot be matched by traditional business analysis methodologies.

A business analysts’ expertise is a competitive advantage in the data science field

A worker’s ability to transition careers is a sign of their competitiveness within the labour force. For business analysts looking to further their career through a transition into data science, their existing career experience provides them with a major competitive advantage within their new field. The business analyst skill set includes competencies that employers value but which many data scientists currently lack, such as industry-specific business knowledge and the ability to effectively communicate their insights to key decision makers. 

Data analyst transferable skills

  • Data analysts have numerous skills that are easily transferable to a role as a data scientist, including the following: 
  • Ability to interpret data and translate it into business solutions 
  • Understanding of how to design data collection and analysis methods and processes 
  • Knowledge of how to identify patterns and trends in data 
  • Understanding of data analysis software
  • Ability to develop, implement and maintain data infrastructure 
  • Understanding of how to create data visualisations and reports

Many data scientists work in a manner that is relatively industry-agnostic. While this means that data scientists can use their skills in a variety of different business environments, it also means they may not understand the “on-the-ground” needs of their employer. This may limit the data scientist’s ability to turn their work into an actionable source of value for their employer, an issue which contributes to why some companies are unable to generate value from their data science programs. 

A business analyst’s industry-specific expertise provides them with the ability to understand any operational or policy obstacles which may impact the usefulness of their insights. Business analysts-turned-data scientists stand out among data scientists due to their ability to recognise the practical needs of employers who operate within the industry that they are experienced in. 

The communication skills that business analysts must hone throughout their careers are another major competitive advantage in the data science world. Soft skills are extremely important for data scientists, who must be able to clearly communicate their insights to company decision-makers who do not possess significant data literacy. As many data scientists currently lack these skills, a business analyst that can demonstrate their soft skills will be well-positioned to succeed as a data scientist.


The skills business analysts need to learn to become data scientists

Although business analysts and data scientists both focus on maximising the value that a business can get from its available resources, the methodology they use to do so differs significantly. A move into data science requires business analysts to learn the data modelling and analytics skills that data scientists rely on. The following are the key skills that business analysts must learn in order to make the transition successfully. 


Modern data science relies heavily on the use of coding tools to model and analyse data, which means that any individual transitioning into data science needs to learn how to code. The most popular coding language used in data science is Python, and R is also used heavily. R is a data science-focused language, but Python is a general-purpose language, which means that Python learners need to ensure that they study it in a manner that emphasises its data science applications. 

Math & Statistics

Data scientists rely upon the use of mathematical modelling to create insights from data; in fact, a key part of the data science learning process involves the development of an intuitive understanding of what statistical tools to use to draw insights from a given dataset. The data science learning process should include an overview of statistical techniques, such as linear regression, as well as a basic study of algorithms, which are vital to predictive analytics. 

Data organization and preparation

It’s a well-known rule of thumb that data scientists spend approximately 80% of their time on data preparation—basic tasks which include finding, manipulating, and organising data, as well as exploratory data analysis (EDA). EDA includes the use of skills such as data visualisation, which data scientists use to understand their datasets better. Because so much of a data scientists job is devoted to this type of work, it is necessary to learn how to do it efficiently by incorporating it into any training regimen. 


One of the most effective ways to demonstrate (and build) your capacity to work as a data scientist is to complete projects to show to potential employers. This may be accomplished through a number of means, such as by asking an employer to do data science work as a form of professional development, or by starting an independent data science project in your spare time. Additionally, many formal data science courses include project-based learning modules that can be included in professional data science portfolios upon their completion.

Tips for transitioning from data analyst to data scientist

As with any career change, transitioning from one role to another can be challenging. However, to make the switch, aspiring data scientists should endeavour to do the following: 

Invest in your education

One of the most powerful ways to change careers is to invest in your education. As regards data science, you have a few different qualifications available to you, including UNSW Sydney’s Master of Data Science.

Apply for a skilled volunteer position

Fortunately, if you’re a data analyst, you’ll already have many transferable skills that you’ll need as a data scientist. However, what you may not have is formal experience in the role. A great way to obtain this is to apply for a skilled volunteering position in this field. This involves volunteering for a nonprofit organisation that may be aligned with causes you care about. 

There are many websites that offer skilled volunteering positions, for example, SEEK Volunteer and Pro Bono Australia.

Complete an internship

Another option that enables you to get the formal experience as a data scientist that you’ll need is an internship. Internships may be paid or unpaid, and usually involve your completing entry-level tasks to obtain experience. After completing an internship, your host organisation may offer you a permanent role. 

Secondments or job shadowing

If you work in a large organisation that already employs data scientists, a great way to make the transition is to job shadow or undertake a secondment. 

Job shadowing involves your shadowing an experienced data scientist to begin to understand what tasks the data scientist undertakes and how to undertake them. 

Secondments involve your actually making the switch to a role as a data scientist and learning on the job. After your secondment, which usually lasts for at least a few months, you may be offered a permanent position.

Power your career transition through a formal data science education

The most efficient way to gain the qualifications necessary to transition into a data science career is through a formal education program, such as those offered by the University of New South Wales. Formal programs eliminate the need for students to spend time developing their own curricula, ensure that their studies include all job-necessary skills, and provide them with formal recognition they can use to show they have acquired a job-ready skill set. 

UNSW’s online Master of Data Science program offers working professionals the ability to learn data science without disrupting their careers. The UNSW curriculum includes a strong focus on the use of data science for business purposes, which means that students with a business analyst background will learn career-relevant skills throughout their study. Furthermore, the program’s final capstone project ensures that students come out of the program with a project they can use to demonstrate their ability to successfully solve real-world data science problems. 

As an in-demand profession, data science is a career that offers high pay and strong employability; UNSW’s data science programs provide the education necessary to make a successful transition into the field. To learn more about UNSW’s Master of Data Science, get in touch with our Enrolment team on 1300 974 990.