Course5 Intelligence Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Course5 Intelligence hiring for Data Analytics roles
Course5 Intelligence is a Mumbai-based analytics and AI company that offers market research, digital analytics, customer analytics and AI-driven insights to global brands. It works with clients in technology, retail, consumer goods, media and pharma. In India it hires data analysts, research analysts, data scientists and engineers who turn survey, digital and business data into insights.
For Data Analytics roles, this Course5 Intelligence practice set focuses most on Statistics and Case Studies (9), Data Modelling & Warehousing (5), SQL (5), Data Cleaning (4). It has 9 easy, 26 medium, 5 hard questions, so you can start easy and work up to the harder ones.
Company type
Analytics
Headquarters
Mumbai, India
Main India offices
Mumbai, Bengaluru
Course5 Intelligence Data Analytics interview process
Online assessment (aptitude + analytics basics)
Technical interview
Case or managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Average delivery time improved by 4 minutes, but customer complaints about late deliveries went up. How do you investigate?
An average can improve while the tail gets worse. I would look at the distribution: median, 90th and 95th percentiles and the share of orders beyond the promised time. Then break it down by city, time slot and restaurant to see where late orders cluster. Also check whether promised times changed, whether complaint tracking changed and whether a few very fast orders pulled the mean down.
02
Write Python code to extract valid 10-digit Indian mobile numbers from a messy text column.
import re
pattern = re.compile(r'(?:\+?91[\s-]?|0)?([6-9]\d{9})')
def get_mobile(text):
if not isinstance(text, str):
return None
digits = re.sub(r'[\s()-]', '', text)
m = pattern.search(digits)
return m.group(1) if m else None
df['mobile'] = df['contact'].apply(get_mobile)
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.
04
How do you split a full name column into first and last name in Excel?
-- A2 = 'Priya Ramesh Iyer'
-- first name
=TEXTBEFORE(TRIM(A2), " ")
-- last name (text after the last space)
=TEXTAFTER(TRIM(A2), " ", -1)
-- older Excel: first name
=LEFT(TRIM(A2), FIND(" ", TRIM(A2)&" ")-1)
-- or use Data > Text to Columns or Flash Fill (Ctrl+E) for a one-off task
05
Sales dropped 20% last month. How will you analyse it?
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.
06
Tell me about a time you worked with a marketing team to improve a campaign using data.
Situation: a lead generation campaign had a high cost per lead and sales said leads were poor. Task: find what to change. Action: I joined ad data with CRM outcomes and found two audience segments produced most leads but almost no closed deals, while one smaller segment converted well. Result: we moved budget to that segment and changed the form questions; cost per qualified lead fell about 30% in six weeks.
First check whether it is a tracking problem: did the analytics tag break on some templates, did consent settings change, does server log or backend order data show the same fall? If the drop is real, check the search console for impressions and rankings, recent site releases such as changed URLs, missing redirects, noindex tags or robots.txt changes, and whether a search algorithm update or seasonal pattern matches. Then break the drop down by page group.
A natural key comes from the source system and has business meaning, such as a customer mobile number or product SKU. A surrogate key is a meaningless integer or hash generated in the warehouse. Surrogate keys protect the model when source keys change, get reused or clash across systems, and they are essential for slowly changing dimensions where one customer has several historical rows. Keep the natural key as an attribute for lookups.
12
What is an action title and why should you use one?
An action title states the insight of a chart or slide as a sentence instead of a label. Instead of Monthly Sales by Region, write South region drove 70 percent of the decline since July. Busy executives may read only the titles, so each one should carry the message and, ideally, the so-what. The chart below then proves the title. Read only your titles in order: they should tell the whole story.
A conformed dimension is one dimension table with the same keys, attributes and meanings shared by several fact tables. For example, one date and one product dimension used by sales, returns and inventory facts. Because they conform, you can compare sales and returns by the same product category in one report without mismatched definitions. Conformed dimensions are the main way an enterprise warehouse stays consistent across departments.
COALESCE returns the first non-null value from its arguments, for example COALESCE(phone, alt_phone, 'Not given') fills missing phone numbers. NULLIF(a, b) returns NULL when a equals b, otherwise a. Its classic use is avoiding division by zero: revenue / NULLIF(orders, 0) returns NULL instead of an error when orders is zero. Together they handle most null-related reporting problems cleanly.
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.
A chi-square test of independence checks whether two categorical variables are related, using a contingency table of counts. Example: is preferred payment method related to city tier? It compares observed counts with the counts expected if there were no relationship. Use it with counts, not percentages, and make sure expected counts are not too small, roughly at least 5 per cell. A significant result shows association, not causation.
17
What is the Central Limit Theorem and why does it matter for analysts?
The Central Limit Theorem says that the distribution of sample means approaches a normal distribution as sample size grows, usually from about 30 observations, even if the underlying data is skewed. This is why we can use z-tests, t-tests and confidence intervals for averages such as order value or session time. It does not make the raw data normal, and very heavy-tailed data may need larger samples.
18
You loaded a month of sales into the warehouse. How do you check the load is complete and correct?
Reconcile against the source: compare row counts, total amount, and counts by day and by store between source and target. Check for duplicate keys, nulls in mandatory columns, and dates outside the expected month. Compare with the previous month to spot big unexplained jumps. Spot-check a few individual transactions end to end. Automate these checks so each future load produces a small pass or fail report.
19
What is the difference between a live connection and an extract in Tableau?
A live connection queries the source database every time a user interacts, so data is always current but performance depends on the database. An extract is a compressed snapshot stored in Tableau's hyper format, usually much faster, refreshed on a schedule and usable offline. Use live for near real-time needs on a fast warehouse, and extracts for speed, slow sources or when you want to reduce load on production systems.
