Data analyst vs data scientist (Responsibilities and skills)
Data is a field with many career opportunities, including data analytics and data science. While there are similarities between a data analyst and a data scientist, the roles are distinct and require unique skills. Understanding the differences between the two career paths can help you choose the one that is best for you. In this article, we explain the differences between a data analyst vs data scientist and describe the educational requirements, key job responsibilities, average salaries and the skills typically necessary to perform these roles.
Please note that none of the companies, institutions or organisations mentioned in this article are affiliated with Indeed.
Data analyst vs data scientist
Here are the different definitions of a data analyst vs a data scientist:
What is a data analyst?
A data analyst gathers and interprets data to identify trends, solve problems and improve organisational decision-making. To visually represent data and present their findings, data analysts create reports with charts and graphs. Analysing data also includes performing statistical analyses to enable companies to make informed decisions about specific business strategy questions or to solve critical operational problems. Often working as part of a multidisciplinary team, data analysts use their skills to help an organisation achieve its goals and objectives.
What is a data scientist?
A data scientist is an individual with exceptional technological skills who designs and uses advanced processes, including data modelling, to analyse datasets, glean insights and make data-backed decisions. They typically focus their efforts on developing methods, data frameworks and automation systems to manage data. To perform their duties, data scientists develop algorithms and predictive models that often involve advanced programming or machine learning. They work with extensive datasets and spend considerable time collecting, cleansing and structuring data to identify trends and predict business outcomes. When performing their duties, data scientists often work alongside engineers and senior management.
Differences between a data analyst and a data scientist
While the two roles are similar, there are also key differences between a data analyst and a data scientist. To choose the career path that best matches your professional aspirations, consider the following areas and how these two roles compare:
The educational requirements are similar, and a bachelor's degree is typically the minimum qualification for both occupations. Common undergraduate degrees include mathematics, computer science, statistics, and engineering. A data analyst might take more classes with a business focus, whereas a data scientist is more likely to complete coursework in computer science and technology. Given the emphasis on advanced coding, specialised statistical analyses, data mining techniques and data architecture, data scientist positions usually require a master's or doctoral degree. This can be helpful for a data analyst but not required for many roles.
Each of these individuals may complete different daily tasks. Here is more information about the responsibilities of each role:
Data analyst duties
The main duty of a data analyst is to help a company solve problems by analysing data and translating the information into understandable metrics. Typical tasks include:
collecting data from primary and secondary sources, including survey data or web analytics
conducting consumer or other market-related data research
creating database structures to store and compare different datasets
identifying and correcting data errors using data cleansing techniques
performing quantitative analyses to assist organisational decision-making
communicating with company leaders or key collaborators to address their informational needs
creating and writing Structured Query Language (SQL) queries to manage and retrieve information from databases
interpreting data to identify trends and patterns that provide valuable business insights
contextualising data by finding connections between the interpreted data and the greater context of the business
communicating dataset findings to company management, colleagues, collaborators or clients
supporting the development of financial reports, key performance indicators or other crucial documents comprising specific data
Data scientist duties
While a data analyst concentrates on analysing data to make better-informed business decisions, the primary duty of a data scientist is to use more advanced data techniques to predict the outcome of those decisions or investigate more abstract questions. Typical responsibilities of a data scientist include:
designing and implementing dataset models, machine learning algorithms or other valuable data-related experiments
collecting, storing and cleansing large quantities of data
developing predictive models to increase company profits or enhance customer experiences
creating processes to monitor data accuracy and the performance of data models
mining and analysing data to improve business strategies or product development
coordinating with other staff or teams to implement dataset models
writing and coding programmes that automate the data collection process
utilising visualisation tools and developing data dashboards
communicating dataset results and pertinent information with senior management, key interested parties or team members
Common qualities that data analysts and data scientists possess include basic statistical and qualitative analytical abilities and effective communication and problem-solving skills. Both disciplines typically require knowledge of programming languages, such as Python, R and SQL, but there are also some unique differences between the roles.
A data analyst is more likely to have experience and aptitude using data analysis software, database management systems and business intelligence tools. A data scientist usually has machine learning, software development, algorithm development and big data software skills and experience.
Data analysts often begin their careers as junior analysts. Once they gain experience collecting and cleansing data, conducting data analyses and sharing the information with management, they can move into more senior roles with greater responsibility and become analytical managers. These roles typically provide opportunities to assume ownership of data processes and lead teams of data analysts. Some data analysts specialise in industries such as healthcare or finance.
Data scientists also tend to start in junior roles. They often require support and assistance from data scientists with experience in writing code, building data models and conducting statistical analyses. After five to seven years of managing and manipulating large quantities of data, including creating predictive statistical models, data scientists can typically perform their duties independently. Some data scientists choose to move into supervisory or industry-specific roles. For example, they could become a machine learning engineer.
Choosing between a career in data analytics and data science
Understanding the key differences between data analysis and data science enables you to choose the career path that is best for you and pursue a role that aligns with your personal and career goals. Key points to consider when choosing between a data analyst and data scientist role include:
Assessing your interests and priorities can help you determine the appropriate career path. A data analyst position commonly requires researching and collecting data from different sources and entering the information into spreadsheets or databases. If your interests lie in research, statistics and analysing trends in data, working as a data analyst may be a good choice for you. If computer science, technology and advanced programming interest you and you are considering a master's or doctoral degree, a career as a data scientist might be a more suitable option.
Choosing a career path that aligns with your natural aptitude and abilities is a primary consideration. It makes it easier to learn about the field and prepares you for your career. Taking the time to assess your skills and career motivation can help you determine which profession to pursue.
These two roles often have different salaries because of the varying educational requirements and responsibilities between the two disciplines. When evaluating your career choices, review the typical salary for each role in your area. Consider both your financial and professional goals to help you make a decision.
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