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Skills in demand: working with data

A woman standing behind a transparent data screen screen

Data skills are transferable across industries and demand for workers is high.

Data are facts and statistics collected together for analysis. Data workers make sense of large amounts of information which would otherwise be too hard to understand.

A wide range of industries benefit from employing data workers. For example, in hospitals data can be used to track and improve the speed at which patients are admitted or discharged.

Data can also be used to improve search results on websites like Google, or to track and analyse user activity on social media.

According to a Domo report, by 2020 there will be 40 times more bytes of data than there are stars in the known universe.

Demand for data workers has never been higher and this trend will continue.

Types of data workers

Data analysts

Data analysts look at raw data and draw conclusions. They find out what information clients need to make good business decisions. Data analysts need a range of skills such as programming, statistical, mathematics, machine learning, data wrangling, communication and visualisation.

Areas where data analysis can be useful:

  • healthcare
  • travel
  • gaming
  • business
  • energy management.
Data scientists

Data scientists advise industries, institutions, businesses and customers by analysing large amounts of information or “big data”. An example of data science is the use of algorithms to deliver the best internet search results. Data scientists need a range of skills such as analysis, creativity, mathematics, statistical, coding and business skills. 

Areas where data science and big data can be useful:

  • internet searches and recommendations
  • digital advertisements
  • financial services
  • retail
  • communication
  • customer analytics.
Data engineers

Data engineers provide a reliable digital infrastructure for data. They create the systems and platforms which organise data and allow data analysts and data scientists to work with it. They’re usually responsible for backend development such as data delivery, storage and processing. Data engineers need to have knowledge of software engineering and coding, as well as specific software platforms.

Skills and training options

The three areas of expertise required to work with data are:

  • maths and statistics
  • algorithms and software engineering
  • communication, visualisation and presentation.

Useful secondary school subjects for a career in data include:

  • maths
  • computer studies or computer science
  • general science
  • English
  • visual design.

Useful undergraduate qualifications for a career in data include:

  • Bachelor of Computing and Mathematical Sciences
  • Bachelor of Computer and Information Sciences
  • Bachelor of Software and Information Technology.

Further training options for a career in data include:

  • Graduate Diploma in Data Analytics
  • Master of Analytics
  • Master of Business Data Science
  • Master of Data Science.

Data jobs

Data is a new field of work which is still growing. Job titles and descriptions may vary for different countries, organisations and businesses.

Data jobs include:

Data workers have valuable and transferable skills

Data skills are transferable across a wide range of industries, and demand for data workers is strong. As the occupation develops there is plenty of room for creative thinkers and entrepreneurs.

Data is a powerful tool for improving complex systems, businesses, institutions and even people’s lives.

Sources
  • Burn-Murdoch, J, ‘Big data: what is it and how can it help?’ Datablog, 26 October 2012, (www.theguardian.com).

  • Domo, ‘Data Never Sleeps 7.0’, accessed August 2019, (www.domo.com).

  • Gifford, A, ‘Data Boffins in High Demand’, NZ Herald, 5 December 2015, (www.nzherald.co.nz).

  • Long, J, ‘Every School in New Zealand Needs a Data Scientist, Microsoft Says’, Stuff, 12 May 2019, (www.stuff.co.nz).

  • Monnappa, A, ‘Data Science vs. Big Data vs. Data Analytics’, Simplilearn, 11 July 2019, (www.simplilearn.com).

  • Speirs, A, ‘Innovation – The Rise of Big Data’, NZ Herald, 4 March 2014, (www.nzherald.co.nz).

  • Verma, E, ‘A Day in the Life of a Data Scientist’, Simplilearn, 26 November 2018, (www.simplilearn.com).

  • wikiHow Staff, ‘How to Become a Data Analyst’, wikiHow, 28 March 2019, (www.wikihow.com). 

Updated 12 Sep 2019