Ultimate Data Analyst Interview Roadmap: Prepare & Succeed!

Data Analyst Interview Challenge

1 / 44

What does an R-squared value of 1 indicate in regression?

No correlation

Perfect correlation

Weak correlation

None of the above

2 / 44

What is an outlier in data analysis?

The most frequent value

A data point significantly different from others

The median of the dataset

The first value in a dataset

3 / 44

What is the probability of getting heads in a fair coin toss?

0.25

0.50

0.75

1.00

4 / 44

What does standard deviation measure?

The average value of a dataset

The spread of data around the mean

The median value of a dataset

The sum of all values

5 / 44

What does df['column'].value_counts() return?

The sum of values in the column

The frequency of unique values in the column

The standard deviation of the column

The unique values in the column

6 / 44

Which library is primarily used for numerical operations in Python?

Pandas

NumPy

Matplotlib

Seaborn

7 / 44

What will df.dropna() do?

Remove all duplicate rows

Remove rows with NULL values

Replace NULL values with 0

Delete the DataFrame

8 / 44

What does df.loc[2] return?

The third row of the DataFrame

The second row of the DataFrame

The third column of the DataFrame

The first column of the DataFrame

9 / 44

How do you merge two Pandas DataFrames on a common column?

df1.append(df2)

df1.merge(df2, on='column_name')

df1.concat(df2, axis=1)

df1.join(df2)

10 / 44

What is the default index of a Pandas DataFrame?

Starts at 0 and increments by 1

Uses the first column as an index

Uses random values

None of the above

11 / 44

What does df.describe() do in Pandas?

Displays summary statistics of numerical columns

Displays the first 5 rows

Returns data types of all columns

Renames the DataFrame columns

12 / 44

Which function is used to read a CSV file into a Pandas DataFrame?

pd.read_csv()

pd.import_csv()

pd.load_csv()

pd.csv_read()

13 / 44

How do you check for missing values in a Pandas DataFrame?

df.isnull()

df.checknull()

df.hasnull()

df.emptyvalues()

14 / 44

Which library is commonly used for data manipulation in Python?

NumPy

Pandas

Matplotlib

Scikit-learn

15 / 44

What is a foreign key in SQL?

A unique identifier for a record

A key that uniquely identifies a table

A key that establishes a relationship between two tables

A temporary key for indexing

16 / 44

How do you fetch the top 10 rows from a SQL table in MySQL?

LIMIT 10

TOP 10

FETCH FIRST 10 ROWS ONLY

ROWNUM <= 10

17 / 44

What is the primary purpose of an index in SQL?

Increase storage size

Speed up queries

Prevent duplicate records

Ensure referential integrity

18 / 44

What does the SQL UNION operator do?

Combines columns from multiple tables

Merges rows from two tables while removing duplicates

Joins tables based on a common column

Sorts the table in descending order

19 / 44

Which of the following JOINs will return only matching records between two tables?

LEFT JOIN

RIGHT JOIN

INNER JOIN

FULL JOIN

20 / 44

Which SQL function returns the number of records in a table?

COUNT()

SUM()

AVG()

LENGTH()

21 / 44

Which SQL clause is used to filter records after aggregation?

DISTINCT

WHERE

HAVING

GROUP BY

22 / 44

What is the default order of ORDER BY in SQL?

Ascending

Descending

Random

None of the above

23 / 44

What does the SQL command GROUP BY do?

Filters rows based on a condition

Aggregates data by unique values in a column

Sorts the data

Deletes duplicate records

24 / 44

Which library is commonly used for creating static, animated, and interactive visualizations in Python?

Pandas

NumPy

Matplotlib

Scikit-learn

25 / 44

What function is used to create a line plot in Matplotlib?

plt.bar()

plt.scatter()

plt.plot()

plt.hist()

26 / 44

In Seaborn, which function is used to create a histogram with a density curve?

sns.barplot()

sns.histplot()

sns.scatterplot()

sns.boxplot()

27 / 44

What does the argument `figsize=(10,5)` do in Matplotlib?

Sets the color of the figure

Defines the width and height of the figure

Adjusts the resolution of the figure

Changes the style of the figure

28 / 44

Which Seaborn function is used to create a correlation heatmap?

sns.heatmap()

sns.lineplot()

sns.pairplot()

sns.boxplot()

29 / 44

Which argument in `plt.bar()` is used to set the bar width?

width

size

height

thickness

30 / 44

What is the purpose of `plt.show()` in Matplotlib?

To save the plot as an image

To clear the figure

To display the plot

To close the figure

31 / 44

Which library provides an easy interface to create aesthetically appealing statistical plots?

