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Updated on November 7, 2024
Data analysis has become an essential skill throughout industries, permitting organisations to make knowledgeable, data-driven decisions. With the growing requirement for data analysts, the job interviews for these roles can be difficult, as they cover a huge range of technical and analytical subjects. From basic principles to advanced techniques, employers search for applicants who can be the best at data management and its transformation or manipulation to get the preferable outcomes.
In this blog, we will undergo some of the common interview questions for data analytics, categorised by skill level and tool-specific understanding, which include Python, SQL, and Excel. Hence, this guide aims to help you put together effectively for a statistics analyst interview.
Also Read: Career in Data Analytics
| Aspect | Data Analysis | Data Mining |
| Purpose | Answer specific questions, make informed decisions | Discover hidden patterns or trends |
| Approach | Uses statistical and analytical methods | Often uses machine learning algorithms |
| Outcome | Provides actionable insights | Reveals unknown information |
| Typical Use Case | Business reporting and performance tracking | Fraud detection, recommendation systems |
Data validation is the process of ensuring that data is accurate, complete, and reliable before using it for analysis. It helps prevent errors that could lead to incorrect insights.
Validation techniques include checking for missing values, verifying data types, and ensuring data falls within expected ranges. Proper validation increases data quality and makes analysis more dependable.
Data analysts rely on various tools for data manipulation, analysis, and visualisation. Each tool serves a unique purpose in the data workflow.
These tools allow analysts to efficiently work with data, uncover insights, and present findings.
| Role Aspect | Data Analyst | Data Scientist |
| Primary Focus | Analysing historical data | Predicting future trends |
| Key Skills | SQL, Excel, Data Visualization | Machine Learning, Python, Statistical Modeling |
| Typical Outcome | Business reports, performance insights | Predictive models, data-driven recommendations |
Analytics projects typically follow a structured process to ensure clarity and accuracy. Here are the main steps:
Each step ensures the analysis is thorough, accurate, and actionable.
| Aspect | Data Analysis | Business Intelligence |
| Purpose | Analyses data to answer specific questions or solve problems | Provides insights for strategic and operational decision-making |
| Approach | Uses statistical and analytical methods for insights | Leverages dashboards and reports for real-time data access |
| Scope | Often project-specific, focusing on particular data sets | Broad and continuous, supporting overall business monitoring |
| Outcome | Generates reports, insights, and recommendations | Presents high-level trends and KPIs for performance tracking |
There are common challenges that data analysts face which may impact their output. Some of these include:
Addressing these challenges enables the analysts to make use of quality insights and shallow analysis.
Data cleaning is essential for ensuring data quality, as it helps remove inaccuracies and inconsistencies. Here are some effective methods:
Good data cleaning ensures that analysis is based on accurate, consistent information.
Exploratory Data Analysis (EDA) is a crucial step in data analysis that involves examining data to uncover patterns, spot anomalies, and test hypotheses. EDA helps analysts gain initial insights, guiding further analysis and model building.
The key significance of EDA lies in understanding data structure, identifying relationships, and ensuring data quality. It allows analysts to detect outliers, understand variable distributions, and prepare the data effectively before deeper analysis.
| Type | Definition | Example |
| Univariate Analysis | Examines a single variable to understand its distribution, central tendency, and spread | Analysing age distribution in a dataset |
| Bivariate Analysis | Studies the relationship between two variables using methods like correlation and scatter plots | Investigating the relationship between age and income |
| Multivariate Analysis | Involves three or more variables to analyse complex relationships and interactions | Performing regression analysis with age, income, and education level |
Time series analysis can be defined as a data analysis technique which focuses on a series or set of data points over a specific period. Most of the time, this analysis is used to identify the direction of a trend, the timing of a season, or a cyclical movement series over time.
Time series analysis is productive in making predictions on unknown variables based on known statistics, such as sales and stock prices or demand over time. Techniques like moving averages, exponential smoothing, and ARIMA models are often used in time series analysis.
In its simplest terms, feature engineering can be defined as the process which involves the construction of new features or modification of the existing ones to drive changes in the model metrics. This means it includes the process of changing the basic data into constructs that make better predictions.
Here are some examples that can make the retraining necessary:
While retraining helps to avoid the obsolescence of the models, it also helps to enhance their performance at any given point in time.
Also Read: Data Analyst Interview Questions with Answers
K-means is a clustering technique that is widely used for grouping similar data into k clusters. This algorithm works by associating each data point with the nearest cluster, and subsequently, the clustering centre is recalculated using the new relationships.
