Data Science
9 stories tagged Data Science on Learning Fanatic.
- Lesson 3.1: The analytics workflow: CRISP-DM and exploratory analysis Learn the six phases of CRISP-DM, how to turn a vague request into a testable question, and a step-by-step routine for exploratory data analysis.
- Lesson 2.4: Correlation, causation, bootstrapping and A/B tests Learn to measure how two variables move together, why that is not causation, how to bootstrap an interval, and how to run and read an A/B test.
- Lesson 2.3: Confidence intervals, t-tests and hypothesis tests Learn to put an honest margin around an estimate, run a t-test and a chi-square test by hand, read a p-value correctly and avoid false positives.
- Lesson 2.2: Sampling, bias and the central limit idea Learn how a sample tells you about a population, why bias beats sample size, and how the central limit theorem makes averages predictable.
- Lesson 2.1: Probability and distributions, from coins to bell curves Learn the rules of probability, how Bayes' rule corrects a gut feeling, and how the binomial and normal distributions describe real business data.
- Lesson 1.4: Charting numbers honestly and spotting misleading charts Learn to pick the right chart for each question and to spot the tricks that make honest numbers lie: cut axes, cherry-picked windows and raw counts.
- Lesson 1.3: Summary statistics: centre, spread and shape Learn the mean, median, mode, standard deviation, quartiles and skew, when each one misleads, and how to describe a column in three numbers.
- Lesson 1.2: Cleaning messy data and handling missing values Learn to find and fix duplicates, inconsistent labels, impossible values and blanks, and to choose between deleting and filling missing data.
- Lesson 1.1: What data is, in rows, columns and types Learn to read any table of data: what a row, a column and a data type are, and the one question to ask before you touch a single number.