Data Scientist vs Data Analyst: which career path is best?

Career tips Compensation and benefits Article
Do you have a keen interest for data? Are you thinking about which IT career path would best suit those interests? As data-driven decision-making continues to take priority across multiple industries, understanding the data roles that contribute to this environment could set up your future career trajectory. Demand for data talent today is high. In fact, according to the latest Robert Half Salary Guide, data analytics is not only one of the most in-demand technical skills today amongst Australian employers, but it also remains one of the areas where hiring managers are struggling to find talent. Among the most prominent (and in-demand) data roles in Australia today are the positions of Data Analyst and Data Scientist. These roles, while interconnected in their use of data, cater to distinctly different aspects of data processing and analysis. If you’re considering a career in either job function, this article will provide you with detailed insights into the requirements, expectations and outcomes that drive these two in-demand roles, to help you choose between becoming a Data Analyst vs a Data Scientist.
While both positions are concentrated around data, the roles of data analysts and data scientists, differ significantly in terms of their responsibilities, tools, and the scope of their work.  Here’s a look at the differences, along with the pros and cons of each IT role:   Data Analyst responsibilities: Clean and process data to ensure it is accurate and usable.Generate reports and dashboards to visualise data for easier consumption and decision-making.Focus primarily on analysing past data to find actionable insights that influence current business decisions.Work closely with specific departments to provide the data required to support day-to-day business operations.   Data Scientist responsibilities: Use statistical tools and machine learning algorithms to create predictive models that forecast future outcomes.Employ sophisticated data mining techniques to identify patterns and trends beyond simple analysis.Not only predict future trends, but also provide recommendations on how businesses can take advantage of these predictions.Work with advanced analytics technologies including AI and machine learning to solve complex business problems.   Data Analyst pros: Easier role to enter with foundational skills in statistical analysis and data manipulation.Directly influence business decisions through clear data presentation and insights.Skills are transferable across many industries, allowing for diverse career opportunities.   Data Analyst cons: Often restricted to descriptive analytics, which may feel repetitive or less challenging over time.Generally, data analysts earn less than data scientists due to the less technical nature of the role.   Data Scientist pros: Due to the high demand and specialised skill set required, data scientists typically command higher salaries.Ability to influence broader business strategies through predictive insights and advanced data-driven products.Regular interaction with the latest advancements in machine learning, AI, and big data technologies.   Data Scientist cons: Often requires advanced degrees in statistics, computer science, or related fields, which can be a barrier to easy entry.The role can involve a higher level of complexity and stress, dealing with ambiguous data sets and evolving algorithms.Continuous learning to keep up with the latest technologies and methods in a fast-evolving field.
“Getting started as a Data Analyst is generally considered easier than beginning a career as a Data Scientist in Australia,” explained specialised tech recruiter Danielle Leonard. “This is primarily due to the differences in the complexity of the skills and educational requirements associated with each role.” Given Danielle’s experiencing placing candidates and in data roles, she says “if you’re looking to quickly start a career in the data field with a solid foundation in basic data handling and analysis skills, becoming a Data Analyst is typically more accessible. It requires fewer specialised skills and a less rigorous educational background compared to the Data Scientist role, which is more intensive both in terms of the depth of knowledge required and the breadth of technical expertise.”
Technical skills required to be a Data Analyst Proficiency in SQL is crucial as it allows analysts to retrieve and manipulate data from relational databases efficiently.  Advanced Excel skills are necessary for handling data sorting, filtering, pivot tables, and using functions to perform calculations. Familiarity with tools like Tableau, PowerBI, or Qlik is important for creating visual representations of data that are easy to understand and actionable.  Understanding basic statistical measures (mean, median, mode, standard deviation) and tests is vital for analysing data sets and summarising data findings. Skills in reporting and data presentation to communicate findings clearly and effectively to stakeholders.  Related: Are we seeing the last days of SSRS and the rise of Power BI? Technical skills required to be a Data Scientist Expertise in programming languages such as Python or R is essential. These languages support data manipulation, statistical analysis, and machine learning.  Knowledge of machine learning techniques (both supervised and unsupervised learning) is critical for predicting future trends and behaviours. strong background in statistics and the ability to apply these concepts to large data sets is required to derive meaningful insights.  Experience with big data platforms like Apache Hadoop or Spark is beneficial, especially when dealing with large data sets. Skills in cleaning and preparing data for analysis are crucial as real-world data can be messy and incomplete. 