K-means clustering is often used in applications such as market segmentation, image segmentation, and outlier detection. The limitation of this algorithm is that the clusters produced are assumed to be isotropic and of the same variance which is not the likely case in most datasets.
Hypothesis testing studies if the sample data provides convincing evidence that there exists a relationship within data. Examples of hypothesis tests are:
As each test deals with the target types of data and target objectives of the study, hypothesis testing can be considered in more general strategies for statistical analyses.
Data wrangling, or data munging, refers to the process of transforming these datasets or mapping them into a cleaner structure for evaluation. It involves cleaning, organising, and enriching data to ensure it is consistent, complete, and ready for further analysis. Data has always been warped and freed from imperfections during wrangling so that the results from analytics are free of distortions.
| Type | Purpose | Techniques |
| Descriptive Analytics | Summarises historical data to understand past events and trends | Reporting, data visualisation, and summary statistics |
| Predictive Analytics | Uses historical data to forecast future outcomes | Regression analysis, machine learning, time series analysis |
| Prescriptive Analytics | Recommends actions based on insights from descriptive and predictive analytics | Decision rules, optimization algorithms, and simulation models |
Data mining involves several stages to uncover patterns and insights in data:
These stages help convert raw data into valuable insights for decision-making.
Data profiling is the system of analysing records to understand their structure, overall quality, and its content. It involves assessing statistics attributes like distribution, completeness, and accuracy.
Data profiling enables the targeting of inconsistencies, lacking values, and anomalies. This makes sure statistics are appropriate for evaluation. It’s frequently used and considered as an initial step in any data quality improvement process.
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| Sampling Technique | Description | Application Example |
| Random Sampling | Each data point has an equal chance of being selected, minimising selection bias | Selecting 100 participants randomly from a population |
| Stratified Sampling | Divides data into subgroups and samples proportionately, ensuring representation of each group | Sampling based on gender proportions in a survey study |
| Cluster Sampling | Divides the population into clusters and selects entire clusters randomly | Sampling entire regions in a national survey |
| Systematic Sampling | Selects data at regular intervals from a larger population | Choosing every 10th customer from a list for feedback |
An outlier is a data point significantly different from others in a dataset. It can arise due to errors, variability, or unique conditions.
Outliers can distort analysis and affect statistical measures like the mean. Analysts often review and decide whether to keep, adjust, or remove outliers depending on their impact on the analysis.
Hierarchical clustering uses the data that has been generated to form a tree-like structure and this tree is called the dendrogram. The statistics factors can be aggregated in a bottom-up style, also referred to as an agglomerative approach. It can also be broken down in a top-down technique also referred to as divisive.
This clustering technique is useful especially when the dataset has a nested structure altogether, i.e. one category containing others. Another advantage of hierarchical clustering is that it no longer requires a predetermined range of clusters, which is useful because the purpose of the analysis is explorative.
Handling suspicious or missing data is crucial for maintaining data quality. Common methods include:
Naive Bayes is referred to as ‘naive’ as it assumes that all functions in a dataset are impartial to each other. This further helps in streamlining the calculations however may be unrealistic when analysing actual data where features are related.
Despite this simplification, Naive Bayes performs well in many practical applications, especially with text data, due to its efficiency and ease of use.
Outliers are detected using statistical methods that identify data points deviating significantly from others:
Detecting outliers helps analysts determine whether these points should be addressed before proceeding with analysis.
| Type | Description | Use Case |
| StandardiZed Coefficients | Scale-free, allowing comparisons between variables by standardising them to have mean zero and SD one | Used to identify the relative importance of predictors in regression |
| Unstandardized Coefficients | Maintain original units, showing the direct effect size of predictors in terms of units | Useful for understanding real-world impact (e.g., income in dollars) |
Collaborative filtering is a technique used in recommendation systems to suggest items based on user preferences and past behaviour. It can either depend on user-based filtering strategies (involving finding like-minded users) or item-based filtering strategies (involving finding like-minded items).
Collaborative filtering techniques are mostly being adopted in systems such as shuffling recommendation movies, and so on where it aids in customising how people view patterned information.
Data validation ensures data accuracy, completeness, and reliability. Key methodologies include:
These methodologies help maintain data quality, reducing errors in subsequent analysis.
Clustering algorithms possess the following characteristics, which makes them more suitable for a particular activity:
Understanding these properties helps in selecting the right clustering algorithm based on the dataset’s characteristics and analysis objectives.
