When looking into a new career in data, comparing the role of a data analyst vs a data scientist can be hugely helpful.
This guide from career experts gives you everything you need for this comparison, so you can make the best decision possible for your future career.
What does a data analyst do?
Typically, a data analyst is responsible for gathering the data necessary to identify trends. These trends are then utilised to help managers and leaders make decisions for their business. The work is focused on performing statistical analyses that help to answer questions and solve problems.
To do the work, a data analyst will use tools like R, Python, or SQL programming languages to make queries to relational databases. It may also be the responsibility of a data analyst to clean data, or to put it in a usable format. This involves discarding irrelevant data and useless information, and figuring out what to do in cases when data is missing.
In most cases, data analysts work as part of an interdisciplinary team. These skilled individuals help determine the organisationās goals and to manage the process of mining, cleaning, and analysing the data collected. To develop and convey their findings to others, data analysts must use:
- Programming languages like R and SAS
- Visualisation tools like Power BI and Tableau
- Excellent verbal and communication skills
What does a data scientist do?
Data scientists and data analysts share many of the same responsibilities, though data scientists more often deal with the unknown by using more advanced data techniques and tools to make predictions about the future. They are also more typically involved in designing data modelling processes for handling structured and unstructured data, automating their own machine learning algorithms, and creating algorithms and predictive models.
Generally considered a āmore advanced versionā of a data analyst, a data scientist may be more focused on the development of tools and methods that can extract information their organisation requires to solve complex problems.
Similarities between data analysts and data scientists
Data scientist and data analyst roles are similar because:
- Both career paths typically require at least a Bachelorās degree in a quantitative field, such as computer science, mathematics, or statistics
- Both require an affinity for mathematics and statistics
- Both roles require the individual to focus on analysing data to gain actionable insights for their organisation
Differences between data analysts and data scientists
The roles are different because:
- Data analysts may spend more time making sense of existing data, while data scientists work on new ways of capturing and analysing data to be used by analysts
- Data analysts work on answering specific questions about the business, while a data scientist may work at macro level to develop new ways of asking and answering important questions
- The roles are sometimes defined by the tools they use; these help data analysts to be proficient with relational database software, business intelligence programs, and statistical software. Meanwhile, data scientists use Python, Java, and machine learning to manipulate and analyse data
Data analyst vs data scientist vs data engineer
Another previously unconsidered career path in this piece, which is often confused with data analysts and data scientists, is that of the data engineer. Again, the position of data engineer is usually considered as more āadvancedā than a data analyst. Their work revolves around building the systems that will collect, manage, and convert the raw data into information.
Data engineers differ from data analysts and data scientists because they develop, test, and maintain the data pipelines and architectures that analysts and scientists use to carry out their work. What they do allows analysts and scientists to provide more accurate metrics.
Education and work experience
As previously mentioned, itās recommended that both data analysts and data scientists have at least a Bachelorās degree in a quantitative subject like statistics, mathematics, finance, or computer science. Itās also possible to find some candidates with degrees in subjects like business or psychology, as long as the course offers a substantial focus on statistics or analysis.
Many data scientists, as well as very experienced data analysts, may also have a Masterās degree, or even a PhD. These would normally be in subjects such as data science, statistics, mathematics, IT, big data, data analytics, or business analytics.
Options for non-graduates
Those without degrees in related fields can also choose to become a data analyst or a data scientist. The simplest method may be finding an entry-level position as a data analyst, and then gaining skill and experience over time. This is a standard method of eventually becoming a data scientist.
There are also professional certifications in data analytics available from Google and IBM. These allow an individual to gain the skills needed to be considered for data analyst positions.
Skill sets
There is a lot of overlap between the skills required to be a data analyst and the skills required to be a data scientist. However, differences can be stark. This is particularly noticeable when looking at the programming languages each role uses and how these are used, and the tools and techniques they use to model data.
Data analysts use SQL or Excel to query, clean, or make sense of collected data. Data scientists, on the other hand, will typically use Python or R. Meanwhile, data analysts tend to use Excel for modelling data, while data scientists use machine learning. This is only very generally speaking, though, as some advanced analysts may use programming languages or be familiar with big data.
This table helps to explain the differences in skill sets:
| Data Analyst Skills | Data Scientist Skills |
| Data mining | Data mining |
| Data warehousing | Data warehousing |
| Maths and statistics | Maths, statistics, and computer science |
| Tableau and data visualisation | Tableau, data visualisation and storytelling |
| SQL | SQL, Python, R, Java, Scala, Matlab, and Pig |
| Business intelligence | Economics |
| SAS | Big data and Hadoop |
| Advanced Excel skills | Machine learning |
Roles and responsibilities
Roles and responsibilities will vary and evolve for both data analysts and data scientists, depending on the organisation the employee works for. Itās also important to note that, for some of these job descriptions, roles and responsibilities commonly associated with data analysts may be required for data scientists and vice versa.
We have separated some of the most common below, for an easier understanding of what would be expected their specific positions.
Data analyst responsibilities
- Acquiring data from primary and secondary sources
- Data querying tasks using SQL
- Cleaning and reorganising data for analysis
- Using Microsoft Excel to carry out data analysis and forecasting
- Using business intelligence software to create dashboards
- Performing various types of analytics, including descriptive, diagnostic, predictive, or prescriptive analytics
- Collaborating with an organisationās leaders to identify information needs
- Presenting findings in an easy-to-follow format, in order to inform data-driven decisions
Data scientist responsibilities
- Scrubbing data (this may take up to 60% of a data scientistās time)
- Data mining using APIs or building ETL pipelines
- Using programming languages (Python or R, for instance) to carry out data cleaning
- Carrying out statistical analysis using machine learning algorithms
- Creating programming and automation techniques that simplify day-to-day processes
- Developing big data infrastructures using Hadoop and Spark and tools like Pig and Hive
Job outlook for data analysts and data scientists
Both data analysts and data scientists are expected to be in high demand in the decade between 2021 and 2031. This is true for both the UK and internationally, because a diverse range of industries all currently already use data in order to make decisions and are likely to continue to do so.
The number of organisations, sectors, and industries in general expected to make use of data in their processes are also expected to increase, resulting in more job opportunities.
Salaries
The average salary for a data analyst in the UK is somewhere around Ā£35,000 a year, though broader pay ranges than this are available, depending on skill, experience, and location. Average salaries for data analysts in London range from Ā£39,000 to Ā£50,000.
The average salary for a UK-based data scientist is somewhere between Ā£50,000 and Ā£60,000 annually. For a data scientist in London, starting salaries may be found at around Ā£55,000 per year, which may increase to somewhere between Ā£60,000 and Ā£65,000 within a few years.
Data analyst vs data scientist: which is better?
Technically speaking there is no ābetterā position to have. There is only the position which suits you most and that you feel most comfortable working in. Both roles are highly sought-after by talented individuals, offer excellent opportunities for growth and learning new skills, and generally offer competitive but lucrative salaries. Many of these positions will also come from organisations promising wonderful benefits to their employees, too.
Looking to start a career in data?
If you have been considering a change in career and youāre interested in working with data, Oakleaf Technology, Change, and Transformation can help. By sending us your CV today you will be taking the first step towards the specialist advice and support you need ā to get the position you want.
Our friendly team will be ready to help match you to your ideal Contract, Permanent, Temporary, or even Interim position in data analysis and analytics or data science. It will even meet all your personal needs, as well as professional criteria.