Data analyst interviews in 2026 are not only about knowing Excel formulas or writing SQL queries.
Companies now want candidates who can understand messy data, ask the right business questions, clean datasets, build dashboards, explain trends, and use AI tools without blindly trusting them.
That means your interview preparation needs to be practical.
You should know SQL, Excel, Power BI, Python, statistics, business thinking, and storytelling. But more importantly, you should know how to explain your answer like a real analyst, not like someone reading from a textbook.
This guide covers the top data analyst interview questions and answers for 2026, including technical questions, scenario-based questions, HR questions, fresher questions, and real-world case-style questions.
Why Data Analyst Interviews Are Different in 2026
Earlier, many data analyst interviews focused mainly on Excel, charts, and basic SQL.
Now the role has changed.
Data analysts are expected to work with larger datasets, automation tools, BI dashboards, cloud databases, Python notebooks, and AI-powered analytics platforms.
Recruiters are also testing whether candidates can think beyond tools.
They may ask:
- Why did sales drop?
- Which metric would you track?
- How would you clean this dataset?
- What would you do if the dashboard number does not match the source data?
- How would you explain this trend to a non-technical manager?
So, if you are preparing for a data analyst interview in 2026, focus on three things: technical clarity, business logic, and communication.
What Skills Are Tested in a Data Analyst Interview?
Most data analyst interviews test a combination of technical and analytical skills.
You may not need to be an expert in every tool, but you should be confident in the basics.
1. Core skills usually tested
- SQL for querying databases.
- Excel for cleaning, formulas, pivots, and quick analysis.
- Power BI or Tableau for dashboards and reporting.
- Python for data cleaning, analysis, and automation.
- Statistics for interpreting data correctly.
- Business understanding for solving real company problems.
- Communication for explaining insights clearly.
2. Skills that can make you stand out in 2026
- Knowledge of AI tools for analytics.
- Basic understanding of data pipelines.
- Experience with cloud platforms like BigQuery, Snowflake, Azure, or AWS.
- Dashboard storytelling.
- A/B testing basics.
- Predictive analytics basics.
- Portfolio projects with business impact.
Top Data Analyst Interview Questions and Answers 2026
Below are the most important data analyst interview questions with human-style answers.
Use these answers as a base. Do not memorize them word-for-word. Personalize them with your own projects, tools, and experience.
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Basic Data Analyst Interview Questions
1. Tell me about yourself.
A good answer should be short, structured, and relevant to the role.
Do not tell your full life story. Focus on your education, skills, projects, internships, tools, and why you are interested in data analytics.
Sample answer
I have a background in data analysis with hands-on experience in SQL, Excel, Power BI, and Python. I have worked on projects involving data cleaning, dashboard creation, trend analysis, and business reporting.
One of my projects involved analyzing customer behavior and identifying key patterns that could help improve retention. I enjoy working with data because it helps convert raw information into clear business decisions.
I am now looking for a data analyst role where I can use my analytical skills, improve reporting processes, and support teams with data-backed insights.
2. What does a data analyst do?
A data analyst collects, cleans, analyzes, and interprets data to help a business make better decisions.
The role is not just about creating reports. A good data analyst understands the problem, checks data quality, finds patterns, creates dashboards, and explains insights in simple language.
Sample answer
A data analyst helps a company understand what is happening in the business through data. Their work includes collecting data, cleaning it, analyzing trends, creating reports or dashboards, and sharing insights with stakeholders.
For example, if sales drop, a data analyst may check product performance, region-wise sales, customer segments, discounts, and time-based trends to find the reason behind the decline.
3. Why do you want to become a data analyst?
This question checks your motivation.
Avoid saying only “because data analytics has good salary.” That sounds weak.
Connect your answer with problem-solving, business impact, and interest in data.
Sample answer
I want to become a data analyst because I enjoy solving problems using evidence instead of assumptions. Data analytics allows me to work with numbers, identify patterns, and explain what those patterns mean for the business.
I also like that the role connects technical skills with business decision-making. It gives me the opportunity to use tools like SQL, Excel, Python, and Power BI while also working closely with teams to solve real problems.
4. What is the difference between data analysis and data analytics?
Data analysis is the process of examining data to answer a specific question.
Data analytics is a broader field that includes collecting, cleaning, analyzing, visualizing, and using data to support decisions.