Logistic regression is a predictive analytical model which is utilised for the binary outcome (Yes or No or 0 or 1 ) variables. Instead of a linear outcome, where a scalar value is produced, the prediction output given out using a logistic function is a scalar probability within the bounds of 0 and 1.
This method is valued for its simplicity and interpretability, making it a popular choice in classification tasks.
Data analysis has the potential to enhance supply chain integration through the elimination of bottlenecks in processes, anticipating demand and regulating inventory in the supply chain. Using historical sales information and seasonality factors helps businesses in meeting demand accurately.
Effective data analysis in supply chains ensures cost savings, resource efficiency, and improved customer satisfaction.
Evaluating a machine learning model’s ROI involves comparing benefits against deployment and maintenance costs. Key metrics include increased revenue, improved efficiency, and cost savings linked to model predictions.
By quantifying gains from model performance, you can assess its value to the business.
Recommendation systems clearly improve the user experience by suggesting items based on what customers bought or are interested in. When recommendations are personalised, users interact more with the items and the average order value increases.
By matching users with relevant products, e-commerce platforms boost sales and improve satisfaction.
Regularization prevents overfitting by adding a penalty to the model’s loss function, discouraging overly complex models. This is critical for instance when one has created a model which has many variables but a number of these variables are not important.
Regularization improves model generalization by reducing sensitivity to irrelevant features.
Optimising a real-time streaming model requires continuous updates, as new data arrives. Use incremental learning to update the model without full retraining.
These techniques ensure quick processing and high performance, critical in real-time applications.
Version control, such as Git, allows teams to manage code changes, collaborate, and revert to earlier versions if necessary. It is particularly helpful in data projects with frequently updated code and datasets.
Using version control ensures organised workflows, better collaboration, and efficient management of code changes.
On a ten-point scale, I would give myself a score of 8 with regard to data analysis. Data cleaning, exploratory analysis and visualisation plus experience in SQL and Python place me at an advantage.
My strengths lie in interpreting data and delivering insights. I am constantly improving my skills in machine learning and advanced modelling to further enhance my proficiency.
SQL has various data types to store different kinds of data. Here are some primary types:
| Data Type | Description | Example |
| INT | Stores integer values | 123, -456 |
| FLOAT/DECIMAL | Stores decimal numbers | 3.14, 2.718 |
| VARCHAR(n) | Stores variable-length strings | ‘John’, ‘Doe’ |
| DATE | Stores date values in YYYY-MM-DD format | 2024-01-01 |
| BOOLEAN | Stores true/false values | TRUE, FALSE |
The ORDER BY clause in SQL is used to sort the results of a query in ascending or descending order. By default, it sorts in ascending order.
Example:
This query retrieves all employee records, sorted alphabetically by LastName.
CRUD Operations stands for Create, Read, Update, and Delete, which are the four primary operations in SQL:
INSERT INTO Employees (FirstName, LastName) VALUES (‘John’, ‘Doe’);
SELECT * FROM Employees;
UPDATE Employees SET LastName = ‘Smith’ WHERE EmployeeID = 1;
DELETE FROM Employees WHERE EmployeeID = 1;
These operations form the foundation of data manipulation in SQL.
The INSERT INTO statement is used to add new records to a table. Specify the table name, column names, and values to insert.
Example:
This statement inserts a new product with its name, price, and quantity.
The WHERE clause filters records based on specific conditions. It limits the rows returned by a query based on conditions you define.
Example:
This query retrieves employees who work in the Sales department and earn more than $50,000.
When you operate the ORDER BY clause, it’s going to sort all the records in ascending (ASC) order or in descending (DESC) order. If no longer mentioned, it will by default sort in ascending order.
Example:
This query lists all products, sorted from highest to lowest price.
A primary key is a unique identifier for each row in a table. It ensures each record is distinct and prevents duplicate entries.
For example:
In the above table, CustomerID serves as the primary key, uniquely identifying each customer.
To restrict the number of rows returned in a query result, you can use the LIMIT clause. This is very useful for viewing a data sample or you can limit the results in pagination.
Example:
This query returns only the first 10 employee records.
To perform calculations on multiple rows of a table, you can use Aggregate functions. Here are examples of common aggregate functions:
The GROUP BY clause groups rows with identical values in specific columns. This is regularly used with aggregate functions to summarise statistics.
Example:
This query counts the number of employees in each department.
