{"id":24307,"date":"2023-02-10T08:12:17","date_gmt":"2023-02-10T16:12:17","guid":{"rendered":"https:\/\/www.springboard.com\/blog\/?p=24307"},"modified":"2025-01-27T04:35:26","modified_gmt":"2025-01-27T12:35:26","slug":"ai-skills","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/ai-skills\/","title":{"rendered":"13 AI Skills To Jumpstart Your AI Career in 2025"},"content":{"rendered":"\n

In the next decade, the demand for artificial intelligence specialists is estimated to grow by 36%. And that\u2019s on top of the massive growth that the field has seen in the past few years. Salaries in fields that employ AI, including data science, often exceed six figures, even for entry-level roles, and so it\u2019s a great time to invest in a future in AI.<\/p>\n\n\n\n

To launch a career in AI, you\u2019ll need in-demand skills to get your foot in the door, and learning these skills can be done quickly and efficiently, but only if you know where to focus your studies.<\/p>\n\n\n\n

That\u2019s why we\u2019ve created this guide. Below, we\u2019ll tell you about the thirteen most in-demand AI skills so you can jumpstart your career and cash in on AI\u2019s lucrative future.<\/p>\n\n\n\n

Are There Any Must-Have Skills for a Career in AI?<\/h2>\n\n\n\n

Yes. To understand how artificial intelligence works and how it\u2019s utilized, you must have a solid understanding of certain mathematical concepts. Bespoke AI solutions are in demand, and so AI programming skills<\/a> are essential too.<\/p>\n\n\n\n

For AI-focused roles like data scientists, machine learning engineers, and robotics engineers, there are quite a few must-have skills. To be involved with the development of AI, you need to understand mathematical concepts like probability and statistics and computing concepts like programming languages and neural networks<\/a>.<\/p>\n\n\n\n

Technical Skills for a Career in AI<\/h2>\n\n\n\n
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The field of AI is progressing rapidly, with new technology always emerging. To keep up with the latest trends and utilize them in your work, technical skills are a must.<\/p>\n\n\n\n

Programming Languages<\/h3>\n\n\n\n

Although low-code AI software is growing in popularity, most businesses with dedicated AI teams will need custom-made systems to achieve their goals. This requires knowledge of one or more of the following programming languages.<\/p>\n\n\n\n

Python<\/h4>\n\n\n\n

Python<\/a> is one of the most widely-used languages in the world and is particularly popular in the fields of AI and data science<\/a>. Professionals use Python for deep learning, neural networks, data mining, and data visualization.<\/p>\n\n\n\n

Related Read: 15 Best Python Libraries for Machine and Deep Learning<\/a><\/em><\/p>\n\n\n\n

Java<\/h4>\n\n\n\n

Java is commonly used for AI-related tasks like genetic programming, procedural programming, and intelligence programming, which help guide AI systems. It\u2019s also used for search algorithms and multi-robot systems.<\/p>\n\n\n\n

Related Read: Which is Better for AI: Java or Python?<\/a><\/em><\/p>\n\n\n\n

C++<\/h4>\n\n\n\n

C++ is a low-level language used to develop advanced elements of AI, like artificial neuron models and neural net functions. The core of an AI program is often written in C++ because it\u2019s good for high performance and speed. <\/p>\n\n\n\n

Julia<\/h4>\n\n\n\n

Julia is popular for machine learning because it\u2019s highly performant and has many machine learning packages available. It\u2019s also used for data visualization and data analytics<\/a>.\u00a0<\/p>\n\n\n\n

R<\/h4>\n\n\n\n

Commonly used in machine learning, R<\/a> is popular for AI-related tasks involving numerical analysis, statistical computations, and neural networks. However, it\u2019s primarily designed for working with data science.<\/p>\n\n\n\n

Scala<\/h4>\n\n\n\n

Scala is useful for dealing with complex algorithms and large data sets, making it a great tool for machine learning. The language is currently growing in popularity, with more and more machine learning and AI-related libraries becoming available. <\/p>\n\n\n\n

Machine Learning Algorithms<\/h3>\n\n\n\n

Machine learning algorithms<\/a> take data and transform it into a model. They\u2019re used to solve regression and classification<\/a> problems and are a must-know for anyone working in AI.<\/p>\n\n\n\n

Deep Learning and Neural Networks<\/h3>\n\n\n\n

A neural network is a computer system designed to mimic how the human brain works. It teaches the computer to process data in a way that\u2019s similar to how we think, and we use it to develop deep learning algorithms. While not every AI-related role will involve working with neural networks, it\u2019s important to understand the processes behind them and why they\u2019re important.<\/p>\n\n\n\n

Data Engineering<\/h3>\n\n\n\n

Artificial intelligence has many uses, but most businesses use it to bolster their data science and analytics. Using machine learning algorithms, businesses can make predictions about their market, their competition, and just about any new venture they want to try. If you want to work using AI rather than developing AI, your role will likely be strongly linked to data engineering. (Learn about the top data engineering courses and how to choose one to grow your skills in this post<\/a>)<\/p>\n\n\n\n

