Technology accompanies us everywhere in our daily lives: online banking, music recommendations, social media feeds and facial recognition options to unlock smartphones.
Data Science undergraduates and graduates are highly sought after by all industries as with the current technological advances, more data can be collected and need to be analysed. Data scientists have appeared amongst the top jobs in developing countries and yet some mention that there is a shortage of such expert skills.
Computer Science or related degrees are needed in Data Science and equip candidates with some of the following attributes: multiple coding languages, strong critical analysis and problem-solving skills, and attention to detail. Below is a non-exhaustive list on how you can break through in the field:
- Applications Architect: analyse customer/target audiences behaviour and preferences when using applications in both professional and personal settings. They look out for patterns (such as associations, customers who buy this cooking book usually also buy a mixer) to understand and predict trends. They are also involved in the design process of applications where user-friendliness, user-interface and user-platform are rigorously analysed.
- Data Engineer: spot patterns/trends in any data sets and develop processes (algorithms) to optimise raw data usage. As such, they work to make sense of large datasets so that businesses can use them for business development or assess significance. They rely on numerous skills including SQL database design and coding literacy in multiple programming languages.
- Enterprise Architect: assesses an organization’s operations (typically reports back to the chief information officer) and improves the IT infrastructure (network and services) so that business goals are reached. They ensure the technical platform meets the company’s need. Typical candidates have a Bachelor’s degree in Computer Science and a minimum of 2 year experience in IT.
The CPD accredited courses are carefully crafted to help you gain in-depth knowledge on a topic of your interest.
- Machine Leaning Engineer: develop self-running artificial intelligence software (driverless cars, chatbots, recommended searches), design machine learning systems and create algorithms to address any data analysis problems. Expected responsibilities include managing data pipelines needed to transform code into industrial production, build statistical modelling procedures and communicating results to non-expert panels.
- Machine Learning Scientist: work in the research and development of algorithms used in artificial intelligence (ability of computers to perform tasks which are usually performed by human beings). They differ from Machine Learning Engineers who mainly focus on building data pipelines and machine learning infrastructures. Indeed, Machine Learning Scientists research new approaches and often create novel algorithms.