Data Analyst educational level: Most entry-level Data Analyst positions require at least a bachelor's degree. This is often considered sufficient if the degree is in a relevant field.  For fields of study, consider statistics, economics, and mathematics. These provide a solid foundation in quantitative analysis, which is essential for data analysis. Analyst roles in specific sectors may benefit from a degree related to that sector to better understand the context and specifics of the data.  IT and computer degrees can provide valuable technical skills such as database management, which is crucial for data handling and reporting. Certifications in specific tools or technologies (like Tableau for data visualisation, SQL for database management, or a specific analytics certification from a reputable organisation) can enhance your resume.  Practical, intensive training sessions focused on data analysis tools and techniques can be extremely beneficial, especially for those transitioning from non-technical fields.   Data Scientist educational level: While not always mandatory, advanced degrees are commonly preferred, especially for competitive or advanced roles. These degrees indicate a higher proficiency in complex mathematical theories and computational methods needed in data science.  Computer and data science are core disciplines for data scientists, providing deep insights into algorithms, machine learning, programming, and system design. Applied mathematics and engineering offer the necessary background in modelling, statistics, and analytical thinking critical for the role.  Machine learning and data bootcamps can provide practical, direct experience with real-world data sets and are often designed to cover the latest technologies and methodologies in data science.
More often than not, data scientists tend to command higher salaries than data analysts in Australia. This difference in compensation reflects the demand for skilled talent, the more advanced technical skills, educational requirements, and responsibilities associated with Data Scientist roles. Moreover, Australia's data analytics market is set to grow at a rate of 20% towards 2025. The supply of data scientists in Australia is increasing, but it's not keeping up with the rapid growth in demand. The average salary for data scientists in Australia ranges from around $115,000 to $155,000 with more experienced professionals or those in specialised sectors earning significantly more. The demand for skills in machine learning, big data management, and advanced predictive analytics - which are essential in roles dealing with large datasets or in industries like technology, finance, and healthcare - contribute to the higher pay scale. Data analysts earn less than data scientists. According to the latest Robert Half Salary Guide, the typical salary range for a Data Analyst might start from around $114,000 and can go up to about $143,000 in Australia. The variance depends on factors such as industry, location, and level of expertise. Salaries for data analysts are often influenced by the industry they work in, the complexity of the data they handle, and their proficiency in specific data tools and technologies. Analysts with strong skills in data visualisation and software like Tableau or advanced SQL skills may command higher salaries. “Both roles can see significant salary differences based on the industry,” said Mark Bennett, Director at Robert Half and specialised tech recruiter. “For example, data scientists working in tech startups or finance may earn more than those in government or educational institutions. Similarly, data analysts in high-tech companies or financial services might earn more than those in less data-driven industries.” Higher levels of experience and education can lead to higher salaries in both roles. Data scientists with a Ph.D. or extensive project experience in advanced machine learning or AI can command top-tier salaries. “Also worth considering is specialising in high-demand areas such as artificial intelligence, deep learning, or big data technologies can increase earning potential,” added Mark.
The work-life balance in roles like a Data Analyst and Data Scientist can depend on multiple factors including the specific industry, company culture, and individual project demands. However, there are general trends in each role that can influence the typical work-life balance experienced by professionals in these fields. “Data analysts often experience more regular hours and a predictable workload,” says Mark. “Their tasks are usually defined and recurring, such as weekly reporting, monthly metrics reviews, and routine data validation.” Since Data Analyst roles are less likely to be involved in high-stakes decision-making or cutting-edge developments, there is generally less pressure and fewer instances of tight deadlines compared to Data Scientist roles. “The balance can still depend heavily on the company and corporate culture. For example, analysts in fast-paced industries such as finance or consulting might experience more demanding schedules,” remarked Danielle. Data scientists may face more irregular hours due to the nature of their work, which involves complex problem-solving, project deadlines, and ongoing research that can extend beyond typical business hours. “The need to meet project milestones or deliver on research outcomes can lead to periods of high intensity, especially in roles that are closely tied to product development or innovation,” said Danielle. “The high impact of their work, particularly in sectors like technology and finance, can also contribute to higher stress levels and a more demanding work environment.” Industries with higher volatility and faster pace (such as tech startups, financial services, and healthcare) often require more time-sensitive analyses and can demand more from both data analysts and data scientists. “Larger corporations might offer more structured hours and resources for balancing work demands, whereas startups may require more flexibility and longer hours due to resource constraints,” commented Mark.
