Analytics
494 bites tagged Analytics — interview questions with model answers, and 60-second explainers.
Outlier Detection: Finding Data That Doesn't Belong
Outlier detection finds data points that don't fit the pattern, signaling an error, fraud, or a new event. It's used to spot faulty sensor readings or fraudulent transactions.
Cluster Analysis: Finding Hidden Groups in Your Data
Cluster analysis automatically finds natural groupings in unlabeled data, like sorting a mixed bag of Legos without a manual. It's used for customer segmentation or anomaly detection.
Regression Analysis: Finding the Line of Best Fit
Regression analysis draws a line through data to model relationships between variables. Use it to predict a house price from its square footage or forecast sales from ad spend. The footgun: a strong correlation doesn't prove one variable causes the other.
P-value: Probability of Your Data, Not Your Hypothesis
A p-value measures how surprising your data is, assuming your null hypothesis (e.g., "no change") is true. It's used in A/B tests to decide if an effect is real. The footgun: a low p-value doesn't prove your theory, it just casts doubt on the null.
Correlation Is Not Causation
Just because two metrics move together doesn't mean one causes the other. This is vital when analyzing user data, as a feature launch might correlate with higher signups when the real cause was a marketing campaign.
Exploratory Data Analysis (EDA): Look Before You Leap
Exploratory Data Analysis (EDA) is like being a detective with your data: you look for clues, patterns, and outliers before forming a theory. It's the first step in any data project, from building a model to creating a dashboard.
Lie Factor: Quantifying Visual Distortion in Graphs
The Lie Factor measures how much a graph's visuals distort the data's story. It's used to critique charts that exaggerate changes, like with a truncated y-axis.
Choropleth Maps: Coloring Data by Region
A choropleth map colors geographic areas to represent a metric, like shading states red or blue on an election map. It's used to show regional data like population density or sales per territory.
Sankey Diagram: Visualizing Proportional Flow
A Sankey diagram visualizes flow, where the width of each path is proportional to the quantity moving through it. Use it to trace user journeys or track budget allocation.
The Narrative Arc for Data Storytelling
A narrative arc gives data a story by building tension toward a key insight. It guides stakeholders from a problem (plot) to a turning point (climax) and a resolution. The footgun is oversimplifying; compelling stories have multiple smaller tension peaks.
Small Multiples: Comparing Data with a Grid of Charts
Small multiples are a comic strip for data, showing different dataset slices in a grid of charts with identical axes. They're used to compare trends across categories, like sales per region. The footgun is using inconsistent scales, which breaks comparison.
Data-Ink Ratio: Maximize Signal, Minimize Noise
The Data-Ink Ratio states that a good chart maximizes the ink showing data and minimizes everything else. It's a call to erase 'chart junk'—heavy gridlines or 3D effects—that doesn't convey information.
Chart Selection: Match Purpose, Not Looks
Start with the purpose, not the chart. The question you're asking—'how do these compare?' or 'what's the trend?'—determines the best visualization. A line chart shows trends; a bar chart compares categories.
Anscombe's Quartet: When Numbers Lie
Anscombe's Quartet shows how four datasets can share identical summary stats (mean, variance) but look completely different when plotted. It's a classic reminder to always visualize your data before trusting numerical summaries.
The Semantic Layer: A Business Map for Company Data
A semantic layer is a translation dictionary for data, mapping cryptic database columns to plain business terms like "Revenue." It lets non-technical teams build reports without writing SQL.
Exception Reporting: Focus on Signals, Not Noise
Exception reporting filters out the noise, showing only data that breaks predefined rules. It's used in financial reconciliation to flag mismatched transactions or to alert on system performance dips.
Benchmarking: Know Where You Stand in Your Industry
Benchmarking answers "Are we good?" by comparing your performance metrics against industry bests. It's used to set realistic goals for cost, quality, or time. The main footgun is comparing apples to oranges—using benchmarks from dissimilar companies.
Data Visualization: Turning Numbers into Insight
Data visualization turns raw data into pictures, revealing stories that numbers alone can't tell. It's used to spot trends, find outliers, and grasp complex relationships in datasets.
Ad Hoc Reporting: Answering One-Off Business Questions
Ad hoc reporting is your data "quick dive" for one-off questions, unlike static dashboards. A sales team might use it to see how a holiday affected regional sales.
Cross-Tabulation: Finding Relationships in Your Data
Cross-tabulation reveals how two variables are related by counting their joint occurrences in a grid. It's key for survey analysis or A/B testing. The footgun is assuming correlation implies causation; the table shows a relationship, not its cause.
Drill-Down Analysis: From Summary to Specifics
Drill-down analysis moves from a high-level data summary to the granular details composing it. It's used in dashboards to investigate a metric's change, like clicking a monthly sales dip to see daily figures.
Data Aggregation: The Big Picture from Small Details
Data aggregation rolls up granular records into high-level summaries, like turning individual sales logs into a daily sales report. It's used to power dashboards and speed up warehouse queries.
Business Intelligence (BI) Tools: From Raw Data to Dashboards
BI tools turn raw company data into visual dashboards and reports. They let non-technical teams explore sales trends or user behavior from a data warehouse, but remember: a slick dashboard built on messy data is just a pretty lie.
Descriptive Statistics: What Your Data Looks Like
Descriptive statistics summarize the data you have, painting a picture of your sample without making guesses about the wider world. It's used for calculating things like average age or max response time.
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