Data Analysis Learning Path

This path orders our data analysis tips into five stages: describing data, cleaning it, statistical inference, relationships and experiments, and applied techniques like cohort and time series analysis. Statistics builds strictly on itself, so the order matters more here than in any other category.

Stage 1: Describing Data

Every analysis starts with summarizing a column of numbers. Means, spread, and the normal distribution are the vocabulary the rest of statistics is written in.

1. Mean, Median, Mode — choosing the wrong average misleads entire reports.
2. Descriptive Statistics — the standard first pass on any new dataset.
3. Standard Deviation — spread matters as much as the average does.
4. Normal Distribution — the assumption behind most tests you'll use later.

Stage 2: Cleaning and Preparing Data

Analysts spend most of their time here, and inference on dirty data is worse than no inference. Learn quality checks before you learn tests.

1. Data Quality Checks — a repeatable checklist before any analysis begins.
2. Handling Missing Data — deletion vs imputation changes your conclusions.
3. Outlier Detection — one bad row can drag every statistic off course.
4. Sampling Methods — biased samples produce confident wrong answers.

Stage 3: Statistical Inference

This stage answers "is this difference real or just noise?" Confidence intervals come first because p-values only make sense once you understand uncertainty.

1. Confidence Intervals — quantifies uncertainty instead of pretending precision.
2. Hypothesis Testing Steps — the framework every statistical test follows.
3. P-Values Explained — the most misused number in all of analytics.
4. T-Tests — comparing two group means, the everyday workhorse test.
5. Chi-Square Test — the equivalent test for categories and proportions.

Stage 4: Relationships and Experiments

With inference down, study how variables relate and how to prove causation. A/B testing is where all the previous stages combine into business decisions.

1. Correlation vs Causation — the distinction that keeps analysts honest.
2. Regression Basics — models relationships and makes simple predictions.
3. A/B Testing — controlled experiments, the gold standard for causation.
4. A/B Test Sample Size — underpowered tests waste weeks and mislead teams.

Stage 5: Applied Analysis Patterns

Finish with two techniques that show up constantly in product and business analytics. They come last because they lean on everything above.

1. Cohort Analysis — reveals retention trends that averages hide completely.
2. Time Series Analysis — trend and seasonality in metrics tracked over time.

How Long Does This Take?

The tips themselves read in about 15 minutes, but statistics rewards slow practice. A realistic pace is 6-8 weeks: one week each for Stages 1 and 2, two to three weeks on inference (redo the tests by hand on small datasets), and the rest applying Stages 4-5 to metrics from your own work.