UK Data Analyst Interview Questions: Complete Preparation Guide

UK Data Analyst Interview Guide ## Table of Contents

Sample Answer: “Both UNION and UNION ALL are used to combine the result sets of two or more SELECT statements. The key difference is that UNION removes duplicate rows from the combined result set,

departments (id, name), write a query to find the names of all employees in the ‘Sales’ department. Sample Answer: SELECT e.name FROM employees e JOIN departments d ON e.department_id $ = $ d.id WHERE d.name $ = $ 'Sales'; “This query works by joining the employees table with the departments table on their common column, department_id . The JOIN creates a temporary table where each row contains information about an employee and their corresponding department. Then, the WHERE clause filters this result to only include rows where the department name is ‘Sales’. Finally, the SELECT statement retrieves the names of those employees.”

的。

example. Sample Answer: “Window functions are a powerful feature in SQL that perform a calculation across a set of table rows that are somehow related to the current row. Unlike aggregate functions ( SUM , AVG , etc.) which collapse rows into a single output row, window functions return a value for each row based on the ‘window’ of data defined by the OVER() clause. They are incredibly useful for tasks like calculating running totals, rankings, and moving averages. For example, to find the top highest-paid employees in each department, you could use RANK() : SELECT name, department, salary FROM ( SELECT name, department, salary, RANK() OVER (PARTITION BY department ORDER BY salary DESC) as rank_num FROM employees ) as ranked_employees WHERE rank_num <= 3; Here, PARTITION BY department creates a separate ‘window’ for each department, and ORDER BY salary DESC sorts employees within that window. RANK() then assigns a rank to each employee based on their salary within their department.”

PARTITION BY 和 ORDER BY 在 OVER() ⼦句中的作⽤。

Sample Answer: “Both WHERE and HAVING are used to filter rows, but they operate at different stages of a query. The WHERE clause filters rows before any groupings or aggregations are performed. It works on the raw data from the tables. The HAVING clause filters groups after aggregations have been performed. It is used in combination with the GROUP BY clause. For example, if you want to find all departments that have more than employees: SELECT d.name, COUNT(e.id) as employee_count FROM departments d JOIN employees e ON d.id $ = $ e.department_id GROUP BY d.name HAVING COUNT(e.id) $ > $ 10; Here, you cannot use WHERE COUNT(e.id) $ > $ 10 because the WHERE clause is executed before the COUNT is calculated.”

来说明为什么必须使⽤ HAVING 。

one? Sample Answer: “A Common Table Expression, or CTE, is a temporary, named result set that you can reference within a SELECT , INSERT , UPDATE , or DELETE statement. You define it using the WITH clause. I would use a CTE for several reasons:

simple, logical building blocks. This makes the query much easier to read and debug, both for myself and for other analysts.

useful for querying hierarchical data like organizational charts or bill of materials.

query, avoiding the need to rewrite the same subquery. For example, instead of a complex subquery in a JOIN , I can define it as a CTE first, making the main query logic cleaner.”

点。

and iloc ? Sample Answer: “Both loc and iloc are used for selecting data

are the pros and cons of different methods? Sample Answer: “There are several ways to handle missing data, and the best method depends on the context and the nature of the data.

Method: Use df.dropna() . You can drop rows or columns with missing values. Pros: Simple and effective if you have a large dataset and the missing values are few. Cons: You lose data, which can be problematic if the dataset is small or the missing data is not random. This can introduce bias.

Method: Use df.fillna() . You can fill the missing values with a specific value. Common imputation strategies: Mean/Median: For numerical data. Median is better if there are outliers. Mode: For categorical data. Forward-fill or backward-fill: Useful for time-series data. Pros: Retains all data, which can be important for model building. Cons: Can reduce the variance of the data and potentially distort the relationship between variables. The imputed value is an estimate, not the true value. My choice would depend on the percentage of missing data and why it’s missing. I would always start by investigating the cause of the missingness before deciding on a method.”

