Business decisions depend on market evidence, yet much secondary market research is unplanned, hard to verify, and now shaped by AI tools that can sound right while being wrong. This specialization gives you a repeatable workflow to plan research, find credible market intelligence, and use generative AI without lowering your standard of evidence.
Designed for marketing, insights, competitive intelligence, strategy, and business professionals, as well as founders validating a market opportunity, the program builds skills used in roles such as Market Research Analyst, Competitive Intelligence Analyst, Insights Analyst, Business Analyst, and Strategy Analyst.
You will turn a business problem into a scoped research plan, locate evidence across government, industry, company, and digital sources, evaluate source credibility and bias, and reconcile conflicting estimates through triangulation. You will then use ChatGPT, Gemini, Perplexity, and NotebookLM to speed up discovery and extraction, while verifying every claim against original sources.
Nine practice labs, along with scenario activities and role-plays, let you apply each skill to realistic business scenarios. To be successful, you should be familiar with fundamental marketing concepts and basic online research. By the end, you will be able to deliver market research that is faster, traceable, and ready to defend in front of any stakeholder.
Applied Learning Project
In Course 1, learners work through a market-entry scenario: judging whether secondary research fits the decision, framing the research problem, setting scope, and building a research framework and plan.
In Course 2, learners act as a research support analyst, assembling and defending an evidence base for a client market question by choosing sources, researching competitors, evaluating credibility and bias, and triangulating conflicting data.
In Course 3, learners complete an AI-assisted research task end to end, documenting prompts and outputs, verifying each claim against original sources, and submitting a validated write-up.
Across all three courses, practice labs build the same outputs analysts use at work: a research plan, a vetted source set, and an audited AI-assisted research record.
