8 In-Demand Skills You'll Refine in an Artificial Intelligence Master's Program
There is vast, almost limitless potential for implementing artificial intelligence (AI) technologies across myriad industries. From customer assistance chatbots in retail to more tailored treatments for patients in healthcare to face ID on our phones and voice assistants like Alexa, AI transforms how we work and live. Companies will require specialists who can work with intelligent systems even more than they do now.
The Bureau of Labor Statistics predicts an increase of over 500,000 new computer and information technology jobs between 2019 and 2029. That’s a growth of 11 percent, much higher than the average for all occupations. Careers in network engineering and artificial intelligence are in high demand, with high median salaries, often requiring a master’s degree. Workplaces seek experts who can leverage AI and machine learning to help them do business and implement their decision-making. Design thinking is as critical as the individual skills that contribute to it. So, too, is understanding the ethics and biases inherent in algorithms and learning how to mitigate them.
Students with an engineering or technological background and a bachelor’s degree may already have a basic understanding of AI’s uses and applications. Still, the industry evolves so quickly—and encompasses so much—that continuing education via a master’s degree can truly be the most effective path towards employment. Thus, an Online Masters in Computer Science with Artificial Intelligence Specialization (Online MSCS: AI), like the one provided by the SMU Lyle School of Engineering, effectively bridges this gap: relevant, timely training that offers students novel approaches and understanding of AI that they wouldn’t obtain elsewhere. Below, you’ll find eight skills that an artificial intelligence master's program will help you develop, why they’re so critical to the field, and the approach that SMU Lyle Online takes in both core courses and electives to impart these essential insights.
Data management entails the administration, organization, storage, retrieval and other managing actions of data in a database. In essence, it means ensuring that the data remain up-to-date, accurate, within compliance, and optimized with relevant applications. As a database grows, there can be associated risk with the applications not functioning as optimally. Thus there’s also a direct link to data-driven performance. According to Labor Insight, there’s an absolute demand for such a skill set. Nearly 123,000 jobs posted in the last 12 months list database management as a requirement with a 3.7 percent expected market growth. As data collects in larger volumes, the need for database management increases.
The SMU Online MSCS-AI degree program prepares graduate students for the necessity of database management in several ways. In "File Organization and Database Management," students learn the principles of database design and survey the approaches and systems currently utilized. Coursework covers query language design, implementation constraints and applying these tools to large databases (including a survey of file structures and access techniques). Students also learn how to apply these tools in a database design project using a relational database management system.
Algorithm engineering—designing, testing, optimizing, applying, evaluating and analyzing algorithms—is a similarly essential skill set of artificial intelligence. A vital part of this work is algorithm development, determining functional need(s), accessing data, creating models, running, testing and fine-tuning the algorithm to make it work more effectively. Per Labor Insights, nearly 11,500 jobs posted in the last 12 months require algorithm development, with demand expected to increase by 3.4 percent over the next two years. Without appropriate algorithm engineering, data won’t be sufficient for relevant business needs. Similarly, bias can creep into models, and understanding the ethical ramifications can have significant consequences on design.
SMU Online MSCS-AI considers algorithm engineering a critical aspect of coursework. In "Algorithm Engineering," students learn design techniques of algorithms, methods of evaluating their efficiency and specification and implementation of data structure. Work also includes applying fundamental computational problems in various ways, including sorting and selection, graphs and networks, scheduling and combinatorial optimization, computational geometry, arithmetic, and matrix computation. The coursework introduces graduate students to computational complexity, parallel algorithms and a survey of NP-complete problems. Accurate, effective algorithm development is key to AI efficacy.
Developing a deep understanding of artificial intelligence itself may be the most critical aspect of study. Going beyond the simple definition of intelligence demonstrated by machines, AI dives deep into computer science and data science endeavors to build intelligent machines, with the ultimate goal of replicating or even exceeding human skills like problem-solving, pattern recognition and decision making. AI is now part of daily life as humans interface with technology both at home and at work, and improvements in the field occur on a near-constant basis. Thus, demand for AI as a data science skill will grow significantly, over 40 percent, over the next two years. Nearly 99,000 jobs posted in the last 12 months already list AI as a required skill, but expect that to increase.
Since the SMU Online MSCS-AI program focuses entirely on artificial intelligence, coursework is quite rigorous and detailed. "CS 7320 Artificial Intelligence" introduces basic AI principles and research, giving students a sense of depth and breadth. Coursework emphasizes real-world problems and solutions and the deduction of knowledge for predicate logic, nonmonotonic reasoning and fuzzy sets. Students apply these methods to expert systems, planning, language understanding, machine learning, neural networks, computer vision and robotics. The potential for the use of these core AI skills is both broad and deep.