Sample answer
Data analysis is more focused on studying data to find answers. For example, analyzing last month’s sales to understand why revenue dropped.
Data analytics is broader. It includes the complete process of handling data, finding patterns, creating dashboards, predicting trends, and supporting decision-making.
So, data analysis is a part of data analytics.
5. What is the difference between a data analyst and a data scientist?
A data analyst mainly focuses on understanding past and current business performance.
A data scientist usually works more with predictive modeling, machine learning, algorithms, and advanced statistical methods.
Sample answer
A data analyst focuses on cleaning data, creating reports, building dashboards, and explaining business trends. The goal is usually to answer what happened and why it happened.
A data scientist goes deeper into predictive modeling and machine learning. They may build models to predict future outcomes, such as customer churn, fraud risk, or demand forecasting.
There is some overlap, but the data scientist role is usually more model-heavy.
SQL Interview Questions for Data Analysts
SQL is one of the most important skills for any data analyst interview.
If you are applying for data analyst roles, you should be comfortable with joins, group by, where, having, subqueries, window functions, case statements, and date functions.
6. What is SQL and why is it important for data analysts?
SQL stands for Structured Query Language.
It is used to store, manage, and retrieve data from relational databases.
Sample answer
SQL is important because most business data is stored in databases. As a data analyst, I need SQL to extract relevant data, filter records, join tables, aggregate results, and prepare datasets for reporting or analysis.
For example, if I want to analyze monthly sales by region, I can use SQL to pull only the required columns, group data by region and month, and calculate total revenue.
7. What is the difference between WHERE and HAVING?
WHERE filters rows before aggregation.
HAVING filters grouped results after aggregation.
Sample answer
WHERE is used to filter individual rows before grouping. HAVING is used to filter results after using GROUP BY.
For example, if I want only orders from 2025, I use WHERE. But if I want customers whose total purchase amount is more than 50,000, I use HAVING after grouping by customer.
8. What are different types of SQL joins?
The main types of joins are INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.
Sample answer
INNER JOIN returns only matching records from both tables.
LEFT JOIN returns all records from the left table and matching records from the right table.
RIGHT JOIN returns all records from the right table and matching records from the left table.
FULL OUTER JOIN returns all records from both tables, whether they match or not.
In data analytics, LEFT JOIN is commonly used when we want to keep all records from the main table and bring matching information from another table.
9. What is the difference between INNER JOIN and LEFT JOIN?
INNER JOIN returns only rows that match in both tables.
LEFT JOIN returns all rows from the left table, even if there is no match in the right table.
Sample answer
If I have a customer table and an order table, INNER JOIN will show only customers who placed orders.
LEFT JOIN will show all customers, including those who have not placed any orders.
This is useful when we want to identify inactive customers or customers with no transactions.
10. What is GROUP BY in SQL?
GROUP BY is used to group rows that have the same values and calculate summary metrics.
Sample answer
GROUP BY is used when we want aggregated results. For example, if I want total sales by city, I can group the data by city and use SUM on the sales column.
It helps convert transaction-level data into summary-level insights.
11. What is a window function in SQL?
A window function performs calculations across a set of rows while keeping individual rows visible.
Common examples include ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, and SUM OVER.
Sample answer
Window functions are useful when we want calculations without collapsing rows like GROUP BY does.
For example, I can use ROW_NUMBER to rank orders by date for each customer, or use LAG to compare this month’s sales with last month’s sales.
Window functions are very useful in trend analysis and reporting.
12. How would you find duplicate records in SQL?
You can find duplicates using GROUP BY and HAVING COUNT greater than 1.
Sample answer
To find duplicate emails in a customer table, I would group by the email column and use HAVING COUNT(*) > 1.
Example:
SELECT email, COUNT()
FROM customers
GROUP BY email
HAVING COUNT() > 1;
This shows which email values appear more than once.
13. What is a subquery?
A subquery is a query inside another query.
It is used when the result of one query is needed for another query.
Sample answer
A subquery helps break down a complex problem. For example, if I want to find customers who spent more than the average purchase amount, I can first calculate the average amount in a subquery and then compare each customer’s spending against it.
Excel Interview Questions for Data Analysts
Excel is still widely used in data analyst roles.
