Revolutionising the way we do business: artificial intelligence and data science

Global communication interconnectivity is a vast market, and the drive to capitalise on the skills required in terms of artificial intelligence (AI) and data science remains on a high growth trajectory.  

Forecasts for the data analytics industry up to 2023 predict exponential expansion. In The Global Big Data Analytics Forecast to 2023, Frost and Sullivan project growth at 29.7%, worth a staggering $40.6 billion. 

Differentiating between data science and AI

The impact of technology in, and on, modern life has been, and continues to be, all-pervasive and massively transformative across all areas. An understanding of both sectors is integral to considering future potential – in terms of influence and ramifications for all global citizens. Used in all day-to-day interactions at source data science is, essentially, the interdisciplinary field of study focused on the acquisition, and derivation, of knowledge and insights from data. AI is machine-learning intelligence, in contrast with natural intelligence such as that of humans. Increasingly, there is an overlap between the two disciplines; however, they are unique in their own way and are used for different purposes.

While specific functionalities differ, examples of universally recognised and branded companies and industries increasingly using data and artificial intelligence applications are:

• Amazon, and other retail giants
• Microsoft
• Google
• Facebook
• Netflix
• Mobile and telecommunications market leaders

Real-world application

As an ever-evolving market, the world of proactively conducting data collection and data analysis across all technological information-gathering necessitates acquiring particular skills and skill sets. Cutting-edge understanding of new data operating systems and software is essential, as neologisms continue to be introduced to embrace the latest sectoral innovations. Certain computer science and software terms are already in everyday use; amongst others, Python (e.g. XML, R Matlab), and SQL (the standard language for storing, manipulating and retrieving data from a database). 

As the relevance and accuracy of online information research demands increase, skilled professionals will be required to embrace all the different areas of datasets and data mining required by both internal and external stakeholders.

Methodologies such as big data – involving extremely large data and dataset volumes coming from multiple sources and in various presentations – are commonplace. Industry experts such as IBM invest heavily in data collection, processing, cleaning, deep learning, predictive analysis and data mining. Data scientists and professionals employ all manner of interpretive tools including mathematical concepts acquisition. For example, regression analysis and graphic representation tools such as visualisation. 

Data management, warehousing, cleansing and analysis, as dictated by algorithms and machine neural networks, are all involved in complex solutions generation and business intelligence decision-making.

Automation, data science and the use of AI to imitate human learning via its machine-learned algorithms will only continue to expand. As world trend and pattern predictions become more embedded and accepted, natural language processing will become even more sophisticated and seamlessly interwoven in our lives.

Checks and balances

However progressive AI and data science are, there must also be checks and balances in solving real-world problems and ameliorating the lives of all. In the drive for business and real-world solutions, ethical issues and well-founded worries about AI in, for example, the areas of privacy and jobs for people, are already proliferating. 

The late, famous scientist Sir Stephen Hawking raised potential concerns about AI prior to his death in a December 2014 interview. Similarly, many different professional bodies are aware of the need to provide ethical guidance on the use of such data. For example, the publication of IFoA Ethical Guidance on Data Science in February 2021 by the Institute and Faculty of Actuaries UK advises that actuarial skills are predicated on the analysis of complex data and its interpretation and use.

Careers in data science and AI

Data science and AI are invested in many wide-ranging career applications and pathways: from retail, media, healthcare, banking and finance, to construction, transportation, insurance, manufacturing and environmental sustainability. There are limitless possibilities, and professionals in whichever segment of the field are in high demand by diverse employers, whatever their size and make-up.

Popular roles include:

• Data scientist
• Business intelligence analyst
• Statistician
• Data analyst
• Data engineer, AI engineer or machine learning engineer
• Data architect
• Applications architect
• Research scientist

Examples of top employers for individuals with data science and AI skill sets include Walmart, Amazon, Hewlett Packard Enterprise, IBM, Deloitte and numerous other tech-forward companies.

For further information and employability prospects, specialised recruitment agencies such as STEM graduate jobs, alongside other vacancies on platforms such as Indeed or Totaljobs, give a fuller insight into the wealth of opportunities available.

Higher educational study

Given the nature of global online connectivity, and the ability to live and work worldwide, the pool of potential employees is vast. As such, the English language requirements for subject areas in AI and data science for those who hold English as an additional language can be accommodated by accredited providers such as IELTS. In order to access universities and other higher education institutions, international students wishing to pursue full-time or part-time postgraduate degrees can find full details online. 

All information relating to prospective students with regard to questions regarding study – i.e. entry requirements, outlines of projected course work and learning structure, and research project demands – can be answered on such pages. Academic structuring information in terms of tutorials, and any work experience and production of case studies relating to the subject, is also included. As a matter of course, any financial details regarding tuition fees, assistance with postgraduate loans for MSc programmes, accommodation issues, and ancillary issues such as start dates for courses will be posted.

Want to be part of the data science and artificial intelligence sector?

Take the first step towards a dynamic, creative and rewarding career in data science.

If you’re looking for a university offering theoretical and practical knowledge and support, together with holistic pedagogical and real-world expertise, choose Keele University’s online MSc Computer Science with Data Analytics programme. Open to all – whether you have a background in computer science or not – you’ll develop a comprehensive understanding of data management and its multivariate applications. In a flexible, supportive environment, your studies will encompass artificial intelligence methods, data visualisation tools and techniques, problem solving with data, and programming languages including Python, R and Matlab.