• Denmark High School Artificial Intelligence Pathway  

    ARTIFICIAL INTELLIGENCE PATHWAY

    Artificial Intelligence is an area of study that deals with the simulation of intelligent behavior in computers. Artificial Intelligence sits at the intersection and synergy of critical components from a variety of fields including programming, data science, mathematical reasoning, creative problem solving, ethics, and applied experiences. Students in the AI pathway will apply professional software development tools and processes to create functional, real-world applications of Artificial Intelligence using machine learning.

    Careers that require Artificial Intelligence skills (machine learning, data science, programming, etc.) are on the rise and many careers that have existed for years, like Data Analyst or Software Developer, are shifting and growing in industries designing Artificial Intelligence solutions.

    • Machine Learning Engineer
    • Data Scientist
    • Machine learning researcher
    • Data analyst
    • Artificial intelligence engineer
    • Algorithm specialist
    • Mechanical engineer

     

    Pathway Courses and Concepts

    1. Foundations of Artificial Intelligence

    Artificial Intelligence is an area of study that deals with the simulation of intelligent behavior in computers. Artificial Intelligence sits at the intersection and synergy of critical components from a variety of fields including programming, data science, mathematical reasoning, creative problem solving, ethics, and applied experiences.

    Careers that require Artificial Intelligence skills (machine learning, data science, programming, etc.) are on the rise and many careers that have existed for years, like Data Analyst or Software Developer, are shifting and growing in industries designing Artificial Intelligence solutions.

    Topics/Skills:

    • Python Development: Algorithms, Conditionals, Iteration, User/Sensor Input
    • Data Classification (e.g., Google Teachable Machine, Weka)
    • Big Data, Predictive Algorithms, Decision-making
    • Excel, Data Visualization, Storytelling
    • Bias, Perception, Privacy, Accuracy, Ethics, Legal, Society
    • Use a creative problem-solving process to collaboratively solve real-world problems relevant to AI
    • Define, describe, and demonstrate productive collaboration, problem-solving, and leadership skills.
    • Apply computational thinking skills to find alternative or creative solutions to problems.

    2. Artificial Intelligence Concepts

    • Machine Learning, Natural Language Processing, Computer Vision
    • Supervised/Unsupervised Learning: Regression, Classification, Clustering, Reinforcement Learning.
    • Pattern Matching, Recursion, Parallelization, Automation
    • Advanced Python Object-Oriented Programming, Data Structures, Database Management Systems (e.g., SQL)
    • Advanced Excel spreadsheet functions, formulas, conditional formatting, cell referencing, and pivot tables
    • Create data tables/graphics (two-way tables, scatterplots, bar graphs, histograms, stem plots, and dot plots)
    • Data Visualization Tools (e.g., Jupyter Notebooks, Matplotlib)
    • Statistical Analyses: regression analysis, ANOVA, hypothesis testing, and sampling distributions

    3. Artificial Intelligence Applications

    • Genetic algorithms, Robotics, Computer vision
    • Networks and cloud services that use AI solutions (Neural Networks, data management, Edge AI)
    • Open-source AI tools (e.g., Tensorflow, ScikitLearn, Spark ML, PyTorch)
    • Proprietary Artificial Intelligence tools (e.g., Microsoft Azure AI, Amazon Web Services, Google AI, IBM Watson)
    • Team-based software development (e.g., Agile) using professional collaboration tools (e.g., Version Control System, GitHub)
    • Use IDEs (e.g., VS Code, PyCharm, Jupyter, Sublime) and packages (e.g., Fast AI, Scikit-Learn, Pandas, Runway ML, Tensorflow, Make Code, PyTorch) to build and train machine learning models.
    • Balanced/imbalanced datasets. Training, validation, and test datasets.
    • Data collection, manipulation, cleansing, and transformation
    • Embedded computing: circuits, sensors, microcontrollers, microcomputers, motors, and other components

     

    EDUCATION AND SKILLS FOR ARTIFICIAL INTELLIGENCE CAREERS

    Not all AI positions are the same. Different roles might need different skills/experiences. However, nearly all entry-level roles will expect:

    • Graduate degree in computer science, mathematics, or statistics
    • Programming skills/languages—Familiarity with Python and SQL
    • Knowledge of data analysis, processing, and visualization
    • Understanding of cloud technologies
    • Business acumen about the industry, market, competition, etc.