标志。

Sample Answer: “I can write this in a few ways. A simple recursive solution is easy to understand, but it’s inefficient. A more efficient iterative solution is generally better. Iterative Solution (More Efficient):

码，还懂算法分析。

Sample Answer: “List comprehensions are a concise and readable way to create lists in Python. They provide a shorter syntax for creating a new list based on the values of an existing list or other iterable. The basic syntax is [expression for item in iterable if condition] . For example, if I have a list of numbers and I want to create a new list containing the squares of only the even numbers, I could do it like this: Without list comprehension: numbers $ = $ [1, 2, 3, 4, 5, 6] squares_of_evens $ = $ []

Sample Answer: “Lists and tuples are both used to store collections of items in Python, but they have one key difference: mutability. Lists are mutable, meaning you can change their content after they are created. You can add, remove, or change elements. They are defined with square brackets [] . Tuples are immutable, meaning once they are created, you cannot change their content. They are defined with parentheses () . Because tuples are immutable, they have a fixed size and can be used as keys in a dictionary, whereas lists cannot. They are also generally slightly more memory-efficient and faster to access than lists. You would use a tuple for data that you know should not change, like the coordinates of a point (x, y), and a list for a collection of items that you expect to modify.”

好”，这会展⽰你更深层次的理解。

Sample Answer: “This is a fundamental concept in data analysis. Correlation measures the statistical relationship or association between two variables. It tells us if and how two variables move together. For example, ice cream sales and sunglasses sales are highly correlated because they both increase during the summer. Causation means that a change in one variable causes a change in another variable. For example, increasing the temperature causes ice to melt. The critical point is that correlation does not imply causation. The classic example is that ice cream sales are correlated with crime rates, but eating ice cream does not cause crime. The hidden or confounding variable is the season: both tend to increase in the summer. As a data analyst, it’s my job to not just find correlations but to design experiments or use statistical methods to try and determine if there is a causal link.”

果”。

系，⽽不仅仅是发现相关性。

Sample Answer: “Imagine we have an idea we want to test, for example, that our new website design (Version B) is better than the old one (Version A). Our starting assumption, or ‘null hypothesis’, is that the new design has no effect. The p-value is the probability of seeing the results we saw, or something even more extreme, if the new design actually had no effect. So, if we get a very small p-value (typically less than .), it’s like saying: ‘It would be very, very unlikely to get this result just by random chance if our new design was truly no better.’ This low probability gives us the confidence to reject our initial assumption and conclude that our new design likely does have a real, positive effect. A high p-value, on the other hand, means the results could easily have happened by chance, so we don’t have enough evidence to say the new design is better.”

率”。

⾜）。

Sample Answer: “The Central Limit Theorem (CLT) is a fundamental principle in statistics. It states that if you take a sufficiently large number of random samples from any population, the distribution of the sample means will be approximately a normal distribution (a bell curve), regardless of the original population’s distribution. It’s important for three main reasons:

like t-tests, assume that the data is normally distributed. The CLT allows us to apply these tests to the sample means even if the underlying data is not normal.

about a population parameter (like the true population mean) based on a sample statistic (the sample mean).

phenomena are the sum of many independent random processes, and the CLT explains why their distribution often approximates a normal distribution.”

布。

⽤。

hypothesis testing? Sample Answer: “In hypothesis testing, we are trying to decide whether to reject our null hypothesis. There are two types of mistakes we can make: Type I Error (False Positive): This is when we incorrectly reject a true null hypothesis. In a business context, this would be like launching a new marketing campaign believing it’s effective, when in reality it has no effect. We’ve wasted money. Type II Error (False Negative): This is when we fail to reject a false null hypothesis. In the same context, this would be like concluding our new marketing campaign has no effect and scrapping it, when in reality it was effective. We’ve missed an opportunity. There is always a trade-off between these two errors. Decreasing the chance of one often increases the chance of the other. The significance level (alpha), which is often set at %, is the probability of making a Type I error.”

Sample Answer: “Regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It helps us understand how the dependent variable changes when one of the independent variables is varied, while the other independent variables are held fixed. For example, we could use regression to model how a house’s price (dependent variable) is influenced by its size, number of bedrooms, and location (independent variables). Some key assumptions for a linear regression model are:

linear.

independent variables.

