Artificial Intelligence

Job roles in the field of artificial intelligence are wide and varied, however, there are some core skills that you need to progress and build a career.

AI Programming

AI Programming is one of the most critical competencies for a career in artificial intelligence. Specialists need to develop strong skills in many programming languages, including Python, R, Java, and C++.

Linear Algebra, Probability, and Statistics

AI specialists need a good working knowledge of linear algebra, probability, and statistics to comprehend and apply the various AI models they work with. Hidden Markov models, Naive Bayes, Gaussian mixture models, and linear discriminant analysis are examples of these models.

Knowledge of Big Data Technologies

AI specialists deal with vast amounts of data, which necessitates the use of big data technologies such as Apache Spark, Hadoop, Cassandra, and MongoDB. Learning these technologies is of great benefit, therefore, for a career in this field. 

Knowledge of Algorithms and Frameworks

Deep knowledge of how machine learning algorithms function is of great value to individuals working in this arena. KNN, Naive Bayes, linear regression, and Support Vector Machine are examples of these. Furthermore, understanding deep learning algorithms and putting them into practice necessitates the use of frameworks such as PyTorch, Theano, TensorFlow, and Caffe. 

Communication and Problem-Solving

AI specialists are often presenting their ideas to company stakeholders and thus strong communication skills are important. To be successful in this field, you also need excellent problem-solving abilities.

To build up these skills, we recommend you check out our range of AI courses and develop the competencies required over time.

We offer a fantastic range of AI online courses that will take you from foundation level learning to advanced competency development. You have a choice of artificial intelligence courses that cover important topics such as machine learning, big data, python, chatbots, r, Watson, data analysis and data visualization, and deep learning.

It's also worth bearing in mind that many of the beginner level courses require no previous programming or AI experience. Plus, the aim of all our courses is to provide learners with job-ready skills they can use straight away.

The field of artificial intelligence has many opportunities, we've listed some of the more popular ones below.

Data Scientist

A data scientist is responsible for collecting and interpreting data. Their work is centered around mathematics and statistics, sophisticated analytics, machine education, and AI. They pull relevant insights from a large pool of organizational data, and they then evaluate the results and extract valuable insights, which give a business a competitive edge.

In recent years, the demand for data scientists has grown by 35%. This increase was not anticipated and has led to a significant skills shortage in the data science arena.

To start a career in data science and AI, basic experience and knowledge of statistics, probability, math, and algorithms are required. However, certifications from accredited institutions are now also prerequisites for many job vacancies.

Machine Learning Engineer

A machine learning engineer oversees the creation and maintenance of machine learning platforms within a business. The role is crucial to AI initiatives and is best suited to those with a background in applied research and data science. When working with massive datasets, machine learning engineers must use predictive models and natural language processing.

Most job listings specify that applicants should have expertise in artificial intelligence, deep learning, and neural networks, as well as solid AI programming, analytical, and cloud application knowledge.  

Those who are well-versed in agile development processes and familiar with software development IDE tools such as Eclipse and IntelliJ will have an advantage when applying for jobs. Many businesses prefer applicants with a master's or doctoral degree in computer science or mathematics. However, individuals with relevant certifications from accredited institutions are also considered a priority.

AI Architect 

Artificial intelligence architects oversee the fundamental requirements of a project. They are responsible for the development and maintenance of architectures based on cutting-edge artificial intelligence technology frameworks. However, this position also combines elements of data science, solutions engineering, and technical expertise.  

To grasp overall mission objectives, AI architects must have a 360-degree view of an AI deployment project. Additionally, they must be able to grasp how AI is applied in business, which requires a detailed awareness of AI patterns, AI platform capabilities, and its data situation. Owing to such demands, an AI architect is not a career entry position, but rather one that needs many years of field experience. 

Business Intelligence (BI) Developer

The primary goal of a business intelligence developer is to evaluate large data sets to spot company and market trends. BI developers typically structure, model, and maintain complex data that's gathered and stored on cloud-based data platforms.

Individuals interested in this role need strong technical and analytical skills. Aspirants must be able to interact well with non-technical colleagues and exhibit strong critical thinking skills.

Unlike many other AI positions, BI developers generally require a bachelor's degree in engineering, computer science, or a related field. However, a combination of hands-on experience and certified qualifications are also highly desired. This means the ideal candidate will be well-versed in data warehouse design, data mining, SQL queries, SQL Server integration services, SQL Server reporting services, and business intelligence technologies.

Big Data Engineer

Most firms seek big data engineers who have completed a Ph.D. in mathematics, computer science, or a similar discipline.

Big data engineers are routinely tasked with creating, planning, and setting up extensive data ecosystems on Hadoop and Spark systems. Thus, to be a big data engineer, you also need extensive AI programming knowledge in C++, Java, Python, and Scala. And extensive knowledge and experience in data mining, data visualization, and data migration is an advantage too.

AI can be applied to a wide variety of use cases across industries and business functions. The sheer volume of AI applications is a major factor driving the increased demand for AI experts. As more and more organizations discover the power of AI, they need experts who can help them navigate these waters.

Now is a very good time to start developing the right skills. Not only is it both rewarding and fun to be learning what drives today's cutting-edge technology, but it's also well remunerated.

For individuals interested in exploring AI, there's a wide selection of learning paths to choose from. And by taking the right steps towards gaining professional certification, it's easy to get started. A technical background is not a prerequisite, and many AI courses are designed for those who have no skills, expertise or experience in coding or IT.

If you're already investigating AI for a career, then it's likely that your interests are aligned with many of the skills required. You may be a keen mathematician, for example, or a whizz in computer science. Those subjects can provide a great foundation for a career in AI.

However, you won't have every base covered yet. The good news is that isn't a problem. If you've never programmed before, Python - one of the most popular languages used in AI - is easy to learn and easy to use. Plus, if you need to work on your knowledge of statistics, that's fine, you can pick up the fundamentals quickly and easily too.

Dive in, read, and discover

The key is to immerse yourself in the world of artificial intelligence. Check out the myriad of articles and videos on the internet. Find trusted sources of information and blog writers and read all you can. Investigate the history of artificial intelligence and get a feel for where things currently are now. It's also worth making a point of considering where AI is heading. You'll be pleased to see what opportunities it holds.

Assess your learning options

Then, once you've built up some solid background knowledge, you will need to start studying. There will be opportunities to learn through a work placement but getting some qualifications under your belt will help secure those positions. There are two main routes to consider: taking a degree in an associated subject, or gaining valuable alternative qualifications, such as professional certification, through artificial intelligence courses.

Degrees

A degree will enable you to apply for a good entry level position once you've graduated.

AI certifications

Certifications in AI will give you an excellent grounding in the subject and provide you with the startup skills you need to begin your career in AI. The range of artificial intelligence courses we offer are an ideal place to start.