NumPy

Pandas

Seaborn

TensorFlow

32 / 44

In a scatter plot, what does increasing the `alpha` value do?

Makes points more transparent

Makes points less transparent

Increases the size of points

Changes the color of points

33 / 44

In a scatter plot, what does increasing the `alpha` value do?

Makes points more transparent

Makes points less transparent

Increases the size of points

Changes the color of points

34 / 44

What type of plot is best for visualizing the distribution of a numerical variable?

Scatter Plot

Histogram

Bar Chart

Line Chart

35 / 44

A company wants to analyze customer churn. Which type of analysis would be most useful?

Descriptive Analytics

Predictive Analytics

Diagnostic Analytics

Prescriptive Analytics

36 / 44

You are given a dataset with 1 million rows and many missing values. What should you do first?

Delete all rows with missing values B) Replace missing values with the column mean or median

Replace missing values with the column mean or median

Analyze the pattern of missing data before deciding

Ignore missing values and proceed with analysis

37 / 44

Your manager asks for insights on sales trends over the last five years. Which visualization is most appropriate?

Scatter plot

Bar chart

Line chart

Pie chart

38 / 44

A dataset contains transaction details for an e-commerce company. Which KPI would best measure customer loyalty?

Average Order Value (AOV)

Conversion Rate

Customer Lifetime Value (CLV)

Bounce Rate

39 / 44

You are analyzing a marketing campaign and need to measure its impact on sales. What statistical test should you use?

ANOVA

Chi-square test

A/B Testing (t-test)

Correlation Analysis

40 / 44

Your team is working with a dataset that has duplicate records. What is the best approach to handle them?

Keep all duplicates for better accuracy

Identify and analyze the reason for duplication before removing

Remove duplicates without investigating

Convert duplicates into separate categories

41 / 44

A retailer wants to segment customers based on purchasing behavior. Which technique should be used?

Regression Analysis

Clustering (K-Means)

Time Series Forecasting

Decision Trees

42 / 44

Your company wants to predict next month’s revenue based on past data. Which model is best suited for this task?

Classification Model

Time Series Forecasting Model

Clustering Model

Sentiment Analysis Model

43 / 44

You have an extremely large dataset, and your current processing method is slow. What should you do?

Use SQL instead of Python for analysis

Optimize queries and leverage parallel computing

Reduce dataset size by removing random rows

Switch to Excel for better performance

44 / 44

You are presenting data insights to executives with no technical background. What is the best approach?

A) Use complex data tables for accuracy

Show raw data for transparency

Focus on storytelling with clear, simple visualizations

Use only technical terms and avoid simplifications

Your score is

The average score is 0%

Data Analyst Interview Preparation Steps

Step 1: Understand the Role & Requirements

Research Job Descriptions – Identify key skills required (SQL, Python, Excel, Data Visualization, etc.)
Know the Industry – Understand domain-specific requirements (e.g., finance, healthcare, e-commerce)

Useful Resources :

Step 2: Master the Core Technical Skills

SQL (Structured Query Language)

Basic queries: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
Joins (INNER, LEFT, RIGHT, SELF, CROSS)
Window functions (ROW_NUMBER(), RANK(), LEAD(), LAG())
Optimizing queries and handling large datasets

Python for Data Analysis

Data manipulation (Pandas, NumPy)
Data visualization (Matplotlib, Seaborn)
Working with APIs, JSON, and CSV files
Basic automation and scripting

Excel & Spreadsheets

Pivot Tables, VLOOKUP, INDEX-MATCH
Data cleaning & transformation
Basic automation with VBA

Data Visualization Tools

Tableau / Power BI basics
Creating interactive dashboards
Choosing the right chart types for data storytelling

Statistics & Probability

Measures of central tendency (Mean, Median, Mode)
Hypothesis testing, A/B Testing
Probability distributions (Normal, Poisson, Binomial)

Useful Resources :

Step 3: Solve Real-World Problems & Case Studies

Practice SQL Challenges – Use platforms like LeetCode, StrataScratch, and Mode Analytics
Analyze Sample Datasets – Kaggle datasets, Google Data Analytics Capstone Projects
Work on Case Studies – Examples: Sales Analysis, Customer Retention, Marketing Campaign Analysis

Step 4: Prepare for Behavioral & Business Questions

Common Questions:

Tell me about yourself
Why do you want to be a Data Analyst?
Describe a time you solved a complex problem with data.
How do you handle missing data in a dataset?

