Market Research Analyst: AI, Power BI, SurveyMonkey skilled

This course combines qualitative and quantitative primary research methods used in modern market research studies. You will develop end-to-end primary research capabilities—designing qualitative and quantitative instruments, and running moderated interviews and surveys to produce defensible insights that inform product and marketing decisions. This course teaches you how to draft professional discussion guides, moderator scripts, and complex branching surveys. You will master advanced methodologies like conjoint experiments and learn to select sampling strategies—both probability and non-probability—with appropriate statistical power to ensure data validity. Operational labs cover survey platform workflows, including automations, data exports, skip-logic, and response validation. Responsible AI use-cases are deeply embedded: you will practice using LLM-assisted moderator prompts and transcript synthesis while applying hallucination checks and ethical guardrails. Furthermore, you will apply GDPR-compliant consent templates and data-handling procedures to ensure your studies respect privacy and meet international legal requirements. By building comprehensive study plans and conducting pilot fieldwork, you will learn to move seamlessly from instrument design to robust statistical analysis and stakeholder-ready interpretation. You will conclude the course by completing a Survey Design and Execution Lab, delivering a portfolio-ready project that showcases your ability to manage the entire primary research lifecycle. Who this is for: Aspiring market researchers, business analysts, product managers, and professionals looking to develop advanced research skills with no prior experience required.

Technical proficiency is only half of the hiring equation. To land the role, you must be able to market your expertise effectively. This course helps you extract and package capstone and project outputs into professional portfolio pages and high-impact resume bullets. You will craft role-focused LinkedIn summaries and concise interview narratives that highlight your ability to use AI, R, and Power BI to drive business value. You will study 'day-in-the-life' exemplars and job-seeking stories from professionals with non-traditional backgrounds to build confidence and set realistic industry expectations. The curriculum provides comprehensive guidance on certification and credit-bearing pathways, including lists of key industry associations and networking groups to accelerate your professional growth. You will master templates for personalized outreach messages, job applications, and strategic follow-ups that set you apart in a competitive market. By the end of this course, you will have a complete 'Career Readiness Kit,' featuring a conversion checklist for turning academic projects into resume-ready artifacts, a one-page professional portfolio, and a prioritized job-search plan designed to secure internships, freelance gigs, or entry-level analyst roles. This final module ensures that your technical proficiency is matched by your ability to market yourself effectively to hiring managers and recruiters in the global research landscape.

You will learn practical approaches to rapidly assemble credible competitive intelligence and to forecast market change in ways stakeholders can act on. This course takes you through classic and adapted frameworks—such as Porter's Five Forces, feature-gap analyses, and risk-scoring matrices—along with pragmatic workflows for evidence discovery and verification. You'll practice advanced search operators, source prioritization based on a credibility hierarchy, and structured note-taking so that public filings, product specs, and earnings transcripts become usable intelligence. The curriculum emphasizes sophisticated methods for quantifying competitor moves, scoring their likely impacts, and visualizing market positioning with clear heatmaps and comparison charts. You will master the art of "Source Auditing" to ensure every forecast is backed by high-quality data. By completing short, time-boxed intelligence briefs and presentations, you'll build the habit of delivering concise, prioritized recommendations that product and strategy teams can operationalize while preserving full auditability of sources and assumptions. This course transforms you into a proactive analyst who can identify emerging marketing shifts and assess their potential implications. You will conclude the module by producing a comprehensive Market Intelligence Brief, a portfolio-ready document that demonstrates your ability to turn raw noise into a competitive advantage. Who this is for: Aspiring market researchers, business strategists, product managers, and professionals looking to develop competitive intelligence skills with no prior experience required.

You will learn to convert raw customer and survey data into actionable segments tied directly to revenue and growth levers. This course introduces sophisticated persona-building, cross-tab and pivot analyses, and RFM (Recency, Frequency, and Monetary) segmentation so you can identify high-value cohorts and prioritize activation tactics. You'll practice extracting segments from both transactional and survey datasets, applying rigorous scoring and labeling conventions, and estimating incremental revenue from small-scale pilots. The curriculum focuses on translating these segmentation results into operational recommendations—including tailored retention, upsell, or reactivation campaigns—complete with simple lift estimates and implementation notes. You will prove that you can translate analytical outputs into actionable strategies and produce portfolio-ready documents showing that you can connect data to the bottom line. By producing concise activation briefs and testing plans, you will learn to bridge the gap between analytic segmentation outputs and marketing or product execution, ensuring research recommendations can be implemented and measured for success. You will conclude this course with a high-impact project: executing a full RFM segmentation to calculate the profitability lift of specific customer segments. This process ensures you can move beyond simple data grouping to deliver evidence-based strategies that drive measurable business growth and customer lifetime value. This course provides the portfolio-ready artifacts necessary to demonstrate your ability to link data-driven insights directly to a company's bottom line.

