{"id":119654,"date":"2026-09-14T15:38:45","date_gmt":"2026-09-14T10:08:45","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/applied-ai-data-science-curriculum-qa-engineer\/"},"modified":"2026-09-14T15:38:48","modified_gmt":"2026-09-14T10:08:48","slug":"applied-ai-data-science-curriculum-qa-engineer","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/applied-ai-data-science-curriculum-qa-engineer\/","title":{"rendered":"Applied AI and Data Science Curriculum: A QA Engineer\u2019s Learning Experience"},"content":{"rendered":"\n<p>For technology professionals, the skills that matter are shifting fast and few illustrate that better than Praveen Kumar Gaddam, a Staff Software Quality Engineer with 15 years of experience. The <a href=\"https:\/\/www.weforum.org\/publications\/the-future-of-jobs-report-2025\/digest\/\">World Economic Forum\u2019s <em>Future of Jobs Report 2025<\/em><\/a> estimates that 39% of workers\u2019 core skills could change by 2030, highlighting the growing importance of continuous learning and upskilling.<\/p>\n\n\n\n<p>For a QA Engineer with 15 years of experience, learning applied AI and data science means moving past existing technical expertise. It means understanding how data, Machine Learning, and AI apply to real business problems. <a href=\"https:\/\/www.linkedin.com\/in\/praveen-gaddam\/\">Praveen Kumar Gaddam\u2019s<\/a> experience with MIT Professional Education\u2019s <a href=\"https:\/\/professional-education-gl.mit.edu\/mit-online-data-science-program\">Applied AI and Data Science Program<\/a> offers one perspective on that learning journey.<\/p>\n\n\n\n    <div class=\"courses-cta-container\">\n        <div class=\"courses-cta-card\">\n            <div class=\"courses-cta-header\">\n                <div class=\"courses-learn-icon\"><\/div>\n                <span class=\"courses-learn-text\">Explore Data Science Program<\/span>\n            <\/div>\n            <p class=\"courses-cta-title\">\n                <a href=\"https:\/\/professional-education-gl.mit.edu\/mit-online-data-science-program\" class=\"courses-cta-title-link\">MIT Professional Education's Data Science Course<\/a>\n            <\/p>\n            <p class=\"courses-cta-description\">Gain the expertise top companies seek and open doors to Data Science jobs.<\/p>\n            <div class=\"courses-cta-stats\">\n                <div class=\"courses-stat-item\">\n                    <div class=\"courses-stat-icon courses-user-icon\"><\/div>\n                    <span>Duration: 12 weeks<\/span>\n                <\/div>\n                <div class=\"courses-stat-item\">\n                    <div class=\"courses-stat-icon courses-star-icon\"><\/div>\n                    <span>Ratings: 3<\/span>\n                <\/div>\n            <\/div>\n            <a href=\"https:\/\/professional-education-gl.mit.edu\/mit-online-data-science-program\" class=\"courses-cta-button\">\n                Discover the Program\n                <div class=\"courses-arrow-icon\"><\/div>\n            <\/a>\n        <\/div>\n    <\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-does-the-applied-ai-and-data-science-program-curriculum-include\"><strong>What Does the Applied AI and Data Science Program Curriculum Include?<\/strong><\/h2>\n\n\n\n<p>MIT Professional Education\u2019s Applied AI and Data Science Program curriculum covers <strong>AI-assisted Python programming, statistical analysis and data preparation, Data Analysis and Visualization, Machine Learning, practical Data Science, Deep Learning, recommendation systems, Generative AI, and Agentic AI<\/strong>. The 15-week online program progresses from foundational concepts to advanced AI applications, with an elective project and a three-week capstone project.<\/p>\n\n\n\n<p>For Praveen Kumar Gaddam, a Staff Software Quality Engineer with 15 years of experience, this breadth was one of the notable aspects of his learning experience. He describes the curriculum as:<\/p>\n\n\n\n<p>\u201cwell structured, covering Python, Data Science, Machine Learning, Deep Learning, and Agentic AI\u2026\u201d<\/p>\n\n\n\n<p>For a professional approaching the subject from an established technology career, this provides a way to build knowledge across multiple stages of the AI and data science landscape.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-did-the-learning-experience-combine-different-formats\"><strong>How Did the Learning Experience Combine Different Formats?<\/strong><\/h2>\n\n\n\n<p>The curriculum was not limited to one type of learning. Praveen experienced a combination of <strong>live online sessions led by MIT faculty with mentor-led learning, recorded\/self-paced content, hands-on projects and case studies<\/strong>, allowing the learning process to include both independent study and interaction with mentors.