Even if a company uses Power BI or Python, Excel is often used for quick analysis, cleaning, validation, and reporting.
14. Which Excel functions are important for data analysts?
Important Excel functions include VLOOKUP, XLOOKUP, INDEX-MATCH, IF, SUMIFS, COUNTIFS, CONCAT, TEXT, LEFT, RIGHT, MID, IFERROR, FILTER, UNIQUE, and pivot tables.
Sample answer
For data analysis, I use lookup functions to combine data, conditional functions to create flags, text functions to clean columns, and pivot tables to summarize data.
I also use SUMIFS and COUNTIFS for conditional aggregation. For modern Excel, XLOOKUP, FILTER, and UNIQUE are very useful because they make analysis faster and cleaner.
15. What is the difference between VLOOKUP and XLOOKUP?
VLOOKUP searches vertically and usually looks from left to right.
XLOOKUP is more flexible because it can search in any direction and does not require the lookup column to be the first column.
Sample answer
VLOOKUP is useful but has limitations. It requires the lookup value to be in the first column of the selected range.
XLOOKUP is better because it can search left, right, vertically, or horizontally. It also handles missing values more clearly.
In most modern Excel work, I prefer XLOOKUP when available.
16. What is a pivot table?
A pivot table is used to summarize and analyze large datasets quickly.
It helps create summaries by category, date, region, product, or customer segment.
Sample answer
A pivot table allows me to summarize data without writing formulas manually. For example, I can quickly calculate total sales by product category, average revenue by city, or customer count by month.
It is useful for quick reporting and exploratory analysis.
17. How do you remove duplicates in Excel?
You can remove duplicates using the Remove Duplicates option under the Data tab.
You can also identify duplicates using COUNTIF or conditional formatting.
Sample answer
If I only need to clean the file, I use the Remove Duplicates feature. But before deleting anything, I first check which rows are duplicates and whether they are truly unwanted.
Sometimes duplicate customer names may be valid, but duplicate customer IDs may not be. So I always check the business meaning before removing duplicates.
18. How do you handle missing data in Excel?
Missing data can be handled by checking blanks, understanding why values are missing, and deciding whether to fill, remove, or flag them.
Sample answer
First, I identify missing values using filters, conditional formatting, or formulas. Then I check whether the missing values are random or meaningful.
If the missing data is small and not important, it may be removed. If it is important, I may fill it using an average, median, previous value, or business rule. In some cases, I create a separate flag to show missing information.
Power BI Interview Questions for Data Analysts
Power BI is one of the most in-demand tools for data analyst roles.
Interviewers usually test dashboard design, data modeling, DAX, relationships, filters, and business storytelling.
19. What is Power BI?
Power BI is a business intelligence tool used to connect data, transform it, build data models, create dashboards, and share reports.
Sample answer
Power BI helps convert raw data into interactive reports and dashboards. It allows users to connect different data sources, clean data using Power Query, create relationships, write DAX measures, and build visuals for business decision-making.
It is useful because stakeholders can track KPIs and explore data without manually working through spreadsheets.
20. What is the difference between Power Query and DAX?
Power Query is used for data cleaning and transformation before the data is loaded.
DAX is used for calculations and measures after the data is loaded into the model.
Sample answer
Power Query is mainly used for preparing data. For example, removing duplicates, changing data types, splitting columns, and merging tables.
DAX is used to create calculations like total sales, year-over-year growth, profit margin, and running totals.
So, Power Query prepares the data, while DAX helps analyze it.
21. What is a measure in Power BI?
A measure is a calculation created using DAX.
It is calculated dynamically based on filters and report context.
Sample answer
A measure is used to calculate values like total revenue, average order value, profit margin, or customer count.
For example, Total Sales = SUM(Sales[Amount]).
Measures are dynamic, which means they change based on filters like date, region, product, or customer segment.
22. What is the difference between calculated column and measure?
A calculated column is created row by row and stored in the data model.
A measure is calculated dynamically based on the report filter context.
Sample answer
A calculated column is useful when I need a value for each row, such as profit per transaction or customer category.
A measure is better for aggregations like total sales, average profit, or revenue growth.
In Power BI, measures are usually preferred for KPIs because they are more flexible and efficient.
23. What makes a good dashboard?
A good dashboard is clear, simple, accurate, and focused on business decisions.
