Being highly technical, complex tech fields, those looking to enter the data and AI industry are recommended to pursue formal training in either area. You’re more likely to gain employer attention with recognised certifications under your belt, along with a strong portfolio of your previous experience.

Those in data science are often encouraged to specialise. Though your skills are applicable to any field, employers will typically seek out industry-specific experience, making it critical to hone your skills in your preferred sector. As mentioned, having at least a basic concept of machine learning and AI can help boost your employment value in the field.

Those in AI are required to build their knowledge deep learning, neural networks, and machine learning to start exploring entry-level opportunities in the job or internship market. Of course, a fundamental understanding of big data and data analytics can help broaden your horizons.

On top of formal education, aspiring data and AI experts are also encouraged to expand their skills through personal practice. Plenty of online communities exist to help you learn from and collaborate with other like-minded professionals, these including Stack Overflow, Kaggle, and Dataquest for those in data science; and GitHub, Global AI Community, and r/machine learning (Reddit) for those into AI.

Data refers to the information we produce through computer systems; from text, multimedia, and programs we create, to our communications and transactions via social media and online services. Artificial intelligence is the development of “smart” machines that, through the use of data, are able to mimic human-like tasks and behaviour. Such technology is gaining widespread use in the impending age of automation, where AI systems are increasingly adopted to handle menial, repetitive business processes.

The two hold a symbiotic relationship, in which data science tactics are used to bolster development of AI technologies, while AI technologies are used to improve the processes of data science and analysis. Those working in the latter field are typically termed as “data scientists”, who spend much of their time cleaning and extracting valuable insights from both structured and unstructured datasets. Those working in the former field are generally considered “AI specialists” or AI developers, focused on the programming and science behind crafting “intelligent” code or machinery.

Data and AI professionals require high levels of both technical and non-technical skills.

Those in either field need a strong grasp of various programming languages, including (but not limited to) Python, SQL, Java, R, and C++. A strong knowledge of analytical tools is also necessary for data scientists, these including software such as SAS, Hadoop, Pig, Hive, and Spark. Artificial intelligence experts are expected to have an advanced knowledge of machine learning and deep learning, along with a close familiarity of programming frameworks such as TensorFlow, NumPy, and SciPy. Plenty of these technical skills may overlap between fields, helping you flexibly find work within either sector – though having experience in both areas can help bolster your opportunities on the job market.

Both professions require expertise in mathematics (i.e. algebra, statistics, probability), and excellent skills in communication, business planning, analytical thinking.