Violating these assumptions can make the results of the regression misleading.”

理解。

Sample Answer: “My goal with any visualization is to communicate insights clearly and effectively. Some key principles I follow are:

at zero, and scales should be appropriate.

most of the ‘ink’ on the chart should be used to represent the data itself, not chart junk like heavy gridlines, borders, or D effects.

a line chart for showing trends over time, a scatter plot for showing relationships between two variables, and so on.

necessary. Avoid clutter and unnecessary information.

dashboard for an executive might be high-level, while a chart for another analyst might be more detailed.”

metrics would you include? Sample Answer: “I would start by asking the sales team what their goals and key questions are. Assuming they are focused on revenue growth and sales performance, I would likely include a mix of leading and lagging indicators: Key Performance Indicators (KPIs) $ - $ The Big Picture: Total Revenue vs. Target: The most important metric, showing performance against goals. I would show this over time (e.g., monthly, quarterly). Average Deal Size: Helps to understand if we are winning larger or smaller deals. Win Rate: The percentage of opportunities that are converted into sales. This measures efficiency. Leading Indicators & Activity Metrics $ - $ The ‘How’: Number of New Leads/Opportunities: The top of the sales funnel. Sales Cycle Length: How long it takes to close a deal. Pipeline Value: The total value of all open opportunities. Breakdowns & Filters: I would allow the dashboard to be filtered by sales representative, region, and product so that individuals and managers can see their specific performance. I would design the dashboard to be clean and easy to read, with the most important KPIs at the top and more detailed breakdowns below.”

⼾为中⼼的数据分析师。

week. How would you investigate this? Sample Answer: “This is a critical issue, and I would approach it with a structured, hypothesis-driven method. Step : Clarify and Validate First, I would confirm the drop is real. Is it a data tracking error? Is the data pipeline delayed? I would check the data source and query logs. I would also clarify the metric. How exactly are we defining a ‘retained user’? Is it someone who just opens the app, or someone who performs a key action? Step : Segment the Data (Isolate the ‘Where’ and ‘Who’) Once the drop is validated, I would try to isolate where it’s coming from. I would segment the retention data by various dimensions: User Segments: Is the drop concentrated among new users or long-term users? High-value users or low-value users? Platform: Is it happening on iOS, Android, or both? Geography: Is it specific to a certain country or region (e.g., the UK)? App Version: Did the drop coincide with a new app release? Step : Formulate Hypotheses (The ‘Why’) Based on the segmentation, I would form hypotheses. For example: Internal Factors: ‘A bug in the new iOS app version is causing crashes, leading to lower retention.’ External Factors: ‘A competitor launched a major marketing campaign last week, attracting our users.’ Product Changes: ‘We recently changed the user interface, and users are finding it confusing.’ Step : Test Hypotheses and Recommend Actions I would then work with engineers and product managers to test these hypotheses. For example, we could check crash logs or run a user survey. Once the root cause is identified, I would recommend actions, such as rolling back the app update or launching a re-engagement campaign, and I would set up a dashboard to monitor the recovery.”

志。

（竞争、市场）。

(e.g., % off). How would you measure its success? Sample Answer: “To measure the success of a promotional campaign, we need to look beyond just the immediate revenue lift and consider its true profitability and long-term impact. I would use an A/B testing framework.

Control Group: A randomly selected group of users who do not see the promotion. Treatment Group: A group of users who do see the % off promotion. Key Metrics: I would track several metrics for both groups: Primary Metric: Incremental Revenue or Incremental Profit. This is the additional revenue/profit generated from the treatment group compared to the control group. Secondary Metrics: Conversion Rate, Average Order Value (AOV), Customer Acquisition Cost (CAC), and subsequent user retention.

Short-term Success: Was the incremental revenue greater than the cost of the discount and the campaign’s operational costs? Did the conversion rate significantly increase for the treatment group? Long-term Success: Did the new customers acquired during the campaign come back and make a second purchase? Or did we just attract one-time bargain hunters? I would track the lifetime value (LTV) of the customers in the treatment group versus the control group over the next few months.