It should not be overloaded with charts.
Sample answer
A good dashboard should answer the main business questions quickly. It should show important KPIs, trends, comparisons, and filters.
The layout should be easy to understand. I avoid unnecessary visuals and focus on metrics that help stakeholders take action.
For example, a sales dashboard should clearly show revenue, growth, target achievement, top products, regions, and monthly trends.
Python Interview Questions for Data Analysts
Python is becoming more important for data analyst roles, especially where automation, large datasets, or advanced analysis is involved.
You do not always need deep machine learning knowledge, but you should know pandas, NumPy, data cleaning, and basic visualization.
24. Why is Python useful for data analysis?
Python is useful because it can clean, analyze, visualize, and automate data tasks.
It also has strong libraries for analytics and machine learning.
Sample answer
Python helps data analysts work with large datasets more efficiently. With libraries like pandas and NumPy, I can clean data, filter records, group values, merge datasets, and perform calculations.
Python is also useful for automation. For example, instead of manually preparing the same report every week, I can write a Python script to clean the data and generate output faster.
25. What is pandas in Python?
pandas is a Python library used for data manipulation and analysis.
It provides data structures like DataFrame and Series.
Sample answer
pandas is one of the most important libraries for data analysis in Python. It helps read data from CSV, Excel, SQL, and other sources.
Using pandas, I can clean data, handle missing values, filter rows, group data, merge datasets, and create summary tables.
26. What is the difference between list and tuple in Python?
A list is mutable, which means it can be changed after creation.
A tuple is immutable, which means it cannot be changed after creation.
Sample answer
Lists are used when we need a collection that may change, such as adding or removing items.
Tuples are used when we want fixed values that should not be modified.
For data analysis, lists are commonly used for column names, filters, and temporary collections.
27. How do you handle missing values in pandas?
Sample answer
First, I check missing values using df.isnull().sum(). Then I decide how to handle them based on the business context.
If missing values are very few, I may remove those rows. If the column is important, I may fill missing values using mean, median, mode, or a business rule.
For time series data, I may use forward fill or interpolation depending on the situation.
28. What is the difference between merge and concat in pandas?
Sample answer
I use merge when I want to join two datasets based on a common column, such as customer ID or product ID.
I use concat when I want to append similar datasets, like combining sales files from different months.
So, merge works like SQL joins, while concat is used for stacking data.
Statistics Interview Questions for Data Analysts
Statistics helps data analysts avoid wrong conclusions.
Interviewers do not expect every candidate to be a statistician, but they do expect a solid understanding of basic concepts.
29. What is the difference between mean, median, and mode?
- Mean is the average.
- Median is the middle value.
- Mode is the most frequent value.
Sample answer
Mean is useful when the data is normally distributed. But if the data has outliers, median is often better.
For example, if most salaries are around 5 lakh but one person earns 1 crore, the mean will become misleading. In that case, median gives a better idea of the typical salary.
Mode is useful for categorical data, such as the most common product category.
30. What is correlation?
Correlation measures the relationship between two variables.
It shows whether they move together, move opposite, or have no clear relationship.
Sample answer
Correlation tells us how strongly two variables are related. A positive correlation means both variables increase together. A negative correlation means one increases while the other decreases.
However, correlation does not prove causation. For example, ice cream sales and AC sales may both increase in summer, but one does not cause the other.
31. What is an outlier?
An outlier is a value that is very different from most other values in the dataset.
Sample answer
An outlier is an unusually high or low value. For example, if most orders are between 500 and 5,000 but one order is 5,00,000, that value is an outlier.
Outliers should not be removed blindly. I first check whether it is a data entry error, a rare but valid case, or an important business event.
32. What is A/B testing?
A/B testing compares two versions of something to see which performs better.
It is commonly used in marketing, product design, websites, and apps.
Sample answer
In A/B testing, users are divided into two groups. Group A sees the original version, and Group B sees the changed version.
Then we compare a metric like click-through rate, conversion rate, or revenue.
The goal is to check whether the change actually improves performance instead of relying on assumptions.
Scenario-Based Data Analyst Interview Questions
Scenario questions test your thinking process.
There may not be one perfect answer. Interviewers want to see how you approach the problem.
33. Sales dropped by 20% last month. How would you analyze it?
First, I would confirm whether the drop is real by checking data accuracy, date range, and source consistency.