Soft skills are critically important for both data analysts and data scientists. These skills enhance their ability to communicate findings, collaborate with team members, and influence decision-making processes. The most crucial soft skill for data analysts is effective communication, which allows them to translate complex data insights into understandable and actionable information for stakeholders who may not have a technical background. Data analysts will also need to stay on top of their diligence, problem-solving abilities, and time management. By comparison, data scientists must be able to explain their complex work to non-experts, including how their findings affect business strategies or operational changes. Evaluating multiple solutions to a problem and predicting potential outcomes is a daily task for data scientists, requiring strong analytical and critical thinking skills. Data scientists often work in cross-functional teams, collaborating with engineers, business leaders, and other stakeholders. Being able to work effectively in team settings is essential. Both roles involve regular interaction with stakeholders to gather requirements, report findings, and suggest actions. Strong people skills can enhance these interactions and increase the impact of their contributions to the organisation. Related: Hiring a Business Analyst vs. Data Scientist: what you need to know
There are (in fact) multiple specialisations within both roles that Australian candidates can consider taking their career down interesting pathways, including:    Marketing Analyst:  Focuses on analysing consumer behaviour, campaign performance, and market trends to optimise marketing strategies.  Tools and techniques often include Google Analytics, CRM software, and social media analytics platforms.    Financial Analyst:  Specialises in financial data like revenue, expenditures, and market trends to aid in financial planning, budgeting, and forecasting.  Frequently uses tools like Excel, financial modelling software, and proprietary financial databases.    Operations Analyst:  Works on improving operational efficiencies by analysing workflows, supply chain management, and production processes.  Often requires knowledge of ERP systems, workflow management tools, and process mapping software.    HR Analyst:  Analyses employee data to help with hiring decisions, performance evaluations, and policy planning.  Uses HR information systems (HRIS), employee performance data, and survey results.    Machine Learning Engineer:  Focuses on designing and implementing machine learning models and algorithms to solve complex predictive problems.  Typically requires deep knowledge of Python, R, TensorFlow, and other ML frameworks.    AI Research Scientist:  Engages in advanced research to develop new methods and technologies in artificial intelligence.  Requires expertise in deep learning, neural networks, cognitive computing technologies.    Natural Language Processing (NLP) Scientist:  Specialises in programming computers to process and analyse copious amounts of natural language data.  Utilises tools and libraries like NLTK, SpaCy, or BERT for tasks such as sentiment analysis, text classification, and language generation.    Data Engineer:  Focuses on the infrastructure and architecture for data generation, collection, and analysis systems.  Often skilled in big data technologies such as Hadoop, Spark, and extensive database management knowledge.    Quantitative Analyst (Quant):  Typically works in finance, using statistical and mathematical models to inform financial and risk management decisions.  Proficient in statistical analysis tools and programming languages like Python, along with knowledge of financial markets. 
Both paths offer opportunities for advancement into more senior roles, specialisations, or even transitions into other areas.  Understanding the potential trajectories can help professionals make informed decisions about their career development.   Career path for data analysts Entry-level Data Analyst: Starts with handling basic data gathering, cleaning, and preliminary analysis. Typically reports findings using visualisation tools and supports senior analysts.   Senior Data Analyst: Takes on more complex analysis and often leads projects or small teams. Develops more sophisticated reporting frameworks and dashboards.   Data Analysis Manager: Oversees a team of analysts and is responsible for major projects and strategic decisions regarding data usage. Focuses on improving team efficiency and data processes.   Career path for data scientists Junior Data Scientist: Works under the supervision of more experienced scientists, focusing on data cleaning, model development, and simple predictive analytics. Gains exposure to a variety of statistical models and machine learning algorithms.   Senior Data Scientist: Leads projects, designs complex models, and often works cross-functionally with different departments to implement solutions. May begin to focus on specific areas like machine learning, AI, or deep learning.   Data Science Manager or Lead: Manages teams of data scientists and coordinates large-scale projects. Set the strategic direction for the company’s data-related policies and procedures.   Chief Data Officer (CDO) or Director of Analytics: Executive role that oversees the data management strategy for the entire organisation. Ensures that data policies and practices align with organisational goals and regulatory requirements.   Other factors influencing career progression Both roles require continual learning and adaptation to new technologies and methodologies.  “Keeping skills current is crucial,” affirmed Mark. “Being aware of how industries are leveraging data can provide insights into potential career opportunities or necessary skill adjustments. Engaging with professional communities and demonstrating thought leadership can also open opportunities for advancement.” Both career paths offer robust opportunities for growth and specialisation. data analysts might find their roles evolving into areas that require more direct business interaction and decision-making, while data scientists might take on roles that are more strategic or research-focused, involving innovative technologies.  Ultimately, the choice between these paths should align with personal career aspirations and the type of work that one finds most fulfilling.
