JP Morgan Chase Data Analytics Interview Questions & Answers

Joining a table to itself with aliases, useful for hierarchies such as employees and managers or comparing rows within the same table.

df = pd.read_csv('file.csv'); then head(), info(), describe() and isna().sum() for a quick health check.

Check histograms and skew(), then apply log, square-root or Box-Cox transforms, or use medians and robust statistics.

INNER JOIN returns only matching rows. LEFT JOIN returns every row from the left table and NULLs where the right table has no match — e.g. all customers, including those with no orders.

Read the execution plan, add or fix indexes, select only needed columns, filter early, avoid functions on indexed columns, and replace correlated subqueries with joins.

Organising tables to reduce redundancy and anomalies by splitting data into related tables, commonly up to third normal form.

It extracts and transforms data using recorded steps in M language, such as merging, unpivoting, changing types and removing errors, before loading to the model.

Salaries are right-skewed, so the median better represents the typical employee while the mean is pulled up by a few high earners.

LEFT JOIN customers to orders and keep rows where order_id IS NULL, or use NOT EXISTS, which also handles NULLs safely.

DAX functions like TOTALYTD, SAMEPERIODLASTYEAR and DATEADD compare periods; they need a proper continuous date table marked as a date table.

Understand why it is missing, consider imputation or a missing flag, test sensitivity, and talk to data owners about fixing collection if it is systematic.

You assume no effect (null), collect evidence and decide whether the data is surprising enough to reject that assumption at a chosen significance level.

It groups rows sharing values so aggregates run per group, for example SELECT region, SUM(sales) FROM orders GROUP BY region.

An ETL tool inside Excel for importing, cleaning and shaping data with recorded steps that refresh automatically when the source changes.

Correlation shows the strength and direction of association; regression builds an equation to predict one variable from others.

Create an engine with SQLAlchemy or a driver such as psycopg2, then run pd.read_sql(query, engine) to load results into a DataFrame.

Sort by date (newest first), then Data → Remove Duplicates on the key column — Excel keeps the first occurrence. Or use UNIQUE / a COUNTIFS helper column.

A primary key uniquely identifies each row in its table; a foreign key references another table's primary key to enforce relationships.

Line for trends, bar for comparisons, scatter for relationships, histogram for distributions and pie only for few parts of a whole.

Start with KPIs (revenue, margin, growth), add trend and comparison to target, allow drill-down by region and product, keep it to one screen and test with the audience.

Detect with IQR or z-score, then cap with clip, remove, transform with log, or keep and flag them depending on whether they are errors or real events.

df['sales'].rolling(window=7).mean() gives a 7-period moving average, useful to smooth daily noise.

SUM(amount) OVER (ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) adds up values cumulatively.

It depends on margin of error, confidence level and expected variability; larger samples give smaller errors with diminishing returns.

A report is multi-page and interactive from one dataset; a dashboard is a single-page canvas of pinned tiles from many reports in the Service.

A tool that summarises large tables by dragging fields into rows, columns, values and filters, so you can slice sales by region or month without writing formulas.

Aggregate revenue by month, use LAG(revenue) OVER (ORDER BY month), then (revenue - prev) / prev x 100.

CALCULATE evaluates an expression in a modified filter context, e.g. CALCULATE([Total Sales], Region[Name] = "North"). It is the key function for time intelligence and comparisons.

A pivot table summarises large data by dragging fields into rows, columns, values and filters — for example total sales by region and month — without writing formulas.

Use a star schema, remove unused columns, prefer measures, reduce visuals per page, limit high-cardinality fields and use aggregations or incremental refresh.

Pick metrics tied to a business goal that are measurable, actionable and owned by someone, and pair a lagging metric with a leading one.

Data → Data Validation restricts what can be entered in a cell (lists, number ranges, dates), reducing manual entry errors in shared sheets.

NULL means unknown, so comparisons with = fail; use IS NULL, COALESCE for defaults and remember most aggregates ignore NULLs except COUNT(*).