Then I would break down sales by product, region, channel, customer segment, and time period.
I would compare last month with previous months and the same month last year to check seasonality.
I would also check discounts, pricing changes, marketing campaigns, stock availability, competitor activity, and website or app issues.
Finally, I would summarize the main reason and suggest action based on the findings.
34. Your dashboard number does not match the source data. What will you do?
I would first check whether both numbers are using the same definition, date range, filters, and data refresh time.
Then I would verify joins, calculated fields, duplicate records, missing values, and data type issues.
I would also check whether the dashboard is using imported data or live connection.
Once I find the issue, I would document the cause and fix it so the same mismatch does not happen again.
35. A stakeholder asks for a metric that you think is misleading. What will you do?
I would first understand why the stakeholder wants that metric and what decision they want to make from it.
Then I would explain the limitation of the metric in simple language.
I would not reject the request directly. Instead, I would suggest a better metric or an additional metric that gives a clearer picture.
For example, if they want only total revenue, I may also suggest profit margin, customer count, or average order value depending on the business question.
36. How would you measure customer retention?
Customer retention can be measured by checking how many customers continue to use or buy from the company over a period of time.
A basic formula is:
Retention Rate = Customers retained during period / Customers at start of period × 100
I would also look at repeat purchase rate, churn rate, cohort retention, customer lifetime value, and product usage frequency.
37. How would you analyze marketing campaign performance?
I would start by defining the campaign goal. It could be awareness, leads, conversions, revenue, or retention.
Then I would track metrics like impressions, clicks, CTR, conversion rate, cost per lead, cost per acquisition, revenue, and ROI.
I would also compare performance across channels, audience segments, creatives, locations, and time periods.
Finally, I would recommend where to increase or reduce spending.
Behavioral and HR Questions for Data Analysts
Behavioral questions test how you work with people, pressure, deadlines, and mistakes.
These answers should sound honest and practical.
38. Tell me about a time you worked with messy data.
In one of my projects, the dataset had missing values, duplicate records, inconsistent date formats, and spelling variations in category names.
I first checked the data quality issues column by column. Then I removed duplicates, standardized formats, handled missing values based on business logic, and created a clean version for analysis.
This helped make the final dashboard more accurate and easier to trust.
39. How do you explain technical findings to non-technical stakeholders?
I avoid using technical terms unless they are necessary. I focus on the business meaning of the analysis.
Instead of saying “the correlation coefficient is high,” I would say “customers who use this feature more often are also more likely to renew.”
I also use simple charts, examples, and clear recommendations so stakeholders can understand what action to take.
40. How do you handle tight deadlines?
I first clarify the priority and expected output. Then I break the task into smaller steps and focus on the most important analysis first.
If time is limited, I communicate early and share a usable version instead of waiting for a perfect one.
I also make sure the numbers are accurate because fast reporting is not useful if the data is wrong.
41. What are your strengths as a data analyst?
My main strengths are attention to detail, structured problem-solving, and the ability to explain data clearly.
I do not just focus on creating charts. I try to understand the business question first, clean the data properly, and then present insights in a way that helps decision-making.
I am also comfortable learning new tools when required.
42. What is your weakness?
Earlier, I used to spend too much time trying to make analysis perfect before sharing it. I realized that in business situations, timely insights are also important.
Now I focus on sharing a clear first version, collecting feedback, and improving it step by step.
This helps me balance accuracy, speed, and stakeholder expectations.
Fresher Data Analyst Interview Questions
Freshers usually worry because they do not have work experience.
But companies hiring freshers mainly check your basics, projects, attitude, and willingness to learn.
43. Why should we hire you as a fresher data analyst?
As a fresher, I may not have years of industry experience, but I have built a strong foundation in SQL, Excel, Power BI, and Python.
I have worked on projects where I cleaned data, created dashboards, analyzed trends, and explained insights.
I am ready to learn business processes quickly and contribute with accuracy, curiosity, and consistent effort.
44. What data analyst projects have you done?
I have worked on projects such as sales dashboard analysis, customer churn prediction, HR attrition analysis, and Excel-based business reporting.
In these projects, I cleaned datasets, analyzed key metrics, created visualizations, and wrote insights.
