This online course data analytics interview Q&A guide covers 20 essential beginner to moderate-level questions, helping learners strengthen their foundation in analytics concepts, tools, and techniques. From structured vs. unstructured data to SQL joins, dashboards, and A/B testing, each answer provides clear explanations with real-world examples. Designed to highlight how data analytics supports decision-making and trend analysis, this guide is perfect for freshers, career switchers, and professionals preparing for analytics roles through an Online course data analytics program.
What is data analytics?
Answer:
Data analytics is the process of examining raw data to identify patterns, trends, and insights that can help make better business decisions.
Example: An e-commerce company uses data analytics to determine the best-selling products during the holiday season.
What is the difference between structured and unstructured data?
Answer:
- Structured data: Organized in rows and columns (e.g., SQL databases).
- Unstructured data: No predefined format (e.g., images, videos, social media posts).
Example: Customer names in a database are structured; customer reviews are unstructured.
Data analytics techniques differ for structured and unstructured datasets.
What is the role of a data analyst?
Answer:
A data analyst collects, processes, and analyzes data to generate actionable insights, create reports, and support decision-making.
Example: Using data analytics to measure marketing campaign ROI and recommend improvements.
What is a KPI?
Answer:
A Key Performance Indicator is a measurable value that shows how effectively an organization is achieving specific objectives.
Example: Customer retention rate for a subscription business can be tracked through data analytics dashboards.
What is data cleaning, and why is it important?
Answer:
Data cleaning involves identifying and correcting errors, inconsistencies, and missing values in datasets to improve accuracy.
Example: Removing duplicate customer records in a CRM before applying data analytics.
Explain the difference between descriptive, diagnostic, predictive, and prescriptive analytics.
Answer:
- Descriptive: Summarizes historical data (e.g., monthly sales reports).
- Diagnostic: Explains why something happened (e.g., identifying reasons for a sales drop).
- Predictive: Uses data to forecast future trends.
- Prescriptive: Suggests actions to achieve desired outcomes.
Data analytics covers all these types to support informed decision-making.
What is a data pipeline?
Answer:
A sequence of processes that extract data from sources, transform it, and load it into a destination (ETL: Extract, Transform, Load).
Example: Pulling sales data from an e-commerce platform, cleaning it, and storing it in a data warehouse for analytics.
How would you handle missing values in a dataset?
Answer:
- Remove rows with missing values (if small in number).
- Replace with mean, median, or mode.
- Use predictive modeling to estimate values.
Example: Replacing missing product prices with the category’s average before performing data analytics.
What is correlation, and how is it different from causation?
Answer:
- Correlation: Two variables move together (positively or negatively).
- Causation: One variable directly affects another.
Example: Ice cream sales and beach visits are correlated, but temperature is the cause.
What tools and languages are commonly used in data analytics?
Answer:
- Languages: SQL, Python, R.
- Tools: Excel, Power BI, Tableau, Google Data Studio.
Example: Using SQL for querying a database and Tableau for visualizing sales trends in a data analytics project.
What is the difference between OLTP and OLAP systems?
Answer:
- OLTP (Online Transaction Processing): Handles day-to-day transactions.
- OLAP (Online Analytical Processing): Used for analysis and reporting.
OLAP systems are a core part of large-scale data analytics.
What is a dashboard in data analytics?
Answer:
A dashboard is a visual interface that displays key metrics, KPIs, and trends in real-time or over a set period.
Example: A marketing dashboard showing daily ad spend, impressions, and conversions.
What is data normalization in databases?
Answer:
The process of organizing data to reduce redundancy and improve data integrity.
Example: Storing customer addresses in a separate table instead of repeating them in every sales record.
How do you determine if a dataset is biased?
Answer:
By checking for missing representation of certain groups, skewed data distributions, or overrepresentation of specific categories.
Bias in datasets can lead to inaccurate data analytics results.
What is the difference between INNER JOIN and LEFT JOIN in SQL?
Answer:
- INNER JOIN: Returns only rows with matching values in both tables.
- LEFT JOIN: Returns all rows from the left table and matching rows from the right table.
What is the difference between a histogram and a bar chart?
Answer:
- Histogram: Shows frequency distribution of continuous data.
- Bar Chart: Compares categorical data.
Both can be used in data analytics for different purposes.
How do you identify outliers in a dataset?
Answer:
- Statistical methods: Z-score, IQR method.
- Visual methods: Box plots, scatter plots.
Example: Using IQR to detect unusually high purchase amounts before performing analytics.
What is A/B testing?
Answer:
A statistical method to compare two versions (A and B) to determine which performs better.
Example: Testing two different website layouts and analyzing the results with data analytics.
How do you choose between mean and median as a measure of central tendency?
Answer:
- Use mean when the data is normally distributed without extreme outliers.
- Use the median when the data is skewed or has outliers.
What is the difference between supervised and unsupervised learning in data analysis?
Answer:
- Supervised learning: Uses labeled data to train models.
- Unsupervised learning: Finds hidden patterns in unlabeled data.