I would also be mindful of potential risks, such as cannibalization (users who would have bought at full price anyway just used the discount) and brand damage (training customers to always wait for a sale). The A/B test helps to measure the cannibalization effect directly.”

指标。

subscription product? Sample Answer: “Determining the optimal price is a complex task that involves balancing customer value, business costs, and market position. I would use a combination of methods:

the finance team to understand the total cost of providing the service, including development, support, and marketing costs. This gives us a price floor; we must charge more than our cost to be profitable.

competing products are charging. This helps us understand the market price range and how we should position our product (e.g., as a premium, mid-range, or budget option).

most important part. I would try to quantify the value our product provides to the customer. This can be done through customer surveys and interviews. A common technique is the Van Westendorp Price Sensitivity Meter, where you ask customers four questions: At what price would it be too expensive? At what price would it be a bargain? At what price would it start to get expensive? At what price would it be too cheap (raising quality concerns)? The intersection of these curves helps identify an acceptable price range.

test on our website, showing different prices to different user segments to see how it impacts the conversion rate. This gives us real-world data on price elasticity. Ultimately, the optimal price is likely not a single number but a pricing strategy that might include different tiers for different types of users.”

字。

Questions

Sample Answer: “In my previous role, the marketing team wanted to invest a significant portion of our budget into influencer marketing on Instagram. The decision was based on the belief that it was the most effective channel. I was skeptical and decided to investigate. My Analysis: I pulled data from our web analytics and CRM systems to track the customer journey from different marketing channels. I built a simple attribution model and found that while Instagram generated a lot of ‘likes’ and initial clicks (first-touch attribution), it had a very low conversion rate. In contrast, email marketing and organic search, while less glamorous, were driving the majority of actual sales (last-touch attribution). Influencing the Decision: I created a clear and simple dashboard in Tableau that visualized the cost per acquisition (CPA) and lifetime value (LTV) of customers from each channel. Instead of just presenting the data, I told a story. I showed that while Instagram was good for brand awareness at the top of the funnel, email and search were the real revenue drivers at the bottom. The Outcome: Based on this data, the team didn’t abandon Instagram, but they reallocated a significant portion of the budget towards optimizing our email and SEO strategies. Over the next quarter, our overall marketing CPA decreased by %, and revenue increased by %. This taught me that data can challenge assumptions and lead to much more effective decisions when presented as a compelling story.”

你如何呈现你的发现（“讲故事”⽽不是“扔数据”）。

Sample Answer: “I was working with a product manager who was very passionate about a new feature but was not very data-driven. He was convinced the feature would be a huge success and wanted to launch it immediately, without any testing. My role was to provide an analysis of its potential impact. My initial analysis suggested the feature might only appeal to a small segment of users and could potentially confuse our main user base. When I presented this, the product manager was very dismissive and questioned my data. Instead of getting into an argument, I took a different approach. I said, ‘I understand your passion for this feature, and you might be right. My data is just one perspective. How about we work together to find a way to test your hypothesis quickly and safely?’ I proposed a small-scale A/B test, targeting just % of our users. I framed it as a way to gather more data to support his idea and de-risk the launch. By showing that I was on his side and wanted his idea to succeed, he became much more receptive. We ran the test, and the data showed that the feature had a negative impact on user engagement. He saw the data for himself and made the decision to pivot the feature design. We ended up building a much better version later on. This taught me the importance of empathy and finding common ground when dealing with difficult stakeholders.”

动”。

为帮助他成功的⽅式，这是⼀个⾮常聪明的策略。

的业务决策。

Sample Answer: “Ensuring data quality is one of the most critical parts of my job. I have a multi-step process:

where the data is coming from. I talk to the data engineers, read the documentation, and understand how the data is collected and defined.

missing values, outliers, and strange distributions. I use functions like df.describe() and df.info() in pandas to get a quick overview.

looking at e-commerce data, I’ll check if there are any orders with negative prices or if a user’s first purchase date is after their last purchase date. I also try to triangulate the data with other sources. For example, I might compare the revenue numbers from our database with the numbers from the finance department’s reports.

in my code. If it’s a recurring analysis, I build automated data quality checks into my data pipeline that will alert me if something looks wrong. Ultimately, data quality is not a one-time task but an ongoing process of vigilance and collaboration with the data engineering and business teams.”