For example, in my sales dashboard project, I analyzed revenue by region, product category, and month to identify top-performing segments and areas needing improvement.
45. What will you do if you do not know how to solve a data problem?
I would first break the problem into smaller parts and understand exactly what is being asked.
Then I would check available data, documentation, previous reports, or similar examples.
If I still need help, I would ask a senior or team member with a clear explanation of what I tried and where I am stuck.
I believe asking thoughtful questions is better than guessing and creating wrong analysis.
Case Study Questions for Data Analyst Interviews
Some companies give case studies to check how you think.
Here are a few common examples.
46. A food delivery app wants to reduce order cancellations. What will you analyze?
I would analyze cancellation rate by restaurant, location, delivery time, payment mode, customer segment, and order value.
I would also check whether cancellations happen more during peak hours, after long wait times, or with specific restaurants.
Important metrics would include cancellation rate, average delivery time, restaurant acceptance time, refund rate, customer complaints, and repeat cancellation behavior.
Based on the findings, I would suggest actions like improving restaurant preparation time, showing accurate delivery estimates, or identifying high-risk orders early.
47. An e-commerce company wants to improve conversion rate. What data will you check?
I would check the full funnel: product views, add-to-cart rate, checkout start rate, payment success rate, and final purchase conversion.
Then I would segment the data by device, traffic source, product category, location, new vs returning users, and discount usage.
If many users drop at payment, the issue may be payment failure. If they drop after viewing products, the issue may be price, product content, or trust.
48. A company wants to know why customer support tickets increased. How will you approach it?
I would compare ticket volume by date, issue category, product, region, customer type, and support channel.
Then I would check if there was a recent product update, delivery delay, payment issue, policy change, or service outage.
I would also analyze resolution time, repeat tickets, escalation rate, and customer satisfaction score.
The goal would be to identify the root cause and suggest ways to reduce repeated issues.
Technical Concepts Every Data Analyst Should Know
Interviewers may not always ask direct questions. Sometimes they test concepts through scenarios.
You should be comfortable with these topics.
- Data cleaning
- Data validation
- KPIs
- Dashboard design
- SQL logic
- Basic statistics
- Storytelling
Data Analyst Salary Expectations in 2026
Data analyst salary depends on skills, company, location, experience, and domain.
In India, entry-level data analyst salaries commonly start around 4 LPA to 6 LPA. Candidates with strong SQL, Excel, Power BI, Python, and project experience can move toward 6 LPA to 10 LPA or higher.
Mid-level analysts with strong business understanding, automation skills, dashboarding experience, and stakeholder handling can earn more.
Approximate salary range in India
| Experience Level | Approximate Salary Range |
| Fresher | 3.5 LPA – 6 LPA |
| 1–3 years | 5 LPA – 10 LPA |
| 3–5 years | 8 LPA – 16 LPA |
| 5+ years | 15 LPA – 25 LPA+ |
These are broad ranges. A candidate in Bengaluru, Gurgaon, Mumbai, Pune, or Hyderabad may see higher packages than candidates in smaller cities.
FAQs
The most important data analyst interview questions in 2026 are based on SQL, Excel, Power BI, Python, statistics, dashboard design, and business case studies. Interviewers often ask about joins, pivot tables, DAX, missing values, KPIs, data cleaning, and how you would solve real business problems using data.
Yes, SQL is one of the most important skills for data analyst interviews. Most companies store business data in databases, so analysts need SQL to extract, filter, join, and summarize data. Even for fresher roles, basic SQL questions on joins, group by, where, having, and subqueries are very common.
Freshers should focus on SQL, Excel, Power BI, basic Python, statistics, and portfolio projects. They should prepare two to three strong projects and explain them clearly. Interviewers do not expect freshers to know everything, but they do expect strong basics, logical thinking, and willingness to learn.
Common case study questions include analyzing sales decline, customer churn, low conversion rate, high cancellation rate, delayed delivery, or marketing campaign performance. These questions test how you break down a problem, choose metrics, check data quality, analyze segments, and suggest practical business actions.
In India, fresher data analyst salaries commonly start around ₹3.5 LPA to ₹6 LPA, while candidates with stronger SQL, Excel, Power BI, Python, and project experience can earn higher. Mid-level analysts can earn much more depending on company, city, domain knowledge, and communication skills.
