Statistics Fundamentals
Describe data, reason with probability, and draw honest conclusions from samples, with short fill-in-the-blank Python exercises.
Leads to: Data Analyst, Data Scientist, Machine Learning Engineer, Data Engineer, Civil Engineer, Mechanical Engineer, Biochemist, Biological Technician, Medical Laboratory Technologist, Chemist, Physicist, Materials Scientist, Statistician, Actuary, Operations Research Analyst
Before you start
This course builds on Math for Tech & STEM. Take it first if you are new to the topic.
About this course
A beginner statistics course for data, science, lab, engineering and actuarial careers. You will classify variables and summarise them with the mean, median and standard deviation; read histograms, skew and outliers; work with probability rules, conditional probability and the normal distribution; spot sampling bias; build and interpret confidence intervals and hypothesis tests; and measure relationships with correlation and simple linear regression, finishing with the ways true numbers tell misleading stories. The only prerequisite is Math for Tech & STEM. Python exercises are fill-in-the-blank: the code already runs and you write one formula, using only Python's standard library.
Course content
Module 1
Describing Data
3 lessons · 1h 33mTypes of variables, centre and spread, and reading the shape of a distribution with histograms, skew and outliers.
- 16mTypes of data and variablesReading
Classify variables as categorical or numerical and choose the summary that fits each type.
- 39mMean, median, and spreadReading
Compute the mean, median, range and standard deviation, and pick the summary that fits the data.
- 38mHistograms and distributionsReading
Read a histogram's shape, recognise skew, and flag outliers with the 1.5 × IQR rule.
Module 2
Probability Basics
3 lessons · 1h 54mProbability rules, conditional probability and Bayes' rule for test results, and the normal distribution with z-scores.
- 39mProbability rulesReading
Compute simple and combined probabilities with the complement, addition and multiplication rules.
- 37mConditional probabilityReading
Use conditional probability and Bayes' rule to work out what a positive test result really means.
- 38mThe normal distributionReading
Use z-scores and the normal curve to find the probability of values above, below or between limits.
Module 3
From Sample to Population
3 lessons · 1h 45mSampling methods and bias, confidence intervals, and hypothesis tests with p-values.
- 25mSampling and biasReading
Recognise common sampling methods and spot the biases that make a sample misrepresent its population.
- 41mConfidence intervalsReading
Compute a 95% confidence interval for a mean and interpret it correctly.
- 39mHypothesis tests and p-valuesReading
Set up null and alternative hypotheses, run a simple z-test, and interpret the p-value without overclaiming.
Module 4
Relationships Between Variables
3 lessons · 1h 38mCorrelation, simple linear regression, and how Simpson's paradox and misleading charts distort conclusions.
- 41mCorrelationReading
Compute Pearson's correlation coefficient r and interpret its sign, strength and limits.
- 44mSimple linear regressionReading
Fit a least-squares line, interpret its slope and intercept, and use it to predict responsibly.
- 13mMisleading statisticsReading
Spot Simpson's paradox, misleading charts and other ways true numbers tell false stories.