     

    RECOMMENDED POST-SECONDARY COURSES

    Math/Statistics:

    • Linear Algebra
    • Differential and Integral Calculus
    • Matrices and Linear Transformations
    • Integration and Approximation
    • Modern Regression
    • Probability Theory
    • Bayesian Networking
    • Probabilistic Graphical Models

    Computer Science:

    • Computer Systems and Programming
    • Principles of Imperative Computation
    • Principles of Functional Programming
    • Data Science Essentials
    • Parallel and Sequential Data Structures and Algorithms
    • Logic Programming and Computational Logic
    • Agile Software Development

    AI Core Subjects:

    • Machine Learning, Deep Learning, and Reinforcement Learning
    • Information Theory, Inference, and Learning Algorithms
    • Neural Networks for Machine Learning
    • AI Representation and Problem-Solving
    • Natural Language Processing
    • Computer Vision and Image Analysis

     

    POST-SECONDARY DEGREES, DIPLOMAS, AND CERTIFICATES

    Top AI Certification/Certificate Programs

    • Certified Artificial Intelligence Engineer — United States Artificial Intelligence Institute
    • CertNexus Certified Artificial Intelligence Practitioner Professional Certificate
    • Google Data Analytics Certificate
    • IBM Applied AI Professional Certificate
    • IBM AI Engineering Professional Certificate
    • IBM Data Science Professional Certificate
    • IBM Data Analyst Professional Certificate
    • Microsoft Certified: Azure AI Engineer Associate
    • Microsoft Certified: Azure AI Fundamentals

     

    Top AI Education/Prep Programs

    • Springboard Machine Learning Career Track
    • Artificial Intelligence: Kellogg School
    • Master the Fundamentals of AI and Machine Learning: Learning Path
    • Azure AI Engineer Associate: Microsoft
    • Post-Graduate Program in AI and Machine Learning: Purdue/IBM
    • Machine Learning: Stanford University
    • eCornell Machine Learning Program: Cornell University
    • Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning: Professional Certificate from DeepLearning.AI
    • Machine Learning: Fundamentals and Algorithms: Carnegie Mellon University
    • Artificial Intelligence: Implications for Business Strategy: Certification Program from MIT
    • Designing and Building AI Products and Services: MITxPRO
    • Artificial Intelligence: Business Strategies and Applications: Berkeley University

     

    COLLEGES/UNIVERSITIES

    • Bachelor of Arts in Applied and Computational Mathematics
    • Bachelor of Arts in Cognitive Science
    • Bachelor of Arts in Information and Data Sciences
    • Bachelor of Science in Artificial Intelligence
    • Bachelor of Science in Computer Science with AI Research
    • Bachelor of Science in Electrical/Mechanical Engineering
    • Master of Science in Artificial Intelligence & Innovation
    • Master of Science in Computational Science & Engineering
    • Master of Science in Analytics

     

    CAREER AND TECHNICAL STUDENT ORGANIZATION:

    • FBLA (Future Business Leaders of America)

     

    Artificial Intelligence is a Good Career:

    • AI jobs are plenty, hiring growing by 32% in the last couple of years
    • There is a high talent gap—not enough qualified applicants for vacant positions
    • AI professionals earn top salaries, well north of $100,000
    • As a rapidly evolving industry, growth opportunities in AI careers are diverse
    • AI careers are flexible—you could be a freelancer, consultant, researcher, practitioner, or even build your own AI products
    • AI jobs seek applicants with strong mathematical skills, experience in machine learning, deep learning, neural networks, and cloud applications, and programming skills in Java, Python, and Scala.

    The current AI job outlook is quite promising. The US Bureau of Labor Statistics expects computer science and information technology employment to grow 11% from 2019 to 2029. This will add about 531,200 new jobs in the industry. This, it appears, is a conservative estimate. “AI and Machine Learning Specialists” is the second on the list of jobs with increasing demand as per the World Economic Forum.

     

    TOP CAREER CHOICES

    Machine Learning Engineers (leverage big data tools and programming frameworks to create production-ready scalable data science models that can handle terabytes of real-time data.); $145,300+ average annual salary

    Data Scientists (collect and analyze data to glean insights using various technology tools, processes, and algorithms, extracting knowledge from data and identifying meaningful patterns.); $119,300+ average annual salary

    Business Intelligence Developers (process complex internal and external data to identify trends,  typically responsible for designing, modeling, and maintaining complex data in highly accessible cloud-based data platforms.); $92,200+ average annual salary

    ADDITIONAL CAREER CHOICES

    • Researcher/Research Scientist
    • Big Data Engineer/Architect
    • Software/AI Engineer
    • Software Architect
    • Data Mining/Analysis
    • Robotics Engineer
    • Natural Language Processing Engineer
    • User Experience (UX) Designer/Developer
    • Computer Vision Engineer