Sunflower Intelligence
Business Analytics & Data Science

B01 · Introductory

Introduction to Business Analytics

9 lessons

B01 · Introductory · 9 lessons in three modules · about 11.5 hours

Introduction to Business Analytics

The four kinds of analytics, turning business questions into measurable ones, and reading data sensibly.

What you will learn

  • Distinguish descriptive, diagnostic, predictive and prescriptive analytics and say which a business question needs.
  • Turn a vague business question into measurable objectives, hypotheses and the data needed to test them, using a structured problem-solving approach.
  • Classify data by structure, type and level of measurement, and choose a sensible way to collect data for a question, recognising sampling, non-response, survivorship and measurement bias.
  • Assess a dataset against the dimensions of data quality and say how each problem would distort an answer.
  • Define KPIs with an exact formula, a target, a direction and an owner, and calculate rates, ratios, growth and percentage-point changes correctly.
  • Summarise and interpret data with averages, spread and charts, diagnose a change by comparing like with like, and avoid common traps such as confusing correlation with causation, misleading charts and mixing groups.

Prerequisites

None. The course starts from scratch.

How it works

Each lesson teaches the idea from first principles, works examples step by step, lets you explore it in an interactive, then has you practise with feedback. A lesson is complete when you have worked its guided problems and scored 80% on its knowledge check. Mastery is tracked separately and comes back for review before you are likely to forget. Each module ends with a test; the course ends with a project and a final assessment.

Fictional case

Lantern Coffee Co. A fictional coffee business founded by Ana Lim. It starts as a catering and barista-training service, opens its own cafés and an online shop for beans, and plans a canned cold brew. Every name and figure in it is invented for teaching.

Not sure where to start?

The diagnostic has 12 questions across the course. It doesn’t count toward anything; it suggests the module to start from. Take the diagnostic.

Modules and lessons

Module 1 What analytics is for

  1. The four kinds of analyticsAnalytics turns data into better decisions. Learn the four questions it answers (what happened, why, what will happen and what should we do), what each needs and produces, where machine learning fits, and the five-step process that runs from a business question to a measured result.80 min · not started
  2. Business questions and measurable objectivesMost analyses that fail, fail at the first step: the question. Learn to turn a vague worry into a decision-focused question, a SMART objective and testable hypotheses, and to pin down exactly what counts, for whom, over what period and compared with what, before you open any data.75 min · not started
  3. Problem-solving frameworksStructure turns a big, messy problem into small questions that data can answer. Learn issue trees and the MECE test, driver trees that break revenue into visits, conversion and order value, root-cause tools such as the five whys and their limits, the CRISP-DM process, and how to tell when you have enough evidence to stop.80 min · not started
  4. Module 1 test11 questions on this module. 70% passes.not taken

Module 2 Data and where it comes from

  1. Types of dataBefore you can analyse data you have to say what kind it is: a table or free text, what one row describes, which columns are categories and which are amounts, how the data runs through time and who collected it. Each answer limits what you can sensibly calculate.70 min · not started
  2. Collecting data: sources, samples and biasHow data was collected decides what it can show. Learn the main sources of business data, how a sample stands in for a population, the four biases that most often mislead (sampling, non-response, survivorship and measurement), how to word a survey question, and the basics of consent and privacy.75 min · not started
  3. Data quality: is the data fit for the question?Data can be wrong in different ways, and each way distorts a different number. Learn the six dimensions of data quality, how duplicates, gaps, invalid values, inconsistent codes and time zones move a metric, how to judge whether data is good enough for a purpose, and who is responsible for its quality. B02 then teaches the repairs.70 min · not started
  4. Module 2 test11 questions on this module. 70% passes.not taken

Module 3 Measuring and reading results

  1. KPIs: measuring what mattersA key performance indicator is a metric with a job: an exact formula, a target, a direction and an owner. Learn to define an online shop’s KPIs precisely, to tell counts from rates and leading from lagging indicators, and to build a balanced set of measures that is hard to game.75 min · not started
  2. Summarising data: changes, averages and spreadMost business numbers are summaries: a share, a change, an average, a growth rate. Learn to calculate each one correctly, to say percentage points when you mean them, to choose between the mean and the median, to describe spread, to weight an average properly and to round without misleading.70 min · not started
  3. Interpreting results: charts, comparisons and causesNumbers don’t interpret themselves. Learn to read a chart critically, to compare like with like, to tell correlation from causation, to spot Simpson’s paradox and regression to the mean, to diagnose why a KPI moved, and to write a finding that says how sure you are.80 min · not started
  4. Module 3 test12 questions on this module. 70% passes.not taken

Project and final assessment

  1. Project: Lantern’s online-shop reviewReview six months of Lantern’s online shop: frame Ana’s questions, choose and calculate KPIs, find out why September’s orders fell, judge the evidence and recommend what to do and measure next.not taken
  2. Final assessment25 questions across the whole course. 70% passes.not taken

The course is complete when every lesson is complete, every module test is passed, the project is finished and the final assessment is passed.

