{"id":120127,"date":"2026-10-26T16:06:12","date_gmt":"2026-10-26T10:36:12","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/no-code-ai-for-economists-seyni-da\/"},"modified":"2026-10-27T15:25:37","modified_gmt":"2026-10-27T09:55:37","slug":"no-code-ai-for-economists-seyni-da","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/no-code-ai-for-economists-seyni-da\/","title":{"rendered":"\"I Wanted to Build Practical AI Skills Without Coding\": An Economist\u2019s No-Code AI Learning Journey"},"content":{"rendered":"\n

Artificial intelligence (AI) is creating new opportunities for data-driven problem-solving, but having access to AI tools does not automatically mean professionals know how to use them effectively. The OECD\u2019s 2026 report<\/a>, AI and Skills, found that around 40% of employers in manufacturing and finance that had not yet adopted AI cited skills shortages as a main barrier to adoption. For professionals outside traditional technology roles, learning to work with AI can be an opportunity to build on existing analytical strengths without necessarily starting with advanced programming.<\/p>\n\n\n\n

Seyni Da<\/a>, a trained economist from Washington, DC, took this approach through MIT Professional Education's No Code and Agentic AI program<\/a>. Motivated to strengthen his quantitative analysis and modeling skills<\/a>, he explored tools such as KNIME<\/a> and RapidMiner<\/a> to build practical AI skills without coding.<\/p>\n\n\n\n

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<\/div>\n Unlock AI Without Coding<\/span>\n <\/div>\n

\n No Code and Agentic AI<\/a>\n <\/p>\n

Build AI and machine learning tools without any coding. Made for professionals ready to lead in the AI-driven world.<\/p>\n

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<\/div>\n Duration: 12 weeks<\/span>\n <\/div>\n
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<\/div>\n 3+ Hands on Projects<\/span>\n <\/div>\n <\/div>\n \n Take your First Step\n
<\/div>\n <\/a>\n <\/div>\n <\/div>\n\n\n\n

Why Did an Economist Decide to Learn AI?<\/strong><\/h2>\n\n\n\n

Economics involves interpreting data, identifying patterns, and using quantitative methods to understand complex problems. AI and Machine Learning can extend these analytical skills by helping professionals process large datasets, make predictions, and explore data-driven solutions.<\/p>\n\n\n\n

For Seyni, the motivation was to strengthen his existing analytical capabilities with new tools. As he explains, \u201cI wanted to upskill my quantitative analysis and modeling skills with AI and Machine Learning tools.\u201d<\/strong><\/p>\n\n\n\n

His goal was to connect his quantitative background with practical AI applications. For professionals in economics, finance, research, and business analysis, no-code AI for economists offers a way to explore data analysis and machine learning tools while building on existing domain knowledge.<\/p>\n\n\n\n

How Can No-Code AI Help Non-Technical Professionals Learn Machine Learning?<\/strong><\/h2>\n\n\n\n

Machine Learning can seem challenging for professionals with limited programming experience. No-code tools offer an alternative starting point by allowing users to explore selected Data Science and modeling workflows through visual interfaces.<\/p>\n\n\n\n

Seyni chose the program specifically to develop practical skills without writing code. In his words, he enrolled \u201cto build practical AI skills without coding.\u201d<\/strong><\/p>\n\n\n\n

The program introduced him to Data Science concepts, AI and Machine Learning tools, and modeling. His testimonial specifically names KNIME and RapidMiner<\/strong>, platforms used for visual data analytics and Machine Learning workflows.<\/p>\n\n\n\n

Such tools can help learners explore how data is prepared, how models are developed, and how analytical results can be interpreted. They can also make it easier to focus on understanding a problem and the logic behind a workflow before progressing to more technically demanding tasks.<\/p>\n\n\n\n

No-code learning does not eliminate the need for technical knowledge in every AI application. However, it can provide a practical entry point for professionals who want to understand Machine Learning<\/a> concepts and experiment with selected tools before pursuing deeper programming skills.<\/p>\n\n\n\n

What Did Seyni Learn Through the Program?<\/strong><\/h2>\n\n\n\n

Seyni\u2019s learning experience focused on understanding Data Science, AI tools, and modeling through training sessions that connected concepts with real-world applications. He describes the program as offering \u201ca great understanding of data science dimensions, AI and Machine Learning tools and modeling.\u201d<\/strong><\/p>\n\n\n\n

The curriculum connects data science concepts with practical modeling techniques. Key areas include:<\/p>\n\n\n\n