A key aspect of artificial intelligence is machine learning or algorithms that “learn” over time to improve. It is also a way computers can perform actions without explicit programming, and it’s become a buzzword for its potential to deliver improved analytical model building. A program's ability to act independently to make decisions can have significant impacts, such as self-driving cars, for example, to maneuver real-world traffic conditions safely. It should come as no surprise that this is a highly in-demand skill within computer science and over 188,000 jobs posted in the last year list machine learning as a requirement. Demand for machine learning skills will grow by over 39 percent in the next ten years.
Learning from data is critical to effective machine learning and a key component of coursework in "CS 7324 Machine Learning in Python." Following an overview of machine learning techniques used in analytics—including pre-processing, visualization, classification and regression—coursework touches on both classic and contemporary learning techniques, particularly deep learning methods and artificial neural networks. Study is not limited to theory; students complete cutting-edge research. This hands-on experience puts students in good stead to apply these techniques in practice later on.
What are neural networks? Often called deep learning in modern vernacular, the practice is several decades old and is essentially how machine learning occurs. Neural networks are interconnected processing nodes modeled on the human brain's ability to make quick, complex decisions based on previous information. Because of its connection to machine learning, this is a critically important skill set—nearly 12,800 jobs posted in the past year list neural networks as a requirement with a projected growth of almost 15 percent in the next 24 months. It’s also worth noting that if a job lists machine learning as an essential skill, you’ll need to know neural networks to be effective in the position.
As with machine learning, coursework at SMU Online MSCS-AI encompasses both theory and practice. "CS 8321 Machine Learning and Neural Networks" teaches the purpose and principles of machine learning. Coursework includes a survey of current research and other essential topics, like using deep learning to “train” massive networks. Neural networks are crucial to ensuring the efficacy and optimization of machine learning.
Natural Language Processing
Accurate and effective natural language processing (NLP) is critically necessary to interpreting and analyzing human language (a.k.a. natural language). These complicated processes identify, summarize, analyze and model words and phrases to obtain information. NLP helps computers “read” as humans do by assessing language and its meaning. Over 43,500 jobs posted in the last 12 months list NLP as a requirement, with over 27 percent growth expected in the next two years as human-computer interaction increases.
SMU Online MSCS-AI teaches NLP methodology in "CS 8322 Natural Language Processing and Internet Applications." Coursework covers both theory and practice, syntax, semantic, and discourse analysis and how these analyses integrate into NLP systems. Work also includes lexical knowledge acquisition, natural language generation, machine translation and parallel processing of natural language. This skill set is of increasing importance.
Data mining is finding patterns, trends, correlations, anomalies and other connections within large datasets. Such human insights can allow for more effective computational analysis and have real-world impacts like increasing revenue or minimizing performance issues. It also leads to more effective machine learning and, thus, better AI. Unsurprisingly, this is one of the more competitive skills to have. Over 103,500 jobs posted in the last year list data mining as an essential skill set, and a number expected to increase 4.7 percent over the next two years.
SMU Online MSCS-AI provides an understanding of data mining techniques used in analytics: classification, association analysis and cluster analysis. Data mining is taught and reinforced through hands-on experience as one would experience in the field. Other coursework includes reviewing data mining topics and discussing advanced techniques – including those in practical application. When one works with data, data mining usually follows as an essential tool.
Python is perhaps the most critical technical skillset because it’s a general-purpose, multi-functional language. Over 610,000 jobs already list Python as a required skill, and labor market experts predict a 21 percent growth in demand for Python experience over the next two years. In "CS 7324 Machine Learning in Python," SMU Online MSCS-AI teaches machine learning in Python, with assignments completed in the language. Students learn to use Python more effectively as a result. Whether you work in electrical engineering, informatics, data science, data analytics, computer science, or any other focus, you need to know Python.
Charting a Path Towards a Career in Artificial Intelligence
Developing all these skill sets, both individually and in conjunction with each other, via an artificial intelligence master's program is the best way to prepare yourself for a career in AI. SMU Lyle's Online MSCS-AI program allows for customization and focus, regardless of your industry or desired career path. Interactive, project-based courses provide students with hands-on, practical experience and critically important facetime with professors.
There isn’t an industry in which AI and machine learning won’t be useful. SMU Lyle Online prepares graduates for impactful careers, and 82 percent of students find jobs before completing their degree.