You will gain the applied data skills needed to turn multiple evidence streams into defensible forecasts and prioritized market recommendations. This course covers data assembly, cleaning, and harmonization for time-series and cross-sectional indicators, followed by practical forecasting techniques such as moving averages, exponential smoothing, and estimation of forecast uncertainty and confidence ranges. You'll learn to calculate critical market sizing metrics—including TAM, SAM, and SOM—while producing year-over-year growth metrics to identify high-velocity opportunities. The curriculum teaches you to create straightforward scorecards that prioritize these opportunities by size, growth velocity, and strategic fit. Emphasis is placed on communicating forecast confidence and producing visualizations that highlight seasonality, inflection points, and the distinction between long-term trends and short-term fads. Through hands-on model-building and interpretation exercises, you will practice converting complex analytic outputs into definitive go/no-go recommendations and short executive briefings that balance quantitative evidence with seasoned business judgment. You will finish this course by delivering a comprehensive Trend Forecast and Market Entry Recommendation, complete with a scoring model and supporting data visualizations, ensuring your insights are both statistically sound and ready for stakeholder review. Who this is for: Aspiring market analysts, business strategists, entrepreneurs, and professionals looking to develop data-driven market analysis skills with no prior experience required.

You will develop reproducible analytics practices using R, paired with governance controls that make research outputs auditable and reliable for stakeholders. The course begins with file management and naming conventions, metadata tagging, and data-quality KPI monitoring to ensure high data integrity standards. It then introduces core R skills for data import, tidy transformations, and pipe-based workflows to join, filter, and aggregate multi-source datasets using the Tidyverse ecosystem. You will learn to author parameterized R Markdown reports to automate regular reporting and to perform diagnostic tests—such as cross-validation and resampling—to evaluate the robustness of regression and predictive modeling techniques commonly used in market research. The curriculum embeds responsible LLM summarization of qualitative data and synthetic-data evaluation use-cases, teaching you how to detect and mitigate hallucination and bias in automated outputs. Labs focus on building end-to-end analytic pipelines that produce reproducible deliverables, paired with rigorous checks that validate metrics against source data to ensure trustworthy results. You will conclude the course by creating a portfolio-ready Data Pipeline and Model Validation Lab, demonstrating your ability to manage the entire data lifecycle from raw ingestion to predictive modeling and executive-ready automated reporting.

You will master practical visualization skills, professional dashboard-building practices, and the persuasive storytelling techniques needed to turn complex analytics into decision-ready products for stakeholders. This combined course covers rigorous chart selection logic, visual design principles (color theory, purposeful annotation, and layout hierarchy), and hands-on Power BI techniques—including Power Query for blending multi-source data and DAX for complex calculations. You will learn to map analytic questions to effective visual encodings, test dashboard usability with simple heuristics, and iterate designs based on stakeholder feedback. The course also bridges the gap between data and action by teaching you to automate report snapshots and validate metrics against source data, ensuring your narratives remain accurate and repeatable. Labs guide you through importing survey and transactional data, building drill-through pages for deep-dive analysis, and producing usability reports. You will conclude the course by producing a portfolio-ready Power BI dashboard and an executive "Strategy Deck," demonstrating your ability to convert raw, complex datasets into intuitive visual narratives that drive organizational decision-making.

In the real world, data projects fail when they aren't grounded in business value. This course teaches you to lead from day one by mastering the process of translating vague business questions into actionable research plans. You will learn how to frame research questions, conduct stakeholder interviews, and capture project requirements using a structured intake template. The course introduces strategic frameworks such as the 4 Ps, SWOT, and the Ansoff Matrix to help connect research insights to real business growth opportunities. You will also evaluate the cost-benefit of different research approaches and estimate potential research ROI, ensuring that each project you lead is viewed by leadership as a worthwhile investment. You will align on and prepare concise measurement plans and executive summaries that connect research outcomes to business KPIs, so insights are directly actionable for leadership. This foundational stage ensures your research is grounded in business value before moving into advanced data tools. By working through templates, case activities, and short feedback loops, you will gain confidence in scoping projects that balance rigor, feasibility, and business value. You will conclude this course with a professional project intake portfolio that demonstrates your ability to lead the research process from the first stakeholder meeting to the final strategic recommendation. Who this is for: Aspiring market researchers, business analysts, marketing professionals, and anyone looking to develop strategic research skills with no prior experience required.