Data science and data analytics can both help businesses succeed

It's important to know the differences between the two disciplines

Data has undoubtedly transformed the way organisations around the world conduct their business, reimagine their products and services, and reach their customers.  

Data science and data analytics both play a vital role in gathering and harnessing data to make data-backed business decisions and accelerate business growth and success. 

But despite both data science and data analytics being clearly data-related disciplines, there are significant differences between the two. 

What is data science? 

Data science is a multidisciplinary field that focusses on collecting large sets of unstructured raw data to discover previously unexplored questions and answers, to help drive innovation.  

Data scientists use various tools and techniques such as statistics, predictive analytics, computer science and machine-learning to translate large data sets into innovative solutions that businesses have not yet considered. 

Essentially, data science focusses on uncovering and asking the right questions that will help to drive innovation. 

What is data analytics? 

Data analytics is a more specific area of data science. It focusses on processing and analysing existing data sets to deliver actionable insights. 

Data analysts collect, organise and analyse data to identify trends and create visual data representations to help businesses make more data-informed business decisions. 

So, while data science is concerned with utilising data to ask the right questions, data analytics focusses on harnessing data to come up with solutions to those questions. 

What does a data scientist vs a data analyst do? 

Although the roles of a data scientist and data analyst can be closely related and often overlap, there are some significant differences between the two. 

Data scientist 

A data scientist collects large sets of raw data for an organisation from a range of sources, by utilising tools and techniques such as machine-learning, artificial intelligence, algorithms, data mining and statistics.

They then broadly interpret and present this data in a clear and meaningful way to businesses, to help them to identify previously unexplored business questions and predict upcoming trends. 

Data analyst 

A data analyst analyses existing data sets and translates them into actionable solutions to business questions and problems.  

Data analysts utilise their analytical skills to discover important insights about businesses and their customers, and transforms these insights into impactful business decisions to drive organisational growth and success.

Like data scientists, they also share their data insights with key stakeholders within the organisation in a way that’s easy to understand. 

What skills do you need to be a data scientist vs a data analyst? 

Again, because the roles of a data scientist and data analyst can be closely linked and overlap, so too can the skills required. 

Here are some of the skills you may need to be a data scientist: 

  • Computer programming skills in various programming languages, such as Python 

  • Proficiency in relevant software and tools such as Hadoop, Knime, Tableau and Spark 

  • Advanced skills in mathematics and statistics 

  • Data visualisation skills 

Here are some of the skills you may need to be a data analyst: 

  • Analytical skills 

  • Reporting skills 

  • Communication skills 

  • Programming skills 

  • Proficiency in business intelligence and data analytics systems and tools 

  • Data visualisation skills 

Who is suited to a data science vs data analytics career? 

Because data scientists need to be able to source, organise and interpret large sets of raw data, those with advanced skills in mathematics and statistics, as well as advanced proficiency in computer programming and various programming languages, are best-suited to a career in data science. 

Similarly, yet with some clear differences, data analysts need to have strong analytical, mathematical and communication skills, but they don’t necessarily need to be highly proficient in programming and its various languages.

The focus is instead on having advanced analytical, problem-solving and data visualisation skills. If this sounds more like you, then you’d probably be better-suited to a career in data analytics. 

Why you should study data analytics if you want to work in the data analytics field 

Although you might already consider yourself a great problem-solver and analytical thinker, studying and gaining a qualification in data analytics can empower you with the specialist skills, techniques and knowledge you need to fast-track your career in the data analytics field and boost your success. 

By studying a course in data analytics, you’ll be able to master what it takes to tackle the real-world challenges of working with data and deliver impactful business solutions. 

If you’re interested in data analytics, University of Portsmouth offers a flexible online MSc Data Analytics that will equip you with the advanced skills and knowledge you need to succeed as an in-demand data analyst in today’s global job market:

Explore the course  ❯

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