Big Data Analytics<\/h3>\n\n\n\n

To make complex predictions, machine learning requires a very large amount of data. As the amounts of data used continue to grow, different techniques and technologies are needed to scale the data analytics systems accordingly. <\/p>\n\n\n\n

Mathematics and Related Concepts<\/h3>\n\n\n\n

Advanced areas of AI, such as neural networks and deep learning algorithms, are built according to mathematical concepts like linear algebra, calculus, statistics, and probability. For some AI engineers<\/a>, mathematical skills are essential and used every day, while other roles require a solid understanding but won\u2019t work directly with it.<\/p>\n\n\n\n

Data Visualization<\/h3>\n\n\n\n

AI and machine learning<\/a> are commonly used to answer questions using massive data sets, and those answers need to be visualized in order to communicate your findings<\/a> to non-technical stakeholders. Creating charts and graphs that highlight the information you\u2019ve uncovered in a digestible way is an important skill for many professionals working with AI.\u00a0<\/p>\n\n\n\n

Soft Skills for a Career in AI<\/h2>\n\n\n\n
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Technical skills, while important, aren\u2019t everything. AI professionals need to have the right mindset and attitude as well as the right knowledge to thrive in the industry.<\/p>\n\n\n\n

Critical Thinking <\/h3>\n\n\n\n

Critical thinkers are good at spotting mistakes and inefficiencies in whatever is in front of them. The trick is to question and analyze the information you get rather than accepting it at face value and decide if there are any improvements you can make or big questions that need to be asked. Critical skills can be applied across your career, from technical problems to managing teams and projects.<\/p>\n\n\n\n

An Eye for Detail<\/h3>\n\n\n\n

AI programs are complex creations; optimizing them is a job for very detail-oriented people. Something might work, but by taking the time to go over it in a bit more detail, you may find ways to make it work better.\u00a0<\/p>\n\n\n\n

Curious and Creative Mindset<\/h3>\n\n\n\n

There\u2019s no one way to build the best AI, and as technical as the field can be, it\u2019s also very creative. AI engineers must constantly think outside the box to determine how an answer can be reached using the set of tools available.\u00a0<\/p>\n\n\n\n

Working Under Pressure<\/h3>\n\n\n\n

Particularly when it comes to AI used in predictive business analytics, producing up-to-date insights at the right time is very important. Many data scientists work against tight deadlines to get stakeholders the answers they need when they need them. This requires the ability to work under pressure and not let time constraints affect your concentration. <\/p>\n\n\n\n

Teamwork and Cooperation<\/h3>\n\n\n\n

As is the case in most professional roles, soft skills in teamwork and cooperation are key. When you\u2019re working on big projects drawing from big data<\/a>, you\u2019re never working alone. You must use communication skills to align and sync with multiple people and teams to keep progressing efficiently.\u00a0<\/p>\n\n\n\n

Morality and Ethics<\/h3>\n\n\n\n

The most pressing ethical issue in relation to AI is privacy and surveillance. To gather the data needed to generate more powerful and valuable insights, more of what the public does needs to be recorded. It\u2019s important to keep these issues in mind, as what you\u2019ve worked on in the past can affect your reputation and the places that will want to work with you in the future. <\/p>\n\n\n\n

Popular Tools To Know for a Career in AI<\/h2>\n\n\n\n

Programming skills are important in AI, but no one is expecting you to write every line of code you need from scratch. Libraries and software are essential tools for AI professionals, and there are many out there to help you develop your skills. Here are some of the most commonly used tools:<\/p>\n\n\n\n

TensorFlow<\/a><\/h3>\n\n\n\n
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TensorFlow is a Python library that helps AI professionals<\/a> create production-grade deep learning models more quickly and easily. You can find pre-trained models, or build and train your own while participating in an active community.<\/p>\n\n\n\n

Related Read: Tensorflow 1.0 vs. Tensorflow 2.0: What\u2019s the Difference?<\/a><\/em><\/p>\n\n\n\n

PyTorch<\/a><\/h3>\n\n\n\n
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Used by organizations like Amazon and Stanford University, PyTorch are an open-source machine learning framework<\/a> and deep learning library<\/a>. It can be used either with a Python or a C++ frontend and has natural language processing and cloud support.<\/p>\n\n\n\n

Keras<\/a><\/h3>\n\n\n\n
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Keras acts as an interface for the TensorFlow library and works as an API that allows users to interact with artificial neural networks using Python. It focuses on simplifying the process of working with neural networks, including implementing clear error messages. <\/p>\n\n\n\n

scikit-learn<\/a><\/h3>\n\n\n\n
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This is a machine-learning Python library with simple and efficient tools for performing predictive data analysis. It offers supervised and unsupervised learning algorithms<\/a>, cross-validation, and ensemble methods.<\/p>\n\n\n\n

Tableau<\/a><\/h3>\n\n\n\n
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Tableau is a low-code analytics and visualization platform by Salesforce. It\u2019s designed to be usable by a range of different roles, such as analysts, executives, IT, and business users.<\/p>\n\n\n\n

What Are the Different Types of AI Jobs?<\/h2>\n\n\n\n

There are various different career paths associated with AI<\/a>, from those who develop it to those who utilize it. Here are some common role titles that involve AI-related skills:<\/p>\n\n\n\n