STAR Method – Structure your answers: Situation, Task, Action, Result
Business Understanding – Learn how data drives business decisions

Step 5: Mock Interviews & Resume Optimization

Mock Interviews – Practice on platforms like Pramp or Interviewing.io
Resume Optimization – Highlight projects, impact, and quantifiable results
LinkedIn & Portfolio – Share case studies & interactive dashboards

Step 6: Final Interview Preparation & Mindset

Review Past Interview Questions – Study common patterns in companies (e.g., Google, Amazon, Meta) or companies you want to target.
Be Ready to Explain Your Thought Process – Interviewers want to see structured thinking.
Stay Confident & Ask Questions – Show curiosity about the company and role.

Ultimate Data Analyst Interview Roadmap: Prepare & Succeed!

Data Analyst Interview Challenge

What function is used to create a line plot in Matplotlib?

In Seaborn, which function is used to create a histogram with a density curve?

What does the argument `figsize=(10,5)` do in Matplotlib?

Which Seaborn function is used to create a correlation heatmap?

Which argument in `plt.bar()` is used to set the bar width?

What is the purpose of `plt.show()` in Matplotlib?

Which library provides an easy interface to create aesthetically appealing statistical plots?

In a scatter plot, what does increasing the `alpha` value do?

In a scatter plot, what does increasing the `alpha` value do?

What type of plot is best for visualizing the distribution of a numerical variable?

Your manager asks for insights on sales trends over the last five years. Which visualization is most appropriate?

A dataset contains transaction details for an e-commerce company. Which KPI would best measure customer loyalty?

You are analyzing a marketing campaign and need to measure its impact on sales. What statistical test should you use?

Your team is working with a dataset that has duplicate records. What is the best approach to handle them?

A retailer wants to segment customers based on purchasing behavior. Which technique should be used?

Your company wants to predict next month’s revenue based on past data. Which model is best suited for this task?

You have an extremely large dataset, and your current processing method is slow. What should you do?

Data Analyst Interview Preparation Steps

Step 1: Understand the Role & Requirements

Step 2: Master the Core Technical Skills

Step 3: Solve Real-World Problems & Case Studies

Step 4: Prepare for Behavioral & Business Questions

Step 5: Mock Interviews & Resume Optimization

Step 6: Final Interview Preparation & Mindset

Click the below essential resources to ace your interview

Interviewbit

DataInterview.com

Learn Python For AI

Playlist of Python

Learn Statistics or collection of data in statistics

Learn Data Visualization

Career in AI 2025

Ultimate Data Analyst Interview Roadmap: Prepare & Succeed!

Data Analyst Interview Challenge

What function is used to create a line plot in Matplotlib?

In Seaborn, which function is used to create a histogram with a density curve?

What does the argument figsize=(10,5) do in Matplotlib?

Which Seaborn function is used to create a correlation heatmap?

Which argument in plt.bar() is used to set the bar width?

What is the purpose of plt.show() in Matplotlib?

Which library provides an easy interface to create aesthetically appealing statistical plots?

In a scatter plot, what does increasing the alpha value do?

In a scatter plot, what does increasing the alpha value do?

What type of plot is best for visualizing the distribution of a numerical variable?

Your manager asks for insights on sales trends over the last five years. Which visualization is most appropriate?

A dataset contains transaction details for an e-commerce company. Which KPI would best measure customer loyalty?

You are analyzing a marketing campaign and need to measure its impact on sales. What statistical test should you use?

Your team is working with a dataset that has duplicate records. What is the best approach to handle them?

A retailer wants to segment customers based on purchasing behavior. Which technique should be used?

Your company wants to predict next month’s revenue based on past data. Which model is best suited for this task?

You have an extremely large dataset, and your current processing method is slow. What should you do?

Data Analyst Interview Preparation Steps

Step 1: Understand the Role & Requirements

Step 2: Master the Core Technical Skills

Step 3: Solve Real-World Problems & Case Studies

Step 4: Prepare for Behavioral & Business Questions

Step 5: Mock Interviews & Resume Optimization

Step 6: Final Interview Preparation & Mindset

Click the below essential resources to ace your interview

Interviewbit

DataInterview.com

Learn Python For AI

Playlist of Python

Learn Statistics or collection of data in statistics

Learn Data Visualization

Career in AI 2025

What does the argument `figsize=(10,5)` do in Matplotlib?

Which argument in `plt.bar()` is used to set the bar width?

What is the purpose of `plt.show()` in Matplotlib?

In a scatter plot, what does increasing the `alpha` value do?

In a scatter plot, what does increasing the `alpha` value do?