Resources

  • Glossary: every term this course defines.
  • Formulas: every formula with its symbols and units.
  • Review: flashcards and the questions you missed, when they are due.

Sources used in this course

  1. Boyd, A. (2012, 6 August). Revisiting “what is analytics”. Analytics magazine. INFORMS. pubsonline.informs.org/do/10.1287/LYTX.2012.04.09/full
  2. INFORMS. “Operations Research & Analytics” (descriptive, predictive and prescriptive analytics). informs.org/About-INFORMS/What-is-Analytics
  3. TDWI. “What Is Diagnostic Analytics?” TDWI Glossary. tdwi.org/pages/glossary/diagnostic-analytics.aspx
  4. eSchool Media Contributors (2021, 14 May). “3 questions to ask when embracing an analytics model”. eCampus News. ecampusnews.com/campus-leadership/2021/05/14/3-questions-to-…
  5. Ault, S. V., Liao, S. N. & Musolino, L. (2025). Principles of Data Science, section 1.1, “What Is Data Science?” OpenStax. openstax.org/books/principles-data-science/pages/1-1-what-is…
  6. Centers for Disease Control and Prevention. “Developing Objectives and Evaluation Questions”. Information for Tuberculosis Programs. cdc.gov/tb-programs/php/evaluation/objectives.html
  7. Farnsworth, D., Clark, J. L., Cothran, H. & Wysocki, A. (2020). Developing SMART Goals for Your Organization (FE577). University of Florida IFAS Extension. ask.ifas.ufl.edu/publication/FE577
  8. Haughey, D. “A Brief History of SMART Goals”. Project Smart. projectsmart.co.uk/brief-history-of-smart-goals.php
  9. US Bureau of Labor Statistics. “Concepts and Definitions (CPS)”. Labor Force Statistics from the Current Population Survey. bls.gov/cps/definitions.htm
  10. Google. “About Analytics sessions”. Analytics Help. support.google.com/analytics/answer/9191807
  11. Illowsky, B. & Dean, S. (2023). Introductory Statistics 2e, section 9.1, “Null and Alternative Hypotheses”. OpenStax. openstax.org/books/introductory-statistics-2e/pages/9-1-null…
  12. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. SPSS. s2.smu.edu/tfomby/eco5385_eco6380/data/SPSS/CRISPWP-0800%20D…
  13. Card, A. J. (2017). The problem with ‘5 whys’. BMJ Quality & Safety, 26(8), 671–677. doi:10.1136/bmjqs-2016-005849. qualitysafety.bmj.com/content/26/8/671
  14. NIST/SEMATECH. e-Handbook of Statistical Methods, section 1.1.2, “How Does Exploratory Data Analysis differ from Classical Data Analysis?” itl.nist.gov/div898/handbook/eda/section1/eda12.htm
  15. McKinsey & Company. “Barbara Minto: ‘MECE: I invented it, so I get to say how to pronounce it’”. McKinsey Alumni News. mckinsey.com/alumni/news-and-events/global-news/alumni-news/…
  16. Illowsky, B. & Dean, S. (2023). Introductory Statistics 2e, sections 1.2 “Data, Sampling, and Variation in Data and Sampling” and 1.3 “Frequency, Frequency Tables, and Levels of Measurement”. OpenStax. openstax.org/books/introductory-statistics-2e/pages/1-3-freq…
  17. Ault, S. V., Nam Liao, S. & Musolino, L. (2025). Principles of Data Science, section 1.3, “Data and Datasets”. OpenStax. openstax.org/books/principles-data-science/pages/1-3-data-an…
  18. Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677–680. doi.org/10.1126/science.103.2684.677
  19. Velleman, P. F. & Wilkinson, L. (1993). Nominal, ordinal, interval, and ratio typologies are misleading. The American Statistician, 47(1), 65–72.
  20. US Bureau of Labor Statistics. Handbook of Methods: “National Longitudinal Surveys: Data sources”. bls.gov/opub/hom/nls/data.htm
  21. Illowsky, B. & Dean, S. (2023). Introductory Statistics 2e, sections 1.1 “Definitions of Statistics, Probability, and Key Terms” and 1.2 “Data, Sampling, and Variation in Data and Sampling”. OpenStax. openstax.org/books/introductory-statistics-2e/pages/1-2-data…
  22. Pew Research Center. “Writing Survey Questions”. Methods. pewresearch.org/writing-survey-questions
  23. Google. “[GA4] Session”. Analytics Help. support.google.com/analytics/answer/12798876?hl=en
