What is the difference between a Data Scientist vs Data Analyst?

Common core skills of Data Scientists and Analysts
Common core skills of Data Scientists and Analysts

Business analytics emerged as a discipline shortly after Microsoft introduced Excel in 1985, enabling businesses to collect and coordinate much more information than ever before. Over the next thirty years, data analytics grew and evolved to become an integral part of modern business operations. Now Data Science is looking to the future and enabling businesses to create value from data.

So, is a Data scientist just an upgraded Data analyst? While they do have similar core skills, these two roles are quite different, and each is in high demand.

Common core skills of a data scientist vs data analyst

It’s fair to say that these two roles are often confused for each other, even by employers and recruiters. While this is partly due to the relatively young industry of data, it’s also true that the core skills of both a Data Scientist and an Analyst are very similar.

Computer science and coding

At the heart of both roles is a passion for computer science and coding. Proficiency in data munging or data wrangling – the process of organising data into a useful form that can be used for analytics – is also essential. In a business setting, these roles also require a familiarity with the languages of analytics, such as R, SAS, Python and SQL or Structured Query Language which are used to talk to and understand databases. 

Mathematics and statistics

Many find their way to the field of data science or analytics through a proficiency for maths or statistics. While computer science does the heavy lifting in organising and processing data, a deep understanding of statistics is required to interpret visual data representations and identify key performance indicators.

Business acumen

The thing that brings these core skills together and really makes the data sing is business acumen. Being able to view data through the lens of business goals is the key to providing information that informs effective business decisions. 

While common in both roles, these abilities will be applied differently in each field of expertise. Specialising as either a Data Scientist or an Analyst also demands some unique qualities that can be extremely valuable for businesses. 

The unique qualities of a data analyst

Data analysis is objective, with the intention of reporting relevant information with impartiality. When answering questions asked by business teams, the Analyst strives to eliminate bias in processing data for that purpose. On the other hand, data interpretation is subjective and influenced by the worldview of the Data Scientist. They take processed data and rely on their advanced business acumen to make predictions that will create value in business.

Business Intelligence (BI)

An Analyst isn’t expected to build statistical models in the business setting but will more likely use Business Intelligence (BI) software to retrieve, analyse, transform, and report data. These software applications include data management tools, data discovery operations and reporting tools.

ETL tools

A large part of the role is to manage data warehousing with a focus on extract, transform, load (ETL) – a process that brings data together from different sources, transforms it into something that is different from those sources, then loads it into one unified data warehouse. There are a variety of ETL tools available to suit different applications and the Analyst must be familiar with such software. 

The Analyst will also use Business Intelligence (BI) software to retrieve, analyse, transform, and report data. These software applications include tools for data management, data discovery and reporting.

It’s a common misconception that big data means a humongous amount of data and that it’s only for big business. Quality of data is more important than the quantity of data and an Analyst can provide useful insights for any business with access to their own or industry data.

While Analysts are employed on staff, it’s not unusual for them to operate as a business consultant, working with a variety of organisations.

Data Analyst applying her training on the job

The unique qualities of a data scientist

The role of Data Scientist has been declared the top job for LinkedIn’s most promising jobs of 2019 and the best job in America three years in a row on the Glassdoor Job Score. Taking into consideration the level of pay, the number of job openings and voluntary reports from people working in the field, the same report also featured the role of Analyst in the top 50 jobs (at number 38).

Data Scientists are in a unique position where they are not expected to know the answers – they are expected to know the questions to ask! Rather than knowing how to get things done, the Data Scientist is the one who has to figure that out. So, this is a role that suits people who really like to solve puzzles.

Machine learning

One area where Data Scientists specialise is machine learning, which leads to the common misconception that machines learn. Machine learning is similar to computational statistics, which uses statistics to make predictions using computers and algorithms. If an algorithm is viewed as a decision-making tool and it is provided with the right answers in advance, then it can predict answers for new observations. Rather than being a machine that learns, it’s a machine that has been programmed and supervised by a Data Scientist to deliver the desired response.

Storytelling through data

In addition to familiarity with a wide range of computer languages, human storytelling skills are also important in data science. Data Scientists report to senior management and boards who are often less computer literate. They need to be able to translate data into a business story that enables managers and teams to develop plans that can be put into action. While the best Data Scientists excel in business acumen, computer science, coding, maths and statistics – the true unicorn can do all that and put the results into words.

Data Scientists are more likely to be employed on staff, or in specialist teams, rather than operating as a business consultant.

Data scientist vs data analyst skills

Skills Data Scientist Data Analyst
Computer science and coding
Data munging/data wrangling
Structured query
Business acumen
Business Intelligence (BI)  
AI & machine learning  
Data visualisation
Human storytelling  

The value of specialisation

Both the Data Scientist and the Analyst are in high demand. Where big data was once the privilege of large companies like Google and Facebook, businesses of all sizes are now collecting their own data. There is now more data than there are specialists available to scrutinise and convert it into useful information.

Interested in starting a career in data science or analytics?

UNSW Online offers professionals from a wide variety of backgrounds an opportunity to upgrade their qualifications to specialise in data science or analytics. The Master of Data Science and the Master of Analytics have been designed for graduates of undergraduate courses in IT, engineering, maths, statistics and a number of other disciplines.