A practical Pandas reference for data analysts: loading data, filtering, groupby aggregations, merging DataFrames, pivot tables, rolling window functions, and a SQL-to-Pandas cheat sheet.
Learn to communicate data insights that drive action: story structure, audience-tailored communication, insight headlines, the SCR framework, chart annotations, and the most common storytelling mistakes to avoid.
Why Data Cleaning Matters Data cleaning — also called data wrangling or data preparation — is the process of detecting and correcting (or removing) corrupt, inaccurate, or irreleva
Learn how to design effective dashboards: choosing the right chart type, establishing visual hierarchy, using colour correctly, writing consistent SQL metric definitions, and the most common design mistakes to avoid.
A practical introduction to the statistics every data analyst needs: descriptive statistics, probability distributions, hypothesis testing, confidence intervals, linear regression, and a guide to choosing the right test.
A practical guide to cleaning raw data: handling missing values with imputation, removing duplicates, standardising formats, detecting outliers, and running cleaning pipelines in both Python and SQL.
A systematic guide to EDA: data quality audits, univariate and bivariate analysis, correlation matrices, SQL profiling queries, and the red flags every analyst should know how to spot.
A complete guide to designing and analysing A/B tests: sample size calculation, two-proportion z-tests in Python, SQL experiment queries, common pitfalls, and an introduction to multi-armed bandit methods.
Learn how to build sequential conversion funnels using SQL and Python, calculate drop-off rates at each step, segment by user dimensions, and identify where users abandon the journey.
Learn how to build retention and LTV cohort tables in SQL and Python, read the triangle heatmap, and avoid the common pitfalls that lead to misleading conclusions.
Master SQL window functions — ranking, LAG/LEAD, running totals, moving averages, and sessionisation — with practical examples for every common analytical pattern.
A comprehensive guide to creating, transforming, encoding, and selecting features that improve ML model accuracy — with Python examples using pandas and scikit-learn.
A practical guide to detecting and fixing the most common data quality issues — missing values, duplicates, outliers, type errors, and inconsistent categories — with Python code examples using pandas.
What Is Time Series Data? A time series is a sequence of observations recorded at successive, equally-spaced points in time. Unlike cross-sectional data — which captures a snapshot
Why SQL Is the Core Language of Data Analysis Structured Query Language (SQL) remains the most widely used tool in a data analyst's toolkit. Unlike programming languages that requi
What Is Customer Segmentation? Customer segmentation is the process of dividing a customer base into distinct groups of individuals who share similar characteristics — such as beha
What Is Customer Segmentation? Customer segmentation is the process of dividing a customer base into distinct groups of individuals who share similar characteristics — such as beha
Why Experimentation Is Central to Data-Driven Decision Making A/B testing — formally called a randomised controlled experiment — is the gold standard for establishing causal relati
What Is Regression Analysis? Regression analysis is a statistical technique for modelling the relationship between a dependent variable (the outcome you want to predict or explain)
The Role of a Data Warehouse in an Analytics Stack A data warehouse is a centralised repository that integrates data from multiple source systems, organises it for analytical queri