  24. Information Commissioner’s Office. “What is personal data?” UK GDPR guidance and resources. ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/…
  25. Information Commissioner’s Office. “A guide to lawful basis”. UK GDPR guidance and resources. ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/…
  26. Information Commissioner’s Office. “The Data (Use and Access) Act 2025 (DUAA): summary of the changes: Data protection”. ico.org.uk/about-the-ico/what-we-do/legislation-we-cover/dat…
  27. European Commission. “Legal grounds for processing data”. Data protection: information for business and organisations. commission.europa.eu/law/law-topic/data-protection/informati…
  28. Information Commissioner’s Office. “What is valid consent?” UK GDPR guidance and resources. ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/…
  29. DAMA UK Working Group on “Data Quality Dimensions” (2013). The Six Primary Dimensions for Data Quality Assessment. White paper, October 2013.
  30. Government Data Quality Hub (2020). The Government Data Quality Framework. GOV.UK, published 3 December 2020. gov.uk/government/publications/the-government-data-quality-f…
  31. Google. “[GA4] Compare Analytics reports and data exported to BigQuery”. Analytics Help. support.google.com/analytics/answer/13578783?hl=en
  32. Google. “Conversions vs. key events in Google Analytics”. Analytics Help. support.google.com/analytics/answer/13965727
  33. Kaplan, R. S. & Norton, D. P. (1992). The Balanced Scorecard: Measures that Drive Performance. Harvard Business Review, January–February 1992. hbr.org/1992/01/the-balanced-scorecard-measures-that-drive-p…
  34. Manheim, D. & Garrabrant, S. (2018). Categorizing Variants of Goodhart’s Law. arXiv:1803.04585. (Quotes Goodhart, C. A. E. (1975), Problems of Monetary Management: The U.K. Experience, Papers in Monetary Economics, Reserve Bank of Australia.) arxiv.org/abs/1803.04585
  35. Strathern, M. (1997). “Improving ratings”: audit in the British University system. European Review, 5(3), 305–321.
  36. Illowsky, B. & Dean, S. (2023). Introductory Statistics 2e, section 2.5, “Measures of the Center of the Data”. OpenStax. openstax.org/books/introductory-statistics-2e/pages/2-5-meas…
  37. Illowsky, B. & Dean, S. (2023). Introductory Statistics 2e, section 2.7, “Measures of the Spread of the Data”. OpenStax. openstax.org/books/introductory-statistics-2e/pages/2-7-meas…
  38. NIST/SEMATECH. e-Handbook of Statistical Methods, section 1.3.5.1, “Measures of Location”. itl.nist.gov/div898/handbook/eda/section3/eda351.htm
  39. NIST/SEMATECH. e-Handbook of Statistical Methods, section 1.3.5.6, “Measures of Scale”. itl.nist.gov/div898/handbook/eda/section3/eda356.htm
  40. U.S. Bureau of Labor Statistics. “Calculating percent changes”. Consumer Price Index fact sheet, last modified 9 February 2023. bls.gov/cpi/factsheets/calculating-percent-changes.htm
  41. Illowsky, B. & Dean, S. (2023). Introductory Statistics 2e, section 12.3, “The Regression Equation” (correlation and causation). OpenStax. openstax.org/books/introductory-statistics-2e/pages/12-3-the…
  42. Weinberger, N. Simpson’s paradox. The Stanford Encyclopedia of Philosophy, first published 24 March 2021, substantive revision 6 June 2026. plato.stanford.edu/entries/paradox-simpson
  43. Bickel, P. J., Hammel, E. A. & O’Connell, J. W. (1975). Sex bias in graduate admissions: data from Berkeley. Science, 187(4175), 398–404. doi.org/10.1126/science.187.4175.398
  44. Barnett, A. G., van der Pols, J. C. & Dobson, A. J. (2005). Regression to the mean: what it is and how to deal with it. International Journal of Epidemiology, 34(1), 215–220. academic.oup.com/ije/article/34/1/215/638499
  45. U.S. Census Bureau. “Quarterly Services: Seasonal Adjustment FAQs”. census.gov/services/qss/seasonally_adjusted_faqs.html
  46. Mercer, A. (2016). 5 key things to know about the margin of error in election polls. Pew Research Center, 8 September 2016. pewresearch.org/short-reads/2016/09/08/understanding-the-mar…