PayPal Data Analytics Interview Questions & Answers

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

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

A function that calculates over a set of rows related to the current row without collapsing them, such as ROW_NUMBER, RANK, LAG or SUM() OVER (PARTITION BY ...).

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

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

Relative references change when copied (A1); absolute references stay fixed ($A$1); mixed references lock only the row or column (A$1 or $A1).

SUMIFS adds values that meet several conditions; COUNTIFS counts rows that meet several conditions. Example: =SUMIFS(Sales, Region, "North", Month, "Jan")

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.

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

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).

Start from the business questions, show 4–6 top KPIs with trend vs target, allow drill-downs, keep colours consistent, remove clutter, add time/region filters and give one clear insight per visual.

Profile the data, remove exact duplicates using keys, impute missing values (mean/median/mode or a business rule), flag imputed rows and document every cleaning step.

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.

WHERE filters rows before grouping. HAVING filters groups after GROUP BY and can use aggregates like SUM() or COUNT().

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.

Use np.where for one condition, np.select for several, or df.apply with a function for custom logic.

Row context evaluates one row at a time (calculated columns); filter context is the set of filters from slicers, visuals and relationships that a measure sees.

It automatically formats cells based on rules such as colour scales, data bars or highlighting values above a target, making patterns visible at a glance.

Compare with a control group or use difference-in-differences, control for seasonality, and measure incremental sales against campaign cost.

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

pd.merge(df1, df2, left_on='cust_id', right_on='id', how='left') joins on differently named keys.

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

Applying operations to whole arrays at once instead of Python loops, which is far faster because it runs in optimised compiled code.

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.

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

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.

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

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

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

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

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.

Data tab, Remove Duplicates, pick the columns to compare. For a non-destructive way use UNIQUE() or conditional formatting to highlight repeats first.

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

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

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

Pull data with SQL or APIs, clean and aggregate with Pandas, build charts and an Excel or PDF output, and schedule it with cron or Airflow with logging and alerts.

SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.

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

A table auto-expands, supports structured references, banded rows and slicers, and keeps formulas consistent as rows are added.

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.

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

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

It evaluates an expression after modifying the filter context, for example CALCULATE(SUM(Sales[Amount]), Region[Name] = "North").

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

A subquery is inline; a CTE is a named, readable block for one statement; a temp table is physically stored for the session and can be indexed and reused.

Descriptive statistics summarise the data you have; inferential statistics use a sample to draw conclusions about a larger population.

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.

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

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

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

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

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

Links between tables on key columns; cardinality can be one-to-many, many-to-one or many-to-many, and cross-filter direction controls how filters flow.

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

DELETE removes chosen rows and can be rolled back; TRUNCATE empties the table quickly; DROP removes the table structure itself.

A result can be statistically significant yet too small to matter; always report effect size and business impact together with the p-value.

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

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

Indexes speed up lookups by keeping sorted pointers. They slow writes and use space, so index columns used in filters and joins, not every column.

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

Find each user's first activity date as a cohort, join to later activity, and compute the share of each cohort active in day, week or month N.

Bookmarks save a report view state for navigation; drill-through jumps to a detail page filtered to the item you picked.

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

A central fact table (sales, numeric events) linked to dimension tables (date, product, customer). It keeps Power BI models simple, fast and correct when filtering.

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.

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.