Uber Data Analytics Interview Questions & Answers

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.

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

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

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.

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.

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

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.

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

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

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

SELECT order_date, SUM(amount) OVER (ORDER BY order_date) AS running_total FROM sales;

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.

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

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

Rules that restrict data per user role, for example sales managers seeing only their region, defined with DAX filters and assigned in the Service.

INNER returns matching rows, LEFT keeps all left rows, RIGHT keeps all right rows, FULL keeps all rows from both, and CROSS returns every combination.

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

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 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.

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 ...).

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

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

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

Check data pipelines first, then segment by channel, region, product and device, compare to the same period last year, and look for launches, outages or campaigns.

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.

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.

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

A bridge that lets the Service refresh data from on-premises sources securely on a schedule.

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

Fix the root cause when possible, or wrap formulas with IFERROR or IFNA to return a clear fallback value.

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.

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

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.

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

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.

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

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

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

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

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

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.

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.

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.

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.

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

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

Use XLOOKUP or COUNTIF to flag missing items in each list, or merge them in Power Query with a full outer join and filter unmatched rows.

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

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