Bootcamps are designed to provide intensive, firsthand training in specific skill areas, making them a popular choice for entering the data field without the time and financial commitment of a traditional degree.  Data Analyst bootcamps typically concentrate on teaching practical skills like SQL, Excel, Python, and data visualisation tools such as Tableau or PowerBI. They also cover basic statistics and analytical techniques necessary for interpreting and reporting data effectively. Graduates are prepared to take on entry-level data analyst roles across various industries. They come out with a portfolio of projects demonstrating their ability to extract, analyse, and visualise data. Bootcamps for aspiring data scientists are more intensive and cover a broader range of topics, including advanced programming in Python or R, machine learning, advanced statistical analysis, and sometimes even elements of big data technologies. These programs can be more demanding, often requiring longer commitments (up to six months) and a stronger foundational knowledge in math and coding than what might be expected for data analysts. Participants can expect to learn how to create sophisticated predictive models, oversee large data sets, and apply machine learning algorithms. Like their analyst counterparts, they finish with a portfolio that highlights their ability to tackle complex data science problems. Bootcamps represent a focused and practical pathway into data careers, appealing especially to those seeking a career transition or rapid upskilling. They can effectively prepare you for entry and mid-level roles in data analysis and data science, provided you balance the skills learned with continuous learning and networking in the field.
Choosing between a career as a Data Scientist vs Data Analyst should depend on your interests, strengths, and career aspirations.  Here are a couple of observations to help you decide which role might suit you better based on personal and professional factors:   How to tell if you should become a Data Analyst: You enjoy working with numbers, charts, and graphs. You have a keen eye for detail and a strong ability to spot trends and patterns in data. You prefer tasks that have a clear start and end and enjoy seeing the practical application of your work. You're good at explaining complex information in a straightforward, accessible way. You are proficient in or willing to learn SQL, basic programming (perhaps in Python or R), and data visualisation tools like Tableau or Power BI. You have or are developing a good understanding of statistics but may not be inclined to delve into more complex mathematical models. You have strong communication skills, enabling you to bridge the gap between data and decision-making. You prefer a role with more regular hours and possibly a better work-life balance. You thrive in roles where you can support decisions rather than drive the research or exploration that leads to new business directions.   How to tell if you should become a Data Scientist You have a strong interest in machine learning and artificial intelligence. You're curious about researching new techniques in the field of data and enjoy continuous learning. You're interested in predictive modelling and the technical challenges of working with big data. You like to dig deeper into the "why" and "how" questions that data can answer. You have a solid foundation in programming, especially in Python or R, and are comfortable with libraries and frameworks used in machine learning. You possess or are keen to develop advanced statistical knowledge and skills in building and tuning predictive models. You are good at problem-solving and innovation, often required to develop new algorithms or data-driven products. You are comfortable with a dynamic work schedule that might require intense periods of research and development. You're interested in roles that offer more autonomy and are project-driven with potentially high impacts on your organisation.
Firstly, consider how much time and resources you are willing to invest in education and training. Data science often requires more advanced study (including potentially a master's or Ph.D.), whereas data analysis can often be entered with a bachelor’s degree and specific certifications or bootcamps. Secondly, reflect on where you see yourself in the future. If you aspire to be at the forefront of technological innovation, data science might be the better fit. If you prefer a role focused on business insights and direct impact on decisions, data analysis might be more suitable. And lastly, consider which type of projects would feel more rewarding to you. Do you get more satisfaction from solving immediate business problems or from developing complex models that may take months to complete but can transform business strategies? Ultimately, both careers offer exciting opportunities in the burgeoning field of data in Australia. Your choice should align with what you find most interesting and what complements your natural strengths and skills.