When you use the join operation in SQL, it will combine the rows from two or more tables, based on a related column. Here’s an outline of common join types:
| Join Type | Description |
| INNER JOIN | Returns rows with matching values in both tables. |
| LEFT JOIN | Returns all rows from the left table and matching rows from the right. |
| RIGHT JOIN | Returns all rows from the right table and matching rows from the left. |
| FULL JOIN | Returns all rows when there is a match in either table. |
A subquery is a query used inside another SQL query. It is used to retrieve data to be used in the primary query.
Example:
Here, the subquery calculates the average revenue, and the principal query retrieves employees with salaries above this average.
The UNION operator combines the outcomes of two or more SELECT statements, getting rid of duplicates by using default. UNION ALL includes all duplicates.
Example:
Here, you can use normalisation to reduce the data redundancy, and it overall improves the data integrity through proper structuring of your database schema.
Normalisation reduces data redundancy, enhances efficiency, and guarantees consistency.
Window functions perform calculations across rows related to the current row within a defined “window.” Unlike regular aggregate functions, window functions do not collapse rows but retain each row in the result set.
Example of a window function:
This query ranks employees based on their salary without grouping rows.
Python has several libraries designed for data analysis. Some of the main ones include:
These libraries together create a robust ecosystem for data analysis in Python.
Here, you need to perform multiplication among each value by its weight. After that, you can divide by the sum of all the weights to get the average weight. Following is the formula you can use.
Formula:
=SUMPRODUCT(A2:A10, B2:B10) / SUM(B2:B10)
When you use the SUMPRODUC, it will multiply each pair of values and weights followed by division by the sum, SUM(B2:B10) to get the weighted average.
In Python, you can use Pandas to handle missing data effectively. Some common methods are:
df.dropna(inplace=True) # Drops all rows with any lacking values
df[‘column_name’].fillna(df[‘column_name’].mean(), inplace=True) # Fills NaNs with column mean
Choosing the approach depends on the facts, context and the number of missing records.
| Data Structure | Description | Mutability | Example |
| List | Ordered collection of items, accessed by index | Mutable | my_list = [1, 2, 3] |
| Tuple | Ordered collection of items, similar to lists | Immutable | my_tuple = (1, 2, 3) |
| Dictionary | Collection of key-value pairs, accessed by keys rather than index | Mutable | my_dict = {‘a’: 1, ‘b’: 2} |
Code:
This code sums the list values and divides by the number of elements, giving the average as output.
You can use the groupby() function in Pandas to group statistics and calculate aggregate information like mean sum, and count.
Example:
This code organises all the records by the Department column. It also calculates the mean salary for every department that you want to include.
The apply() function in Pandas applies a function along an axis of the DataFrame. It’s used for complicated operations that require custom logic on each row or column.
Example:
In this example, apply() doubles every fee in column A, growing a new column C with the outcomes.
Vectorisation allows operations on entire arrays or data columns without explicit loops, making calculations faster. Libraries like NumPy support vectorised operations, which run at optimised speeds by leveraging low-level implementations.
Benefits of vectorisation:
Example:
Here, multiplying arr by 2 applies the operation to each element at once.
Also Read: Mastering Pandas in Python
The merge() function helps you to combine datasets in Pandas, which are based on a common key. It helps in joining datasets through various different manners like inner, left, right, and outer joins.
Example:
This code merges df1 and df2 on the ID column, preserving only rows with matching IDs in both data frames.
Here, you can define any anonymous function with the help of Lambda functions using a lambda keyword. They are normally used for easy operations where defining an ordinary characteristic would be excessive.
Example:
Using Pandas, you can filter rows based on conditions by applying a conditional expression directly to the DataFrame.
Example:
Here, when you use this code, it will filter all the rows where there is an Age column greater than 30.
Common Excel functions used in data analysis include:
=SUM(A1:A10)
=AVERAGE(B1:B10)
=COUNTIF(C1:C10, “>50”)
=VLOOKUP(D2, A1:B10, 2, FALSE)
=IF(E1>50, “Pass”, “Fail”)
These functions are very helpful in performing different calculations and analysing data patterns across the dataset, which helps in making further decisions.
| Function | Description | Advantage |
| VLOOKUP | Searches for a value in the leftmost column and returns a value in a specified column | Simpler for basic lookups |
| INDEX-MATCH | Uses INDEX to return a value and MATCH to find a position, more flexible than VLOOKUP | Allows lookup in any column direction |
Pivot tables can be really helpful in summarising large datasets. When you use pivot tables, you can get analysis of large tables through sorting, grouping, aggregating, etc. on any specific field.
