Your cart is empty.
Keep shoppingData Storytelling: Turn Data into Decisions and Action [EN]
2,350 students
Data Storytelling: Turn Data into Decisions and Action [EN]
data storytelling | data analysis | business analytics | survey design | critical thinking | problem solving | insights
Last updated 8/2026
English
English [Auto],
What you'll learn
- Design a survey that produces usable data: question types, scales, sample, and the open questions worth reading
- Segment a dataset and find the insight rather than reporting the average of everything
- Run key driver analysis, calculate correlations in Excel and interpret a multiple regression without overclaiming
- Build a forecast with a trend line and design an experiment with a control group
- Frame a finding as SCQA and break the problem down with MECE so nothing important is missing
- Separate a true root cause from a plausible story using 5 Whys and an Ishikawa diagram
- Prioritise with Pareto and a decision matrix, so the recommendation has one obvious next step
- Run the meeting where the analysis becomes a decision, including when the loudest person disagrees
- Learn alongside Mike's 1.6 million students from 185 countries
- Get the author's experience from Preply, Wargaming, iDeals and Alfa-Bank
Explore related topics
Course content
14 sections • 38 lectures • 19h 15m total length
Requirements
- Basic spreadsheet skills. The statistics are run in Excel, and nothing here needs Python or SQL
- No statistics background required. Correlation and regression are built up from zero
- A real dataset or a real question, since every section ends in something applied
- English at intermediate level or above, since all lessons are in English
- Willingness to discover that one of your existing conclusions does not survive the bias section
Description
This course contains the use of artificial intelligence.
The most common fate of a good analysis is that everyone agrees with it and nothing happens.
The numbers were right. The charts were clean. And three weeks later the company is doing exactly what it was doing before.
Why correct analysis does not produce decisions
Because a decision needs something analysis alone does not provide. It needs the finding framed as a problem someone recognises as theirs. It needs a cause that has been tested rather than assumed, since the first plausible explanation is usually wrong and always comfortable. It needs one recommendation rather than nine observations. And it needs to be told to a specific audience in the form that audience can act on, which is almost never the form you did the analysis in.
Analysts learn the first half of this job in training. The second half they usually learn by watching their work get ignored.
How the course runs
Thirty-eight lessons following one arc from data to decision. It starts before the data exists: survey design, question types, scales, the response rate problem, and the report and action plan at the end of a survey cycle.
Then the analysis itself. Segmentation, lifetime value and ROI, journey and funnel analytics, attrition analysis and the mistakes almost everyone makes measuring it, key driver analysis, correlations in Excel, multiple regression with R-square and variance, forecasting with trend lines, and experiments with a proper control group.
Then six lessons on critical thinking, which is where you find out whether your conclusion survives contact with a determined sceptic. Deduction and induction, weak arguments, confirmation bias, anchoring, framing, fact against opinion, and decision models including OODA and Cynefin.
Turning the finding into a decision
Then ten lessons of structured problem solving, and this is the part that changes how your work lands. SCQA for framing. MECE and logic trees for breaking it down. 5 Whys and Ishikawa for the cause. Pareto and the decision matrix for prioritisation. TRIZ, SCAMPER and Six Thinking Hats for generating options. And a script for the session where a group turns your analysis into a choice, including how to handle the person who talks over everyone.
The last six lessons are delivery: audience segmentation, personas, message templates, channel choice, communicating findings nobody wants, and measuring whether the message landed.
Who is teaching this
Mike, the number one HR instructor on Udemy. More than 1.6 million course enrolments, over 150,000 professionals trained, PHRi and SHRM-CP certified, HRCI representative in more than 10 countries. I built the people function of the unicorn Preply and worked at Wargaming, Alfa-Bank and iDeals. The analytics examples run on workforce data, because it is the messiest data in most companies and the methods transfer to anything cleaner.
What is included
Lifetime access to all course materials
Active instructor support in the Q&A section
Udemy Certificate of Completion
Practical assignments and real business cases
A section with additional courses, tools and resources
How to take it
Bring a real question you have been asked to answer, and work it through the course section by section rather than watching it end to end first. The methods only stick when they are applied to data you care about. Enrol now and start the first lesson today.
Who this course is for:
- Analysts whose reports are read, agreed with and then not acted on
- Managers who receive dashboards and cannot tell which number should change their plan
- HR and people analytics specialists building the analytical function from scratch
- Business analysts and consultants who need the structuring layer between data and recommendation
- Anyone who has to present findings to people who did not do the analysis
- Founders and functional leads making decisions on data they have never checked
- People moving into an analytics role from an operational one