Practice
Short, focused questions for every topic — Basic, Intermediate and Advanced. You write every line; hints point the way without giving code.
- Checked instantly
- Hints, never code
- Earn XP for every solve
Solved · of 1690
SQL · of 800
Python · of 380
Libraries · of 490
ML · of 20
From CREATE TABLE and constraints to window functions and interview combos.
Interview questions
The patterns interviewers ask again and again, collected from every topic.
CREATE TABLE & data types
Design tables with the right columns and types.
Constraints
PRIMARY KEY, NOT NULL, UNIQUE, CHECK, DEFAULT and FOREIGN KEY.
INSERT, UPDATE & DELETE
Add, change and remove rows safely.
ALTER, DROP & views
Change table structure and save queries as views.
SELECT & WHERE
Pick columns and filter rows with conditions.
ORDER BY, LIMIT & DISTINCT
Sort, take the top rows and remove duplicates.
Aggregates & GROUP BY
COUNT, SUM, AVG, MIN, MAX — overall and per group.
HAVING
Filter groups after aggregating.
Joins
INNER, LEFT, RIGHT and FULL joins across tables.
Self & cross joins
Join a table to itself and build combinations.
Subqueries
Queries inside queries: IN, EXISTS and scalar subqueries.
CTEs (WITH)
Break problems into readable steps.
CASE WHEN
Conditional logic, buckets and pivots.
NULL handling
IS NULL, COALESCE, NULLIF and NULLs in aggregates.
String functions
Clean and transform text.
Date functions
Extract parts, truncate, add and subtract dates.
Window functions: ranking
ROW_NUMBER, RANK, DENSE_RANK, NTILE.
Window functions: LAG, LEAD & running totals
Compare rows and accumulate values.
UNION, INTERSECT & EXCEPT
Combine and compare result sets.
Mixed & interview problems
Combine everything — the questions interviews love.
Strings, lists, dicts, loops, functions, lambdas and comprehensions.
Interview questions
The patterns interviewers ask again and again, collected from every topic.
Strings
Slicing, methods, formatting and text puzzles.
Lists
Indexing, slicing, sorting and list methods.
Tuples & sets
Immutable sequences and set operations.
Dictionaries
Counting, grouping and looking things up.
For loops
Iterate, accumulate, enumerate and zip.
While loops
Loop until a condition changes.
Functions
Parameters, defaults, return values and recursion.
Lambda, map, filter & sorted
Small functions and functional tools.
Comprehensions
List, dict and set comprehensions.
Practice test 1: basics
Ten mixed questions on strings, lists, dicts and loops, to check you're ready.
Practice test 2: functions & more
Ten mixed questions on sets, functions, lambdas, comprehensions and recursion.
The four libraries every data analyst uses, in the order to learn them. Every question is a small task from real work, from the very first array to full analysis and report charts.
NumPy
0 / 110 solvedFast arrays: the maths engine under pandas and every ML library.
NumPy: creating & inspecting arrays
Build arrays from data and read their shape, size and type.
NumPy: indexing, slicing & masks
Pick values, rows, columns and everything that matches a rule.
NumPy: vectorised maths & statistics
Whole-array maths, aggregates and classic analysis formulas.
NumPy: reshape, stack & broadcasting
Change shapes, join arrays and combine different shapes.
NumPy: random, linear algebra & real analysis
Simulations, matrices and business problems solved with arrays.
NumPy: practice test
Ten mixed questions from every topic above, to check you're ready.
Pandas
0 / 160 solvedLoad, explore, clean, combine and summarise business data tables.
Pandas: first look at a dataset
Series and DataFrames: size, types, summaries and quick counts.
Pandas: selecting & filtering
Select columns, filter rows, sort and add columns.
Pandas: groupby & aggregation
Summaries per group.
Pandas: merge, pivot & reshape
Join DataFrames and change their shape.
Pandas: cleaning data
Missing values, duplicates, text and dates.
Pandas: dates & time series
Date parts, resampling, rolling windows and period comparisons.
Pandas: apply, windows & analysis tasks
Custom logic, ranking, cohorts and full analyst tasks.
Pandas: practice test 1
Ten mixed questions on selecting, filtering and groupby.
Pandas: practice test 2
Ten mixed questions on merges, dates, pivots and analysis.
Matplotlib
0 / 110 solvedBuild any chart line by line, from a quick plot to a report figure.
Matplotlib: line charts & labels
Your first charts: lines, titles, axis labels and legends.
Matplotlib: bars, histograms & pies
Compare categories and show distributions and shares.
Matplotlib: scatter plots & styling
Relationships between numbers, colours, markers and line styles.
Matplotlib: subplots & layouts
Several charts in one figure, shared axes and twin axes.
Matplotlib: report-ready charts
Annotations, reference lines and charts a manager can read.
Matplotlib: practice test
Ten mixed questions from every topic above, to check you're ready.
Seaborn
0 / 110 solvedStatistical charts in one call: distributions, comparisons and heatmaps.
Seaborn: distributions
Histograms, density curves and ECDFs of a column.
Seaborn: comparing categories
Count, bar, box, violin and point plots.
Seaborn: relationships & trends
Scatter and line plots with hue, style and size.
Seaborn: heatmaps & regression
Correlation and pivot heatmaps, trend lines.
Seaborn: facets, themes & analysis
Small multiples, styling and full charting tasks.
Seaborn: practice test
Ten mixed questions from every topic above, to check you're ready.
Every algorithm starts with a real business problem. First watch a model being built step by step and run it yourself, then build one on new data in the practice questions. Python and pandas first will make this much easier.
Linear Regression
Regression
Predict the delivery time of a food order.
Logistic Regression
Classification
Will a loan applicant repay or default?
Coming soon
K-Nearest Neighbours
Classification
Recommend a plan from what similar customers chose.
Coming soon
Decision Tree
Classification
Which employees are likely to leave the company?
Coming soon
Random Forest
Classification
Which telecom customers will stop recharging?
Coming soon
Naive Bayes
Classification
Is this SMS spam or a real message?
Coming soon
Support Vector Machine
Classification
Flag a risky card transaction.
Coming soon
K-Means Clustering
Clustering
Group mall shoppers for targeted offers.
Coming soon
PCA
Dimensionality reduction
Shrink 12 survey questions into 2 clear scores.
Coming soon