20
Write an Excel formula to look up a price using two criteria, product and city.
=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
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.
Incrementality testing measures sales that happened because of a campaign, compared with what would have happened anyway. You hold out a random group of users or a set of matched cities from the campaign and compare their conversions with the exposed group. Attribution tools often over-credit channels like retargeting and brand search, which reach people already likely to buy, so incrementality tests show the true lift and the true cost per incremental sale.
MERGE INTO dim_product t
USING stg_product s
ON t.product_id = s.product_id
WHEN MATCHED AND (t.name <> s.name OR t.category <> s.category) THEN
UPDATE SET name = s.name,
category = s.category,
updated_at = CURRENT_TIMESTAMP
WHEN NOT MATCHED THEN
INSERT (product_id, name, category, updated_at)
VALUES (s.product_id, s.name, s.category, CURRENT_TIMESTAMP);
25
How do you pivot rows into columns in SQL, for example sales by quarter as columns?
SELECT region,
SUM(CASE WHEN quarter = 'Q1' THEN amount ELSE 0 END) AS q1,
SUM(CASE WHEN quarter = 'Q2' THEN amount ELSE 0 END) AS q2,
SUM(CASE WHEN quarter = 'Q3' THEN amount ELSE 0 END) AS q3,
SUM(CASE WHEN quarter = 'Q4' THEN amount ELSE 0 END) AS q4
FROM sales
GROUP BY region;
-- conditional aggregation works in every database; some also offer PIVOT
26
Tell me about a time your analysis was challenged in a meeting.
Situation: while presenting regional sales, a manager said my numbers were wrong because his team's tracker showed more. Task: handle it without derailing the meeting. Action: I acknowledged the concern, explained my source and definition briefly, and offered to reconcile after the meeting. That afternoon we found his tracker counted orders before cancellations. Result: we agreed on net orders as the standard, and he later asked me to rebuild his tracker.
A flat table is quick to start and fine for a small one-off analysis. But at scale it bloats the model because repeated text attributes compress poorly, makes DAX filters and time intelligence harder, and breaks when you add a second fact like targets or returns. I would show them a star schema with one fact and a few dimensions, compare model size and measure speed, and let the results convince them.
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.
These are late-arriving dimensions. Do not drop the orders, or revenue will be understated. Common practice is to insert a placeholder customer row with the natural key and 'Unknown' attributes, link the order to it, and update that row when full details arrive. Alternatively map to a single Unknown member with key -1. Also monitor how many such rows appear daily, because a spike usually means a broken feed.
35
What is a percentile and what is the interquartile range?
A percentile is the value below which a given percentage of data falls; the 90th percentile delivery time means 90 percent of deliveries were faster. The interquartile range is the 75th percentile minus the 25th percentile, the spread of the middle half. IQR is robust to outliers, and values beyond 1.5 times the IQR from the quartiles are often flagged as outliers in box plots.
The normal distribution is a symmetric, bell-shaped distribution described by its mean and standard deviation. The empirical rule says about 68% of values fall within one standard deviation of the mean, 95% within two and 99.7% within three. For example, if delivery times average 30 minutes with an SD of 5, about 95% arrive in 20 to 40 minutes. Many business metrics are skewed, so check before assuming normality.
38
How do you make sure a metric like active customer means the same thing across all teams?
Write a clear definition in a shared metric dictionary: formula, filters, time window, grain, owner and source table, for example a customer with at least one paid order in the last 90 days. Implement it once, in a governed dataset or semantic layer, and have all dashboards reuse it rather than recalculating. Version changes, announce them and show the definition in the dashboard tooltip. Review new metrics before they are published.
Tidy data follows three rules: each variable is a column, each observation is a row, and each type of observational unit is a table. A sheet with months as columns is not tidy; unpivoting it to Region, Month and Sales columns makes it tidy. Tidy data is easy to filter, group, join and chart in pandas, SQL, Power BI and Tableau, so reshaping into this form is a core cleaning step.
40
Write Excel formulas to calculate ROAS and CPA for each campaign and flag campaigns below a ROAS target.
-- B = Spend, C = Conversions, D = Revenue, target ROAS in H1
E2 (ROAS): =IFERROR(D2/B2, 0)
F2 (CPA): =IF(C2=0, "No conversions", B2/C2)
G2 (Flag): =IF(E2<$H$1, "Below target", "OK")
-- Copy down, then add conditional formatting on G to highlight 'Below target'
Tips to crack the Course5 Intelligence Data Analytics interview
Revise Excel, SQL and a visualisation tool such as Power BI or Tableau, which are widely used in analytics roles.
Learn basic market research concepts like surveys, sampling and segmentation if you apply for research and insights positions.
Practise presenting a short insight story from a dataset, explaining what the numbers mean for a business decision.
Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
Practise the hard questions on this page aloud; Course5 Intelligence interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Course5 Intelligence Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + analytics basics); Technical interview; Case or managerial round; HR round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Course5 Intelligence Data Analytics interview?
This practice set leans on Statistics and Case Studies (9), Data Modelling & Warehousing (5), SQL (5), Data Cleaning (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Course5 Intelligence interviewers ask you to walk through real work.
Are these the exact questions asked at Course5 Intelligence?
No. These are curated practice questions that match the skills and difficulty usually tested in analytics interviews like Course5 Intelligence's. They are not a leaked or verified list of past questions.
Where does Course5 Intelligence hire Data Analytics professionals in India?
Course5 Intelligence has large teams in Mumbai, Bengaluru. Openings and locations change often, so check the Course5 Intelligence careers page and filter by city and experience.