Understand averages, trends, and sample sizes well enough to ask the right questions.
Every week you are handed numbers and asked to act on them. A conversion rate moved. A survey says customers are happy. A dashboard is flashing red. Nobody expects you to run the analysis yourself, but everybody expects you to know whether the number deserves a decision.
Not a statistics course. There is no algebra, no notation, and nothing to memorise.
Not a tools course. You will not open a spreadsheet or write a query.
A course about judgement. It covers the handful of ways numbers routinely mislead capable people, and the questions that expose each one before it costs you.
Meet the failure mode in a real example, drawn from ordinary reporting.
See why it fools people who are paying attention.
Practise on a case where the obvious reading is the wrong one.
The practice matters more than the explanation. The learners who apply each section to a report they already receive finish able to challenge their own reporting; the ones who only watch finish able to describe the ideas.
You are not training to produce the analysis. You are training to know when to trust it.
By the end you will read a report and say precisely which part you believe, which part you do not, and what evidence would change your mind. You will know when a movement is real, when a sample is too small, when a chart is arguing rather than reporting, and when two things moving together prove nothing at all.
This course is the middle step of the Data-Informed Manager specialization, which continues into modelling a decision and reporting it so a team acts on it.
10 sections37 lessons3.5 total hours
Learn from practitioners who bring their real-world experience into every lesson.
Analytics Lead and Decision Intelligence Advisor
Amara has spent a decade building the reporting that commercial and operations teams run on, first as an analyst and now as the leader of a decision-intelligence team. She teaches the way she reviews her own dashboards: start from the decision, test the assumptions behind the numbers, and cut anything that does not help someone choose the next action.
LinkedInHead of Data Science
Dinesh spent eight years as a research statistician before moving into industry and now leads a data science team supporting commercial and operations decisions. He specializes in experiment design, forecasting, and communicating uncertainty to non-technical leaders. He is happiest showing why a confident-looking number should not be trusted yet, then building the evidence needed to use it responsibly.
Managers who act on reports
You approve budgets and change direction based on dashboards you did not build, and you want to know when to trust them.
Founders and operators
You have more metrics than time, and you need to tell the numbers that matter from the ones that merely move.
Anyone working beside a data team
You want to ask sharper questions in review meetings without pretending to be an analyst.
Lifetime access
Come back to the lessons whenever you need them, including future updates to this curriculum.
Learn at your own pace
Every lesson works on desktop, tablet, and phone, and picks up where you left off.
Certificate of completion
Finish every lesson and claim a shareable certificate from My Learning.
Secure card payment
Pay in Sri Lankan rupees through PayHere. Your card details never reach us.
Ishara W.
Buy the whole path in one purchase instead of a course at a time.
Read the numbers, model a decision, and report it so the team acts on it.
3 courses45 lessons3.5 total hoursRs 34,000.00
Rs 42,000.00 bought separately — save Rs 8,000.00
I sit in a weekly numbers review and used to nod along. Two weeks in I asked what our baseline variation was and the room went quiet. Nobody had checked. That question alone was worth the price.
Tharindu B.
Exactly what it says on the tin. No formulas, no theory for its own sake, just the specific ways numbers go wrong and how to catch them. The section on averages of averages fixed a reporting bug we had shipped months ago.
Menaka R.
Very strong on judgement and questions. I would have liked a couple more worked examples in the time series section, but the checklist at the end is now stuck to my monitor.
Chamath G.
The correlation section is the best explanation of confounders I have come across. It never once made me feel stupid for not knowing statistics, which is why I actually finished it.
Sanduni P.
I run a small team and approve spend off dashboards I did not build. I now know which parts of those dashboards I trust and which I do not. That is a completely different conversation with our analyst.
Ravindu A.
Practical and honest. The pacing is brisk in the middle sections, so I rewatched a couple. The margin of error lesson is the clearest version of that idea I have seen.
Nethmi J.
Came in expecting to be lost by lesson three. Instead I finished in a fortnight and rewrote our monthly report. Half the charts had the wrong denominator.
Kasun H.
Solid content and well taught, but a lot of it was familiar if you have worked near an analytics team. The chart and time series sections were still worth my time.
Dilini S.
The framing of the axis as an argument changed how I read every chart. I caught a truncated axis in a board deck the same week.
Pasindu R.
Two instructors with genuinely different angles, and it works. One shows you the trap, the other explains why it fools people. Better than either would have been alone.
Hiruni D.
Useful throughout. I especially valued the lesson on who never made it into the data, which is the failure I now see everywhere in our survey results.
Yasas W.
Finished it on the commute over three weeks. I have stopped reporting week-on-week swings that mean nothing, and my updates are shorter and more believed.