<\/p>\n\n\n\n<p>This distinction is relevant for professionals evaluating an AI and data science course because the format of learning can be as important as the subjects themselves.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-do-projects-and-labs-support-practical-ai-learning\"><strong>How Do Projects and Labs Support Practical AI Learning?<\/strong><\/h2>\n\n\n\n<p>Practical labs, AI Hackathons, and capstone projects give opportunities to apply concepts from the curriculum to hands-on exercises, real-world problems, and end-to-end AI solutions. The program includes 10+ case studies and three graded projects, including a three-week capstone, with projects spanning areas such as Data Science, Machine Learning, Deep Learning, recommendation systems, Generative AI, and Agentic AI.<\/p>\n\n\n\n<p>The labs, hackathon, and capstone gave Praveen a way to put the coursework into practice.. He says:<\/p>\n\n\n\n<p>\u201cThe hands-on assignments and practical labs helped reinforce the concepts effectively.\u201d<\/p>\n\n\n\n<p>The <strong>AI Hackathon<\/strong> added a collaborative dimension to this learning experience. As Praveen puts it:<\/p>\n\n\n\n<p>\u201cThe AI Hackathon was a great opportunity to collaborate with peers and solve real-world problems in a competitive setting.\u201d<\/p>\n\n\n\n<p>The capstone provided a more sustained opportunity to bring different parts of the curriculum together.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-important-is-mentorship-during-the-learning-journey\"><strong>How Important Is Mentorship During the Learning Journey?<\/strong><\/h2>\n\n\n\n<p>Beyond the curriculum and hands-on work, access to guidance can make a difference when learners encounter challenging concepts or need help applying them. Praveen found the mentors and support team responsive throughout his experience:<\/p>\n\n\n\n<p>\u201cThe mentors and Great Learning support team were knowledgeable, responsive, and always willing to help.\u201d<\/p>\n\n\n\n<p>For a working professional navigating technical coursework alongside existing responsibilities, this kind of support can help make the learning process more manageable.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"from-learning-to-applying-ai-and-data-science\"><strong>From Learning to Applying AI and Data Science<\/strong><\/h2>\n\n\n\n<p>For Praveen, who works as a Staff Software Quality Engineer in the ServiceNow ecosystem, the value of the learning experience came from being able to connect foundational concepts with practical application. The combination of technical coursework, hands-on work, collaborative problem-solving, and a business-focused capstone helped him build greater confidence in applying AI and Data Science in a professional context.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"frequently-asked-questions\"><strong>Frequently Asked Questions<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"what-are-the-prerequisites-for-the-applied-ai-and-data-science-program\">What are the prerequisites for the Applied AI and Data Science Program?<\/h3>\n\n\n\n<p>The <a href=\"https:\/\/professional-education-gl.mit.edu\/mit-online-data-science-program\">Applied AI and Data Science Program<\/a> requires exposure to computer programming and high-school-level knowledge of statistics and mathematics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"what-tools-and-technologies-are-covered-in-the-program\">What tools and technologies are covered in the program?<\/h3>\n\n\n\n<p>The program includes tools and technologies such as Python, Google Colab, VS Code, OpenAI GPT-5, Gemini, Codex, LangGraph, RAG, and Claude, alongside AI-assisted coding and AI workflows.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"how-can-ai-and-data-science-skills-help-a-qa-engineer\">How can AI and Data Science skills help a QA Engineer?<\/h3>\n\n\n\n<p>AI and Data Science can help technology professionals expand beyond traditional testing and quality functions by developing skills in areas such as data analysis, Machine Learning, AI-assisted coding, and AI system evaluation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"why-is-understanding-the-ai-lifecycle-important-for-working-professionals\">Why is understanding the AI lifecycle important for working professionals?<\/h3>\n\n\n\n<p>Understanding the AI lifecycle helps professionals see how individual techniques connect to broader business applications. In Praveen's capstone experience, he applied the Machine Learning and Agentic AI lifecycle to a business use case.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Discover how a QA Engineer with 15 years of experience learned AI, Data Science, Machine Learning, Generative AI, and Agentic AI through MIT Professional Education's Applied AI and Data Science 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