Data Analyst interview prep

Short answer: The mean is the sum divided by the count and is sensitive to outliers. The median is the middle value of the ordered data and is robust to outliers. The mode is the most frequent value. For skewed data the median better reflects the "typical" observation.

In depth:

1. Mean — good for symmetric data and downstream math (variance, regression), but a single outlier shifts it.
2. Median — the 50th percentile; splits the data in half and barely reacts to extremes.
3. Mode — the only one of the three that works for categorical data ("most common plan").

Metric	When to use	Outlier sensitivity
Mean	symmetric data, downstream calculations	high
Median	skewed data: income, latency, prices	low
Mode	categorical data, most frequent value	low

⚠️ Common mistake: reporting the mean for income or latency. A long right tail drags the mean upward, so it no longer describes the typical user — use the median.

Short answer: An A/B test is a randomized experiment: users are split at random into control and treatment, shown different versions, and compared on a single pre-chosen primary metric under a fixed decision rule.

In depth:

1. Hypothesis — what you change, which metric it should move, and why.
2. Metric and MDE — one primary metric plus a minimum detectable effect (MDE).
3. Randomization — a random, stable split into control/treatment.
4. Sample size and duration — compute and fix them before launch.
5. Run to N — run until the sample is reached, without peeking at interim results.
6. Analysis — compare the metric, compute the p-value and the confidence interval of the lift.
7. Decision — ship or roll back per the pre-defined rule.

Hypothesis → Randomize → Run to N → Analyze → Decision
              │                                  │
       control / treatment              ship ✓ / rollback ✗

⚠️ Common mistake: changing the UI mid-test or stopping the moment it first looks 'significant' — both break the conclusions.

Short answer: A good metric is measurable, comparable, understandable and hard to game, and above all actionable: it can drive a decision. A KPI is the same kind of metric but tied to a goal and a target value for a period; a KPI is a subset of metrics, not a synonym.

In depth:

1. Actionable, not vanity — a metric should change someone's decision. If the number goes up but nobody knows what to do, it's a vanity metric.
2. Comparability — prefer rates and ratios (conversion, retention %) over raw counts: they compare across periods and segments.
3. Hard to game — ask "how could this be inflated without creating value?". "Clicks" are easy to game; "activated users" are harder.
4. A precise definition — one numerator, one denominator, a fixed window and filters, or two teams will count it differently.

Property	Metric	KPI
What it is	any measurable number	a metric with a goal
Tied to a target	not necessarily	always (target)
How many	many	a chosen few
Example	time on page	activation rate ≥ 40% by Q3

⚠️ Common mistake: calling everything a KPI. KPIs are the 3–5 metrics a team is actually judged on; the rest are diagnostic metrics.

Short answer: The chart type is dictated not by taste but by the kind of relationship in the data: comparing categories — bars, change over time — a line, correlation between two variables — a scatter plot, share of a few parts in a whole — a pie. When torn between a pie and anything else, almost always pick bars.

In depth:

First state which relationship you are showing, and only then choose the visual:

Relationship in data	Chart type	Why
Comparing categories	Bar	the eye compares length precisely; axis must start at 0
Change over time	Line	a line emphasizes trend and continuity
Correlation of X and Y	Scatter	reveals the cloud, clusters and outliers
Parts of a whole	Pie / stacked	only for 2–4 parts, summing to 100%
Distribution	Histogram / boxplot	shows shape, spread, tails

• Bars — the workhorse for categories; the Y axis starts at zero, otherwise the height difference lies.
• Line — only for an ordered continuous axis (time, days); never connect unrelated categories with a line.
• Scatter — for "are X and Y related"; add a trend line if needed.

⚠️ Common mistake: a pie chart with 8 slices — the shares become indistinguishable; replace it with horizontal bars sorted by value.

Short answer: A Series is a one-dimensional labelled array (a single column); a DataFrame is a two-dimensional table of column-Series sharing a row index. You load data with pd.read_csv() / pd.read_sql() and inspect it with .head(), .info(), .describe(), .dtypes, .shape.

In depth:

1. Series vs DataFrame — df['col'] returns a Series; df[['col']] (double brackets) returns a one-column DataFrame. Both carry an .index.
2. Loading — read_csv (file/URL), read_sql (a SQL query plus a connection), read_parquet (fast columnar format).
3. Inspecting — .head()/.tail() peek at the edges, .info() shows dtypes and null counts, .describe() gives stats on numeric columns, .dtypes lists types, .shape gives size.

import pandas as pd

df = pd.read_csv("sales.csv", parse_dates=["order_date"])
df.shape          # (10000, 8) — rows, columns
df.info()         # column dtypes and non-null counts
df.describe()     # count/mean/std/min/quartiles/max for numerics
df["revenue"].head()   # Series — a single column

⚠️ Common mistake: forgetting parse_dates= on load — the date column stays object (a string), so date arithmetic and resample() won't work.