AI & Data Science
Artificial intelligence and data science are interdisciplinary fields that involve the use of advanced computational methods and algorithms to make predictions, automate tasks, and extract insights and knowledge from complex data. Overall, there are many exciting career opportunities in the field of AI and data science, and the skills you have developed can be applied to a wide variety of roles.
These positions often require a combination of technical and communication skills, including programming, data analysis, statistical reasoning, problem-solving, and communication. Candidates with expertise in deep learning and machine learning are in high demand in the AI and data science job market, especially in industries like healthcare, finance, and e-commerce.
An introduction to the fundamentals of AI and the cutting-edge tech that is rapidly changing our world. Students will collaborate with each other, mentors, and Pria in an online AI Lab. In the final project, they will build their own large language model using the skills and coding expertise they developed throughout the course. Qualifies for the Foundations of AI digital badge.
Using R Studio, students will discover several data visualization and graphing techniques, learn how to explore and analyze data using various methods, mine Twitter for sentiment analysis, and gain practice using interactive shiny apps. Qualifies for the Foundations of Data Science digital badge.
Foundations of AI
Earners of the Foundations of AI credential have successfully demonstrated experiential skills in data science, statistical learning, clustering, neural networks, generative AI and practical AI applications. The Foundations of AI badge requires 50+ hours of hands-on activities and labs across 16 skills that make up a foundational understanding of artificial intelligence for both research and industry applications. The Foundations of AI credential was built in collaboration with AI expert Reed Bender.
Foundations of Data Science
Earners of the Foundations of Data Science credential have successfully demonstrated experiential skills in the creation of polished data products through advanced data visualization techniques. The Foundations of Data Science badge requires 50+ hours of hands-on activities and labs across 10+ skills in data science. The Foundations of Data Science credential was built in collaboration with data science expert Dr. Josh Vandenbrink.
Top AI & Data Science Careers
Here are the top 10 careers in AI & Data Science leveraging the skills earned in these Praxis courses and digital credentials. It is worth noting that these are just a few of the many exciting career paths available in these high-demand fields. Also, many of these roles require a strong foundation in programming languages like Python and a deep understanding of data structures and algorithms.
- Data Scientist: As a data scientist, you would be responsible for analyzing large amounts of data and extracting insights to help organizations make informed decisions. You would use your skills in statistical hypothesis testing, exploratory data analysis, data visualization, linear regression, and model evaluation to uncover patterns and trends in the data.
- Machine Learning Engineer: As a machine learning engineer, you would develop and implement algorithms that enable machines to learn from data and make decisions. You would use your skills in linear algebra, feature engineering, K-nearest neighbor classification, decision tree classification, multi-layer perceptrons, and deep neural networks to build and optimize these algorithms.
- Natural Language Processing (NLP) Specialist: As an NLP specialist, you would work on developing algorithms that can process and analyze human language. You would use your skills in natural language processing, deep learning, and neural networks to build systems that can understand and respond to human language.
- Computer Vision Engineer: As a computer vision engineer, you would develop algorithms that enable computers to interpret and understand visual data, such as images and videos. You would use your skills in convolutional neural nets, deep learning, and computer vision to build and optimize these algorithms.
- AI Ethics Specialist: As an AI ethics specialist, you would work to ensure that the development and implementation of AI systems are done ethically and with consideration for social, legal, and ethical implications. You would use your skills in data ethics, regulatory compliance, and stakeholder engagement to develop responsible AI practices.
- Business Intelligence Analyst: As a business intelligence analyst, you would work with data to provide insights to organizations about their operations and customers. You would use your skills in data visualization, statistical hypothesis testing, and exploratory data analysis to develop reports and dashboards that help organizations make data-driven decisions.
- Big Data Engineer: As a big data engineer, you would develop and manage large-scale data infrastructure and systems. You would use your skills in data-intensive computing, data engineering, and database design to build and optimize data pipelines that can handle massive amounts of data.
- Data Analyst: As a data analyst, you would work with data to identify patterns and trends that can help organizations make informed decisions. You would use your skills in exploratory data analysis, statistical hypothesis testing, and data visualization to develop reports and insights that drive decision-making.
- AI Product Manager: As an AI product manager, you would work with cross-functional teams to develop and launch AI products. You would use your skills in data visualization, model evaluation, and engagement with generative AI to develop products that meet customer needs and achieve business goals.
- Data Visualization Specialist: As a data visualization specialist, you would work with data to develop compelling and informative visualizations. You would use your skills in plotting with GGPlot and advanced plots with Plotly to create visualizations that effectively communicate complex data to a variety of audiences.