Steps:
For example, a pivot table can show total sales by region or average sales per product, enabling quick insights without complex formulas.
Conditional formatting in Excel changes the appearance of cells based on set criteria. This feature is useful for highlighting values that meet certain conditions.
Example: To highlight sales greater than $10,000:
In this method, you can easily emphasise high-value sales through visualisation.
When you use the IFERROR function, it automatically handles any occurring errors. It also returns a specific value instead of displaying an error, which can be considered good for programmers to understand the nature of errors. For example, it is used when a formula may generate errors like #DIV/0! or #no.
Example: To avoid an error when dividing:
=IFERROR(A1/B1, “Error”)
If B1 is zero, this formula returns “Error” instead of a division error. It helps to prevent error messages from cluttering your worksheet.
Here, the data that is entered into cells can be limited through data validation. This will overall benefit you by reducing errors and maintaining data consistency.
How to Use:
For example, setting a validation rule for dates ensures that only valid dates are entered, preventing incorrect data entries.
Dynamic charts automatically update when the data changes, allowing for real-time visual analysis. This is particularly useful in dashboards or reports where data frequently changes.
Steps:
Dynamic charts are precious for monitoring ongoing performance metrics, such as each day’s income or website site visitors, without having to recreate the chart.
Drop-down lists simplify statistics entry with the aid of providing predefined options for a cellular, decreasing manual input mistakes.
Steps:
For example, you can use a drop-down list for different departments like Sales, HR, Finance, etc. to make sure there is a steady flow of entries in a worker records sheet.
Macros are recorded units of actions in Excel that automate repetitive duties, saving time and effort.
Example: To create a macro that formats a report:
| Tool | Key Features | Strengths | Use Cases |
| Tableau | Powerful visualisation capabilities, easy to use | Excellent for interactive and complex dashboards | Ideal for visual analysis and large datasets |
| Power BI | Microsoft ecosystem integration, DAX support | Cost-effective, integrates well with Office | Best for businesses using Microsoft products |
| Looker | SQL-based, focused on data modelling | Strong data exploration and customisable views | Great for SQL-savvy teams needing flexible analysis |
Visualisations for categorical data focus on comparing groups, distributions, or parts of a whole:
Choosing the right visualisation type enhances the clarity of categorical comparisons.
Choosing the right chart depends on the data and the message you want to convey:
Each chart type highlights a different aspect, so selecting the right one enhances data interpretation.
To handle large datasets effectively in BI tools, apply the following strategies:
These techniques reduce data load, helping BI tools perform efficiently with large datasets.
ETL (Extract, Transform, Load) is a process that prepares data for analysis:
ETL is crucial because it ensures data accuracy and consistency before analysis, making BI insights reliable and actionable.
Filters and slicers allow users to focus on specific data segments, creating a customised view:
These tools let users explore data dynamically, offering flexible insights based on their needs.
The executive dashboard must be as simple and as easy as possible since it is designed to look at high-level information:
If the executive dashboard is well designed, the dashboard exhibits areas that need a very high level of immediate attention.
Drill-Down and Drill-Up features allow users to move up and down within the hierarchy of the data structure:
In Power BI, you can use DAX to calculate a running total:
This DAX formula calculates a running total by summing SalesAmount up to the current date, allowing users to track cumulative sales over time.
A KPI dashboard should display metrics in a structured, easy-to-read format:
These elements help ensure that the dashboard effectively communicates performance measures and suggests relevant decisions regarding our project.
Data analytics and business intelligence are essential in today’s data-driven world. It helps various organisations make smart decisions by mastering tools like SQL, Python, Excel, Tableau, Power BI, and other BI platforms. Analysts can transform raw data into meaningful insights that drive strategic action and perform required relevant changes.
Day by day, with the rapid growth and increasing complexity of data, the demand for skilled data analysts increases continuously. Keeping up to date with tools and techniques ensures that experts can effectively interpret data and support decision-making, along with strong analytical and visualisation skills with clear communication. This makes data analysts invaluable in guiding businesses towards their goals and maintaining competitive advantage in today’s market. If you’re interested in shaping your career in data analytics, consider pursuing the Accelerator Program in Business Analytics and Data Science offered in collaboration with edX and Harvard University.
Updated on November 7, 2024
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