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Certified Market Research, Business Insight & Data Science

Turn data into business decisions with our Certified Market Research, Business Insight & Data Science program. Learn Market Research, Power BI, Business Analytics, AI-powered Insights, and Forecasting through real business data, corporate case studies, and hands-on projects. Led by an industry expert, this 12-week executive program prepares you for high-demand careers in research, business intelligence, and data analytics.

Level

Beginner

Duration

48 Hours

Instructor

Mahafuzr Rahman

Course Fee

৳20000

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Certified Market Research, Business Insight & Data Science

12

Modules

24

Lessons

48h

Hours

Fee

৳20000

Learning Path

Complete Course Curriculum

12 modules and 24 lessons designed for practical, job-ready learning.

  • What market research really is, and why it drives corporate decisions
  • Consumer insight defined: the non-obvious “why” behind customer behavior that points to action — not a data point or a finding.
  • The insight ladder (Data → Information → Finding → Insight → Action) and the Fresh–RelevantActionable–Evidenced test of a strong insight.
  • An honest look at the market research ecosystem in Bangladesh: FMCG, telecom, banking, pharma, ecommerce
  • Qualitative research — understanding the “why” behind behavior.
  • Quantitative research — measuring the “how much” and “how many.”
  • Mixed-method research — how strong corporate studies blend both.
  • When to choose which approach, with real Bangladeshi business use-cases.
  • The end-to-end research process, step by step.
  • Defining the business problem before the research question.
  • Writing sharp, measurable research objectives.
  • The three classic designs — exploratory, descriptive and causal — and matching design to business need.
  • Primary research: fresh, first-hand data you collect yourself — surveys, interviews, focus groups, observation — tailored to your exact question.
  • Secondary research: existing data collected earlier by others — syndicated reports, public and industry sources and internal records.
  • Basics of competitor and market intelligence; avoiding the common trap of “googling” instead of structured research.
  • Choosing the right mix: when primary collection is worth the cost, and when secondary data is enough.
  • Population, sampling frame and the real-world gap between them
  • Probability methods: simple random, systematic, stratified, cluster.
  • Non-probability methods: convenience, quota, purposive, snowball, judgmental.
  • A practical, non-intimidating approach to sample size; where sampling goes wrong in the field.
  • Measurement and scaling fundamentals.
  • Likert, rating, ranking and semantic-differential scales.
  • CSAT (Customer Satisfaction): the share of customers choosing the top ratings (e.g., 4–5 on a 5-point scale), calculated as satisfied responses ÷ total responses × 100.
  • NPS (Net Promoter Score): from the 0–10 “how likely to recommend” question; NPS = % Promoters (910) − % Detractors (0–6), ranging from −100 to +100.
  • Question wording, order effects and the discipline of removing bias.
  • Designing surveys that respondents actually complete.
  • Focus group discussions, in-depth interviews and observation.
  • Moderation and probing skills.
  • Introduction to thematic analysis and coding of qualitative data
  • Turning qualitative findings into a sharp consumer-insight statement.
  • Hands-on build: each participant drafts a complete research plan.
  • Design of a survey instrument for a real business question.
  • Peer review and trainer feedback.
  • Month 1 deliverable submitted.
  • The reality that 70–80% of analysis is data cleaning.
  • Structuring messy data; validation, deduplication, handling missing values.
  • Excel for analysts: lookups, logical functions and PivotTables.
  • Google Sheets for collaborative analysis.
  • Connecting and consolidating data from multiple sources.
  • Preparing a clean dataset that is ready to feed a dashboard.
  • Descriptive analytics that managers trust.
  • Cross-tabulation and correlation.
  • A plain-language introduction to inference — significance and confidence — to read results responsibly.
  • Demographic, behavioral and value-based segmentation.
  • The RFM (Recency–Frequency–Monetary) model.
  • Cohort thinking for retention analysis.
  • Choosing the right chart for the right message.
  • Storytelling with data.
  • The design language of a credible corporate dashboard.
  • Connecting data and building the data model
  • Producing a first interactive dashboard.
  • Tool orientation and navigation.
  • Introductory DAX measures.
  • Filters, slicers, drill-downs and KPI cards.
  • Tracking customer metrics such as CSAT and NPS as live dashboard KPIs.
  • Interactivity and report layout.
  • Hands-on build: each participant develops a working corporate dashboard.
  • Built from their own dataset prepared in Week 5.
  • Trainer review and refinement.
  • Month 2 deliverable submitted.
  • Why Python matters for the modern analyst.
  • Friction-free setup using Google Colab / Jupyter (no installation needed).
  • Pandas basics: loading, exploring and filtering data.
  • Grouping and aggregation.
  • First visualizations with matplotlib / seaborn.
  • Connecting Python analysis back to the business question.
  • Forecasting explained in business terms.
  • Understanding trend and seasonality.
  • Simple, defensible methods: moving average, exponential smoothing; an introduction to regression.
  • Predictive-modelling concepts at a conceptual level.
  • Clustering for segmentation.
  • Evaluating a model honestly and communicating its limits to leadership.
  • Using AI to summaries reports and synthesize open-ended survey responses.
  • Effective prompting for research and analysis.
  • Responsible AI: verifying outputs, labelling estimates and never presenting fabricated figures as fact.
  • The analytics ladder: descriptive (what happened) → diagnostic (why it happened) → predictive (what is likely next) → prescriptive (what to do about it).
  • Mapping the ladder to this course: dashboards and segmentation cover descriptive and diagnostic; forecasting covers predictive; data-backed recommendations cover prescriptive.
  • Decision Intelligence: bringing data, analytics and human judgement together to make and act on better decisions — the layer above analytics.
  • An introduction to emerging agentic AI workflows, and a forward look at where the corporate insight function is heading.
  • Structured working session on each participant’s project.
  • One-to-one mentor feedback.
  • Final refinement of dashboard and insight narrative.
  • Delivery of the corporate dashboard.
  • Delivery of the data-backed insight presentation before a panel.
  • Final evaluation and program certification.

Who Should Join

This Course Is For You

Audience information is being updated.

Instructor

Learn From Mahafuzr Rahman

Mahafuzr Rahman
Lead Instructor

Mahafuzr Rahman

Experienced instructor with deep expertise in technology education. Dedicated to helping students master modern tech skills and build production-ready applications.

Expert Mentor Industry Professional

Prerequisites

Before You Start

No prior experience required. Perfect for beginners.

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