⼈给你⼲净的数据。

测量）。

technologies in data analytics? Sample Answer: “I’m very passionate about learning, and I use a combination of resources to stay current: Online Communities and Blogs: I regularly read blogs like ‘Towards Data Science’ on Medium and participate in online communities like Kaggle and Stack Overflow. These are great for learning new practical techniques. Podcasts and Newsletters: I subscribe to data science newsletters and listen to podcasts. They are great for understanding high-level trends and new ideas in the industry. Online Courses: I periodically take online courses on platforms like Coursera or DataCamp to deep-dive into a new technology, for example, a new data visualization library or a cloud data platform. Practical Application: Most importantly, I try to apply what I learn in my personal projects. For me, the best way to learn a new skill is to actually use it to solve a problem.”

Sample Answer: “I want to be a Data Analyst because I love solving puzzles and I’m fascinated by how data can be used to understand the world and make better decisions. I enjoy the entire process, from digging into a messy dataset to find a hidden pattern, to building a model that predicts a future trend, and finally, to communicating that insight in a way that inspires action. I see data analysis as a unique blend of technical skill and business storytelling, and I find that combination incredibly rewarding. I’m not just interested in numbers; I’m interested in the stories the numbers tell and the impact those stories can have.”

Sample Answer: “A/B testing, at its core, is a randomized controlled experiment. It’s a way to compare two versions of something (a webpage, an app feature, an email subject line) to determine which one performs better. You show Version A (the control) to one group of users and Version B (the treatment) to another, and then you compare a key metric, like conversion rate, to see which version is the winner. It’s incredibly important because it allows us to make decisions based on data and evidence, rather than on opinions or gut feelings. It’s the most reliable way to establish a causal link between a change we make and its impact on user behavior. This helps companies to de-risk new ideas, iterate on their products effectively, and ultimately drive growth.”

change is important? Sample Answer: “Statistical significance, often determined by the p-value, tells us the probability that the observed result of an experiment occurred due to random chance. If a result is statistically significant (e.g., p $ < $ .), it means it’s unlikely to be a random fluke. It gives us confidence that the effect we are seeing is real. However, a statistically significant result does not necessarily mean the change is practically or commercially important. This is the concept of practical significance versus statistical significance. For example, with a very large sample size, we might find a statistically significant .% increase in conversion rate. While the effect is real, a .% lift might not be large enough to justify the engineering cost of launching the new feature. As a data analyst, my job is to report both. I need to say, ‘Yes, this effect is real and not due to chance,’ but also, ‘Here is the size of the effect (the confidence interval), and here is what it means for the business in terms of revenue or user engagement.’”

test? Sample Answer: “There are several potential pitfalls that can invalidate the results of an A/B test:

significant result (a practice called ‘peeking’), you are likely to get a false positive. You should decide on the sample size and duration in advance.

not be representative of normal user behavior. Similarly, users might initially react positively to any change simply because it’s new (the novelty effect).

the same time or by an external event (like a major news story).

could be a huge win with one user segment and a huge loss with another. It’s important to look at the results for different user groups.

metric) but decrease long-term user retention. It’s crucial to look at a range of metrics to understand the full impact.”

Sample Answer: “A confidence interval is a range of values that we are fairly sure our true population parameter lies in. For example, an A/B test might show that a new feature increased the conversion rate by %, with a % confidence interval of [.%, .%]. This means that while our single best estimate of the uplift is %, we are % confident that the true uplift for the entire user population is somewhere between .% and .%. It’s more informative than just a single point estimate because it gives us a sense of the uncertainty and precision of our measurement. A very wide confidence interval suggests that we have a lot of uncertainty, while a narrow one suggests our estimate is more precise.”

a data engineer? Sample Answer: “These roles often overlap, but they generally focus on different parts of the data lifecycle. Data Engineer: They build the foundation. They are responsible

来）。

刻理解⾮常清晰。