Top 50 Most Asked Data Analytics Interview Questions

Cumulative Sales = VAR maxDate = MAX('Date'[Date]) RETURN CALCULATE( [Total Sales], FILTER(ALL('Date'[Date]), 'Date'[Date] <= maxDate) ) -- for a running total that resets each year use TOTALYTD([Total Sales], 'Date'[Date])

// First Purchase Date { FIXED [Customer ID] : MIN([Order Date]) } // Order Type IF [Order Date] = [First Purchase Date] THEN 'New' ELSE 'Repeat' END // Cohort Month for retention analysis DATETRUNC('month', [First Purchase Date])

city_map = { 'delhi': 'Delhi', 'new delhi': 'Delhi', 'bombay': 'Mumbai', 'mumbai': 'Mumbai', 'bengaluru': 'Bengaluru', 'bangalore': 'Bengaluru', } clean = df['city'].str.strip().str.lower().str.replace(r'\s+', ' ', regex=True) df['city_clean'] = clean.map(city_map).fillna(clean.str.title()) print(df['city_clean'].value_counts())

=XLOOKUP(1, (A2:A500=G2) * (B2:B500=H2), C2:C500, "Not found") -- A = product, B = city, C = price -- G2 and H2 hold the product and city to find -- each condition returns TRUE/FALSE; multiplying gives 1 only where both match

top2 = ( df.groupby(['region', 'product'], as_index=False)['revenue'] .sum() .sort_values(['region', 'revenue'], ascending=[True, False]) .groupby('region') .head(2) ) print(top2)

// with YEAR(Order Date) in the view, Compute Using: Table (across) (ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1)) / ABS(LOOKUP(ZN(SUM([Sales])), -1)) // the built-in Quick Table Calculation 'Year over Year Growth' generates similar logic // format the field as a percentage

# Fill missing delivery_days with the median for that city df['delivery_days'] = ( df['delivery_days'] .fillna(df.groupby('city')['delivery_days'].transform('median')) ) # Rows in a city with no values at all stay NaN; fall back to overall median df['delivery_days'] = df['delivery_days'].fillna(df['delivery_days'].median())

WITH gaps AS ( SELECT customer_id, order_date, order_date - LAG(order_date) OVER ( PARTITION BY customer_id ORDER BY order_date) AS days_since_prev FROM orders ) SELECT customer_id, COUNT(*) AS orders, AVG(days_since_prev) AS avg_gap_days FROM gaps GROUP BY customer_id HAVING COUNT(*) > 1; -- order_date is DATE (PostgreSQL); use DATEDIFF in other databases

Achievement % = DIVIDE([Total Sales], [Sales Target]) KPI Colour = VAR a = [Achievement %] RETURN SWITCH( TRUE(), ISBLANK(a), "#9E9E9E", a >= 1, "#2E7D32", a >= 0.9, "#F9A825", "#C62828" ) -- apply KPI Colour via Format > Callout value > fx > Field value

SELECT s.campaign_id, COUNT(DISTINCT s.user_id) AS delivered, COUNT(DISTINCT o.user_id) AS opened, COUNT(DISTINCT c.user_id) AS clicked, ROUND(100.0 * COUNT(DISTINCT o.user_id) / COUNT(DISTINCT s.user_id), 1) AS open_rate, ROUND(100.0 * COUNT(DISTINCT c.user_id) / COUNT(DISTINCT s.user_id), 1) AS ctr FROM email_sends s LEFT JOIN email_opens o ON o.campaign_id = s.campaign_id AND o.user_id = s.user_id LEFT JOIN email_clicks c ON c.campaign_id = s.campaign_id AND c.user_id = s.user_id WHERE s.status = 'delivered' GROUP BY s.campaign_id;

SELECT col, COUNT(*) FROM t GROUP BY col HAVING COUNT(*) > 1; use ROW_NUMBER() partitioned by the key to delete extras.

-- PostgreSQL syntax CREATE TABLE dim_date AS SELECT TO_CHAR(d, 'YYYYMMDD')::INT AS date_key, d::DATE AS full_date, EXTRACT(YEAR FROM d) AS year, EXTRACT(QUARTER FROM d) AS quarter, EXTRACT(MONTH FROM d) AS month_no, TO_CHAR(d, 'Mon') AS month_name, TO_CHAR(d, 'Dy') AS weekday, EXTRACT(ISODOW FROM d) IN (6, 7) AS is_weekend FROM GENERATE_SERIES(DATE '2026-01-01', DATE '2026-12-31', INTERVAL '1 day') AS d;

from statsmodels.stats.power import NormalIndPower from statsmodels.stats.proportion import proportion_effectsize baseline = 0.04 target = baseline * 1.10 # 4.4% effect = proportion_effectsize(target, baseline) n = NormalIndPower().solve_power( effect_size=effect, alpha=0.05, power=0.8, alternative='two-sided' ) print(round(n)) # roughly 39,000 users per group