Applied Statistics
The statistical concepts every data analyst actually uses — standard deviation, outlier detection, correlation, and A/B testing — explained practically with Python, not heavy math.
Why learn Applied Statistics?
Most working data analysts do not need heavy math, they need the handful of statistical ideas that come up constantly: measures of center and spread, what a distribution is actually telling you, correlation versus causation, and how to design and read an A/B test. Applied Statistics teaches exactly those, in Python, with no theoretical detours.
It is 2 modules and 10 lessons: descriptive statistics, covering measures of center, spread and standard deviation, distributions in practice, correlation versus causation, and a summary stats workflow, and A/B testing, covering what it is, designing a test, statistical significance explained in plain English, running a test in Python, and the difference between statistical and practical significance.
By the end you can look at a dataset and describe it precisely, and design an experiment you can actually trust the result of.
Who this course is for
- Aspiring data analysts who want statistics that is immediately usable, not academic
- Anyone who has run an A/B test without being sure the result meant anything
- Python for AI or Data Cleaning & EDA graduates ready for the analysis layer
- Developers preparing for the Data Analyst Portfolio track
What you'll build and practice
- Fluency with mean, median, standard deviation, and what a distribution's shape tells you
- A working sense of when correlation does and does not imply causation
- A properly designed A/B test, from hypothesis to sample size
- Statistical significance explained and computed in plain Python, not treated as a black box
- The judgment to tell statistical significance apart from practical significance
What you'll learn
Applied Statistics is organized into 2 focused modules. By the end you'll be comfortable with:
- Descriptive Statistics
- A/B Testing
Course curriculum
10 lessons across 2 modules. Lessons marked Free preview are readable without an account.
Module 1. Descriptive Statistics
Summarise data with the measures every analyst uses daily — no heavy math required
- 10mMeasures of CenterFree preview
Mean vs median — the most important choice in descriptive statistics, and when each one misleads.
- 10mSpread and Standard DeviationFree preview
Understand standard deviation as 'typical distance from the average' — and use it to flag unusual values.
- 10mDistributions in Practice
Recognize normal, right-skewed, and bimodal distributions from a histogram — and know what each means for your analysis.
- 10mCorrelation vs Causation
Use df.corr() to find relationships — and learn why correlation never proves cause.
- 12mSummary Stats Workflow
A practical end-to-end routine for characterising any new numeric column in under 2 minutes.
Module 2. A/B Testing
Design and analyse experiments to make data-driven product decisions with confidence
- 10mWhat is A/B Testing?
How companies use controlled experiments to make data-driven product decisions — and why it beats opinions.
- 12mDesigning an A/B Test
How to choose the right metric, avoid common design mistakes, and know when you have enough data.
- 12mStatistical Significance in Plain English
What p-values actually mean — explained without formulas, using plain language and code output.
- 14mRunning an A/B Test in Python
End-to-end: load the data, compute conversion rates, run the significance test, and report the result.
- 12mStatistical vs Practical Significance
Why 'statistically significant' and 'worth doing' are two completely different questions — with examples.
Frequently asked questions
- Is this course heavy on math and formulas?
- No, that is a deliberate design choice named right in the course description: the statistical concepts every analyst actually uses, explained practically with Python rather than heavy math. Formulas appear, but always attached to a concrete, worked example.
- Does Applied Statistics require any statistics background at all?
- No. Module 1 starts with measures of center, the kind of averaging most people already do intuitively, and builds up from there. No prior coursework in statistics is assumed.
- What does the course actually mean by statistical versus practical significance?
- It is the final lesson of the course, and the distinction it teaches is that a result can be statistically real but too small to matter for the business, or borderline statistically but large enough to act on. Knowing which one you are looking at is what keeps A/B test conclusions honest.
- Is A/B testing covered with real Python code, or just theory?
- With real code. The Running an A/B Test in Python lesson has you compute significance directly, following the conceptual groundwork laid in the Designing an A/B Test and Statistical Significance in Plain English lessons before it.
- How does this course relate to Data Visualization & Business Metrics?
- They are companion tracks. That course focuses on charting and standard business metrics like churn and LTV. This one focuses on the statistical reasoning, distributions, correlation, and experiment design, that lets you trust the numbers behind those charts.
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