Skills Needed for AI Jobs: What Skills Should Be Acquired?

By Dr. Frank Appiah  |  11/05/2024

As artificial intelligence (AI) continues to advance, it has transformed the global job market. Many organizations in multiple industries are seeking candidates equipped with AI skills and knowledge, such as an understanding of:

• Natural language processing (NLP)
• Machine learning (ML)
• Artificial neural networks
• Machine learning algorithms
• Deep learning models
• Data processing
• Predictive modeling
• Data analytics
• Data mining
• Search algorithms

AI can be found in many fields such as marketing, graphic design, healthcare, education, and finance. However, many people are unfamiliar with the basics of artificial intelligence and how AI replicates human intelligence and human language.

The Growing Demand for Workers with Artificial Intelligence Skills

AI has become deeply integrated into our everyday lives, from the way we heat our food to the cars we drive and the methods we use to interact with one another. This widespread impact is a clear indicator of the AI industry’s growth.

The Grand View Research Institute forecasts that the global AI market will experience exponential growth over the next decade. According to its report, the AI market is projected to reach $733.7 billion by 2027. This expansion will influence services, software development, and hardware production.

Artificial intelligence requires sophisticated software for its development, robust hardware to support its infrastructure, and a variety of business problems to solve. As a result, there is a growing demand for professionals skilled in AI, machine learning, natural language processing, and data science.

Companies are increasingly seeking experts to harness AI tools for solving complex challenges and improve business efficiency. Machine learning, which combines computer science with statistical analysis, and data science are the driving forces behind AI’s evolution.

Given this heightened demand for people with artificial intelligence expertise, there is an emphasis on developing skills that go beyond a foundational knowledge of AI and machine learning. These essential skills include advanced programming and technical proficiency in AI tools and frameworks.

What Are the Critical Skills for Artificial Intelligence Jobs?

To be competitive in the AI job market, tech professionals need a blend of hard skills, such as technical skills and analytical abilities. They must also possess soft skills such as critical thinking, problem-solving skills, and good communication skills. These soft skills can help AI professionals to communicate analyses to non-technical audiences and collaborate with program managers and coworkers.

Some of the key AI skills required for AI-related job opportunities include:

• Programming and software development
• Mathematics and statistics
• Machine learning and deep learning
• Data management and preprocessing
• Cloud computing and AI frameworks

Programming and Software Development

Programming skills are vital to any AI practitioner. Knowing programming languages such as Python®, R, and Java®, amongst others, are essential for developing complex algorithms, building machine learning models, and automating complex tasks in data analysis.

Why is a deep understanding of these programming languages so critical in the AI industry? Most AI algorithm development packages are well-established and optimized for these programming environments.

Over time, further advancements in AI have continued to evolve within these programming languages, making them remarkably efficient to develop. While other programming languages such as Scala® have their merits, Python remains the industry standard for AI development.

Mathematics and Statistics

Artificial intelligence is deeply rooted in mathematical principles, particularly in areas such as linear algebra, probability, statistics, and calculus. For instance, machine learning models are constructed using sophisticated algorithms that require a solid grasp of various areas of mathematics.

Machine Learning and Deep Learning

A fundamental aspect of AI lies in enabling machines to learn from datasets with little to no guidance from humans. AI professionals must possess the skills to develop, train, and optimize machine learning, techniques that form the backbone of most AI applications.

Deep learning, a specialized branch of machine learning, enables neural networks to process large volumes of data. It is integral to advanced AI tasks such as image recognition, natural language processing (NLP), and autonomous systems.

The term “deep” is indicative of the degree of complexity of the layers within the architecture. In essence, the more complex layers within the architecture, the deeper it is.

Data Management and Preprocessing

AI systems are only as effective as the quality of the data in which they are trained on, making data management, cleaning, and preprocessing essential skills for AI professionals. Effective data preprocessing ensures that AI models receive accurate, relevant, and properly structured data for training purposes.

AI algorithms take the time to learn and understand intricate relationships within data, so it is critical for this data to be carefully curated to optimize performance. In practice, AI and data science professionals spend approximately 70-80% of their time applying proper treatments to datasets and ensuring they are ready for analysis. This process, known as data cleaning and transformation, involves managing inconsistencies, handling missing values, and formatting data to meet the needs of AI models.

Cloud Computing and AI Frameworks

With the growing complexity of AI models and the vast datasets required for training, cloud computing has become indispensable for storing, processing, and deploying AI applications. Cloud computing, in essence, is a framework that allows for the storage and accessibility of large datasets on a global scale.

In the past, people stored data on local hard drives and other hardware, which limited user access and made it difficult for multiple users to simultaneously work with the same data. Cloud computing eliminates these limitations by enabling data to be stored centrally and accessed by authorized users from anywhere in the world. With the necessary permissions, a user in Africa can access the same data as a colleague in the United States.

This global accessibility and secure data-sharing infrastructure are critical for AI development and deployment. Cloud computing frameworks combined with tools like TensorFlow®, PyTorch®, and scikit-learn®, widely used to build and deploy AI systems, have been pivotal in the rise of AI. These frameworks allow for the seamless integration of AI models with scalable computing resources, enabling faster and more efficient processing of large datasets. 

AI Ethics

As AI technologies become more and more prevalent in various industries, the ethical considerations of using AI have become increasingly important. AI professionals must be aware of the ethical implications surrounding their work, including biases that are built into algorithms, privacy concerns, and the societal impacts of automation.

Every major technological advancement brings tremendous benefits to society, but it also carries significant risks, particularly in cases of potential misuse. As AI technologies continue to evolve, these ethical challenges must be carefully monitored to ensure that AI models are being built, deployed, and managed responsibly.

A key ethical challenge is the need for transparency in AI systems. A transparent approach – where the workings of deep learning algorithms are visible and understandable – inspires greater trust from the public. Ensuring that AI systems are developed with transparency and accountability is critical to building public trust and ensuring that these technologies are applied fairly and ethically.

Computer Science Degrees at AMU

For adult learners interested in computer science, AI technology, AI skills, and other related topics, American Military University (AMU) offers two degree programs:

• An online bachelor of arts in computer science
• An online bachelor of science in computer science

The topics covered in these degree programs include algorithms, data structures, operating systems, database systems, design for the Web, and Web applications programming. Other topics include machine architecture and organization, digital systems, networking, network security fundamentals, and cybersecurity.

AMU’s programs allow students to do coursework that sharpens their technical skills and provides hands-on experience through labs and projects. Our curriculum is continuously updated to integrate the latest advancements in AI, ensuring that students remain competitive and well-prepared to seek job opportunities in the ever-evolving AI market. 

For more information on these degree programs, visit our information technology program page.

Python is a registered trademark of the Python Software Foundation.
Java is a registered trademark of Oracle.
Scala is a registered trademark of Scala, Inc.
TensorFlow is a registered trademark of Google, Inc.
PyTorch is a registered trademark of the Linux Foundation.
Scikit-learn is a registered trademark of the Institute National de Recherche en Informatique et en Automatique.