Decide with data and know how sure you are: distributions and outliers, sampling, confidence intervals, hypothesis tests, A/B testing, power, multiple comparisons, regression, forecasts and a guided pricing experiment project.
Level
03
Modules
05
Lessons
22
Introduction
Downloadable Assets and Resources
Distributions and Shape
Spread, Percentiles and Outliers
Correlation Is Not Causation
Sampling and Bias
Assessment
"Is this difference real?" and "how sure are you?" are the two questions that decide whether an analysis is trusted. This course teaches the statistics behind them without turning into a math course: every idea is explained with its formula, then tested by running code and simulations on data, so that you see why a method works and where it breaks.
Each lesson has a short theory section with the definition, the assumptions and a worked number, a diagram of the idea, and then code that you run in Python with pandas, SciPy and statsmodels. Wherever a result can be checked by simulation (the central limit theorem, false alarms under peeking, the winner's curse of small studies) you run the simulation and compare. The exercises come with hints or answers, and each lesson ends by pointing to the next.
The data is the fictional online shop Trailhead Outfitters plus a fictional A/B test of a new checkout page with 20,000 visitors, so the same customers and orders come back and the results are easy to sanity-check.
The final module is a full pricing experiment analysis: a four-week test of a new shipping threshold with 24,000 visitors. You write the hypothesis and the decision rule before looking at any result, check the randomization, test conversion and order value with intervals, size the effects and the power, look at segments with a multiplicity correction, fit an adjusted regression and end with a decision memo that says how sure you are and what the experiment cannot show. A starter notebook with 26 self-checks and a worked solution are included, and the data is built so that the honest answer is not obvious.
Lessons that pair each method with its formula and a simulation, real experiment data to test on, a guided pricing experiment project with starter and solution notebooks, and a quiz for every module.
Anyone can report a number. The analysts people trust are the ones who can say how sure they are, and why. If you run experiments, read dashboards or defend recommendations, this course gives you the tools to do that with confidence, and it explains the math well enough that you will actually remember it. Stop guessing whether a difference is real. Enroll now, and learn to back every claim with evidence.
SkillEnsure certificates reflect hands-on practice and real-world skills, not just watching videos or reaching the end of a course.
Learn moreIt’s different. It’s not about watching content endlessly, it’s about proving competency and earning a credential that actually means something.
It was a good opportunity to strengthen my understanding of the fundamentals of Machine Learning and get some practical exposure to the core concepts Since I’m continuing my journey in AI & Data Science I’m trying to keep learning and improving my skills step by step.
I’ve worked in my field for years, but never had a structured way to prove my skills. They gave me a credible, verifiable certification that reflects my real ability.
I really enjoyed the curriculum and the practical insights from the AI Engineering course. Looking forward to applying these concepts to build impactful AI solutions.