VLOOKUP only looks to the right and breaks when columns are inserted. INDEX-MATCH looks in any direction and is robust. XLOOKUP is the modern option with exact match by default and left lookups.

Matplotlib is the low-level plotting library with full control; Seaborn builds on it with attractive statistical charts and simpler syntax.

A Microsoft BI tool made of Power BI Desktop for building reports, the Service for sharing, and Mobile; it connects, models and visualises data.

Visually with a box plot, with the IQR rule (below Q1 − 1.5×IQR or above Q3 + 1.5×IQR) or a z-score above 3. Then decide with the business whether to cap, remove or keep them.

Lead with the answer, use one simple chart per point, translate numbers into business impact (₹, %), avoid jargon and end with a clear recommendation and next step.

A Common Table Expression (WITH name AS (...)) is a named temporary result used inside one query. Use it to break complex logic into readable steps or for recursive queries.

Import loads data into memory for fast visuals; DirectQuery queries the source live, giving fresh data but slower performance and some DAX limits.

Use RFM scoring or clustering on behaviour and value, validate segments with business teams and attach an action to each segment.

It adds conditional logic inside queries, for example bucketing ages or flagging values, returning different results per condition.

Grouping users by a shared start date or trait and tracking behaviour over time, for example monthly retention per signup month.

Pandas for data handling, NumPy for numerics, Matplotlib and Seaborn for charts, and openpyxl or SQLAlchemy for files and databases.

melt converts wide data to long format; pivot converts long data to wide, and they are used to reshape data for analysis or charts.

A calculated field lives inside a PivotTable and works on summarised data; a helper column is a normal column of formulas that feeds the PivotTable or other formulas.

A Common Table Expression is a named temporary result defined with WITH that makes complex queries readable and allows recursion.

Read in chunks with chunksize, load only needed columns, set efficient dtypes or switch to Dask, Polars or a database.

df['date'] = pd.to_datetime(df['date']); df['amount'] = pd.to_numeric(df['amount'], errors='coerce'); astype('category') for repeated strings.

Goal Seek changes one input to hit a target output; Solver optimises with several variables and constraints, for example maximising profit under budget limits.

COUNT counts numbers, COUNTA counts non-empty cells and COUNTIF counts cells meeting one condition; COUNTIFS handles several conditions.

Track users through steps (visit → product view → cart → payment), compute conversion between each step, find the biggest drop, segment by device and source, and suggest fixes to test.

ROW_NUMBER gives a unique sequence. RANK gives ties the same rank and skips the next (1, 1, 3). DENSE_RANK gives ties the same rank without gaps (1, 1, 2).

A calculated column is computed row by row and stored in the model. A measure is computed at query time using the filter context, e.g. Total Sales = SUM(Sales[Amount]). Prefer measures for aggregations.

Confirm data accuracy first → break down by region, product, channel and customer segment → compare with seasonality and last year → check pricing, stock-outs, marketing changes and funnel conversion → present the root causes with clear charts.

Slicers are on-page visual filters users click; filters can be set at visual, page or report level and may be hidden from users.

DAU/MAU, conversion rate, average order value, cart abandonment, retention/churn, CAC, LTV, return rate and NPS.

MATCH finds the position of a value in a range and INDEX returns the item at that position, giving a flexible lookup that works left or right and survives column insertions.

Use TRIM, CLEAN, UPPER or PROPER, Text to Columns, Flash Fill, SUBSTITUTE, and Power Query for repeatable cleaning steps.

UNION removes duplicate rows and sorts internally; UNION ALL keeps every row and is faster.

IF returns one result if a test is true and another if false. Nest IFs for multiple outcomes, or use IFS for cleaner formulas.

DAX is the formula language for Power BI models. It works on columns and tables with filter context rather than on individual cells.

Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.

A view stores a query and runs it each time; a materialised view stores the result physically and must be refreshed, making reads faster.

The population is the whole group of interest; a sample is a subset used to estimate population values.

Mean for symmetric data without outliers, median for skewed data (salaries, house prices), mode for categories (the most common city).