FDM Group, whose mission is to bring people and technology together, has offered expert advice on the top ten AI skills to learn to land your dream job.
According to reports by Gartner, the global revenue of artificial intelligence reached $51.5 billion in 2021, experiencing a 14.1% increase from the previous year. This shows no sign of slowing in coming years as AI becomes increasingly ingrained across industries.
AI skills are now not only beneficial, but often required in fields like technology, healthcare, finance, transportation, and marketing, among others.
Understanding AI can provide a competitive edge in the job market, likely resulting in better job opportunities. Possessing AI skills could also lead to higher salaries due to increased demand.
In essence, knowing about top AI skills is a strategic move for job seekers aiming to land their dream job in this rapidly evolving job market.
Here, FDM Group lists the top 10 skills to develop, implement, and manage artificial intelligence systems and applications:
Proficiency in programming languages, such as Python, R, and Julia, is essential for AI professionals. These languages serve as the building blocks for AI development, facilitating the creation of algorithms, data manipulation, and the implementation of AI models. If you’re interested in learning how to code, there are plenty of AI roles at your fingertips, such as Machine Learning Engineering, AI Software Development, Data Science, and much more!
Data modelling and analytics
A deep understanding of data modelling and analytics is crucial as AI algorithms rely heavily on high-quality, structured data for effective learning and decision-making. This AI skill involves the collection, cleaning, and interpretation of data, enabling organisations to make data-driven decisions and construct accurate AI models. Proficiency in tools like SQL and data visualisation platforms is also vital.
Machine learning and deep learning
Machine learning constitutes a substantial domain within the AI landscape, encompassing the majority of AI software and initiatives. So, a mastery of machine learning techniques, including supervised and unsupervised learning, as well as deep learning, like neural networks, is essential. These techniques underpin the development of AI systems that can learn from data, make predictions, and solve complex problems.
Libraries and frameworks
Familiarity with AI libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras streamlines AI model development and deployment. These tools offer pre-built functions and structures that expedite the development process and ensure the efficient implementation of AI solutions. With many businesses looking to prioritise efficiency, and libraries and frameworks being essential for this, candidates who possess these skills will be highly desirable,
Mathematics and statistics
Mathematics and statistics are not just in-demand skills but are fundamental to problem-solving, innovation, and success in AI-related positions.
A strong foundation in mathematics and statistics is fundamental to understanding and optimising AI algorithms. Concepts like linear algebra, calculus, and probability theory are crucial for grasping the inner workings of AI models and for making informed decisions during model development.
Natural Language Processing (NLP)
The Natural Language Processing (NLP) market is anticipated to experience substantial expansion, with its global market size forecasted to increase from $24.10 billion in 2023 to a remarkable $112.28 billion by 2030. Proficiency in Natural Language Processing (NLP) techniques is vital for building applications that can understand, interpret, and interact with human language. This includes sentiment analysis, named entity recognition, and the development of chatbots and virtual assistants, among other applications.
Landing a job in AI is not all about your technical or ‘hard’ skills. In fact, soft skills are just as important. For example, AI professionals encounter unique challenges and uncertainties during model development. Problem-solving skills are crucial for identifying and addressing these issues, adapting to changing circumstances, and optimising AI systems to perform effectively in real-world scenarios.
Critical thinking skills are indispensable in the AI field. Professionals must critically assess AI models for biases, fairness, and ethical considerations. They also use critical thinking to make informed decisions about model selection, feature engineering, and hyperparameter tuning to ensure the best AI performance.
Creativity is not just a skill for those working in ‘creative’ fields. In fact, creativity plays a significant role in AI, enabling professionals to devise innovative solutions and explore novel applications. AI experts often need to think outside the box to create unique AI-driven products, services, or experiences that solve complex problems and meet the evolving needs of industries and society.
As ethical concerns surrounding AI continue to grow, understanding AI ethics is paramount. AI professionals must consider the ethical implications of their work, including issues related to bias, fairness, transparency, and privacy. Ensuring responsible and unbiased AI development and deployment is not just a technical concern but a moral and societal obligation in the AI industry.
For more information on the top AI skills to harness for 2024, visit: https://www.fdmgroup.com/blog/top-ai-skills/