Data is information, often in the form of numbers, text, or multimedia, that is collected and stored for analysis. It can come from various sources, such as business transactions, social media, or scientific experiments. In the context of a data analyst, their role involves extracting meaningful insights from this vast pool of data .
In the 21st century , data holds immense value, making data analysis a lucrative career choice. If you’re considering a career in data analysis but are worried about interview questions, you’ve come to the right place. This article presents the top 85 data analyst interview questions and answers to help you prepare for your interview. Let’s dive into these questions to equip you for success in the interview process
Data Analyst Interview Questions
Table of Content
Data analysts is a person that uses statistical methods, programming, and visualization tools to analyze and interpret data, helping organizations make informed decisions. They clean, process, and organize data to identify trends, patterns, and anomalies , contributing crucial insights that drive strategic and operational decision-making within businesses and other sectors.
Here we have mentioned the top questions that are more likely to be asked by the interviewer during the interview process of experienced data analysts as well as beginner analyst job profiles.
Data analysis is a multidisciplinary field of data science, in which data is analyzed using mathematical, statistical, and computer science with domain expertise to discover useful information or patterns from the data. It involves gathering, cleaning, transforming, and organizing data to draw conclusions, forecast, and make informed decisions. The purpose of data analysis is to turn raw data into actionable knowledge that may be used to guide decisions, solve issues, or reveal hidden trends.
Data analysts and Data Scientists can be recognized by their responsibilities, skill sets, and areas of expertise. Sometimes the roles of data analysts and data scientists may conflict or not be clear.
Data analysts are responsible for collecting, cleaning, and analyzing data to help businesses make better decisions. They typically use statistical analysis and visualization tools to identify trends and patterns in data. Data analysts may also develop reports and dashboards to communicate their findings to stakeholders.
Data scientists are responsible for creating and implementing machine learning and statistical models on data. These models are used to make predictions, automate jobs, and enhance business processes. Data scientists are also well-versed in programming languages and software engineering.
Data analysis and Business intelligence are both closely related fields, Both use data and make analysis to make better and more effective decisions. However, there are some key differences between the two.
The similarities and differences between the Data Analysis and Business Intelligence are as follows:
There are different tools used for data analysis. each has some strengths and weaknesses. Some of the most commonly used tools for data analysis are as follows:
Data Wrangling is very much related concepts to Data Preprocessing . It’s also known as Data munging. It involves the process of cleaning, transforming, and organizing the raw, messy or unstructured data into a usable format. The main goal of data wrangling is to improve the quality and structure of the dataset. So, that it can be used for analysis, model building, and other data-driven tasks.
Data wrangling can be a complicated and time-consuming process, but it is critical for businesses that want to make data-driven choices. Businesses can obtain significant insights about their products, services, and bottom line by taking the effort to wrangle their data.
Some of the most common tasks involved in data wrangling are as follows:
Descriptive and predictive analysis are the two different ways to analyze the data.
Univariate, Bivariate and multivariate are the three different levels of data analysis that are used to understand the data.
Some of the most popular data analysis and visualization tools are as follows:
Data analysis involves a series of steps that transform raw data into relevant insights, conclusions, and actionable suggestions. While the specific approach will vary based on the context and aims of the study, here is an approximate outline of the processes commonly followed in data analysis:
Data cleaning is the process of identifying the removing misleading or inaccurate records from the datasets. The primary objective of Data cleaning is to improve the quality of the data so that it can be used for analysis and predictive model-building tasks. It is the next process after the data collection and loading.
In Data cleaning, we fix a range of issues that are as follows:
Exploratory data analysis (EDA) is the process of investigating and understanding the data through graphical and statistical techniques. It is one of the crucial parts of data analysis that helps to identify the patterns and trends in the data as well as help in understanding the relationship between variables.
EDA is a non-parametric approach in data analysis, which means it does take any assumptions about the dataset. EDA is important for a number of reasons that are as follows:
EDA provides the groundwork for the entire data analysis process. It enables analysts to make more informed judgments about data processing, hypothesis testing, modelling, and interpretation, resulting in more accurate and relevant insights.
Time Series analysis is a statistical technique used to analyze and interpret data points collected at specific time intervals. Time series data is the data points recorded sequentially over time. The data points can be numerical, categorical, or both. The objective of time series analysis is to understand the underlying patterns, trends and behaviours in the data as well as to make forecasts about future values.
The key components of Time Series analysis are as follows:
Time series analysis approaches include a variety of techniques including Descriptive analysis to identify trends, patterns, and irregularities, smoothing techniques like moving averages or exponential smoothing to reduce noise and highlight underlying trends, Decompositions to separate the time series data into its individual components and forecasting technique like ARIMA , SARIMA, and Regression technique to predict the future values based on the trends.
Feature engineering is the process of selecting, transforming, and creating features from raw data in order to build more effective and accurate machine learning models. The primary goal of feature engineering is to identify the most relevant features or create the relevant features by combining two or more features using some mathematical operations from the raw data so that it can be effectively utilized for getting predictive analysis by machine learning model.
The following are the key elements of feature engineering:
Data normalization is the process of transforming numerical data into standardised range. The objective of data normalization is scale the different features (variables) of a dataset onto a common scale, which make it easier to compare, analyze, and model the data. This is particularly important when features have different units, scales, or ranges because if we doesn’t normalize then each feature has different-different impact which can affect the performance of various machine learning algorithms and statistical analyses.
Common normalization techniques are as follows:
For data analysis in Python, many great libraries are used due to their versatility, functionality, and ease of use. Some of the most common libraries are as follows:
Structured and unstructured data depend on the format in which the data is stored. Structured data is information that has been structured in a certain format, such as a table or spreadsheet. This facilitates searching, sorting, and analyzing. Unstructured data is information that is not arranged in a certain format. This makes searching, sorting, and analyzing more complex.
The differences between the structured and unstructured data are as follows:
| Feature | Structured Data | Unstructured Data |
|---|---|---|
| Structure of data | Schema (structure of data) is often rigid and organized into rows and columns | No predefined relationships between data elements. |
| Searchability | Excellent for searching, reporting, and querying | Difficult to search |
| Analysis | Simple to quantify and process using standard database functions. | No fixed format, making it more challenging to organize and analyze. |
| Storage | Relational databases | Data lakes |
| Examples | Customer records, product inventories, financial data | Text documents, images, audio, video |
Pandas is one of the most widely used Python libraries for data analysis. It has powerful tools and data structure which is very helpful in analyzing and processing data. Some of the most useful functions of pandas which are used for various tasks involved in data analysis are as follows:
In pandas, Both Series and Dataframes are the fundamental data structures for handling and analyzing tabular data. However, they have distinct characteristics and use cases.
A series in pandas is a one-dimensional labelled array that can hold data of various types like integer, float, string etc. It is similar to a NumPy array, except it has an index that may be used to access the data. The index can be any type of object, such as a string, a number, or a datetime.
A pandas DataFrame is a two-dimensional labelled data structure resembling a table or a spreadsheet. It consists of rows and columns, where each column can have a different data type. A DataFrame may be thought of as a collection of Series, where each column is a Series with the same index.
The key differences between the pandas Series and Dataframes are as follows:
| pandas Series | pandas DataFrames |
|---|---|
| A one-dimensional labelled array that can hold data of various types like (integer, float, string, etc.) | A two-dimensional labelled data structure that resembles a table or a spreadsheet. |
| Similar to the single vector or column in a spreadsheet | Similar to a spreadsheet, which can have multiple vectors or columns as well as. |
| Best suited for working with single-feature data | The versatility and handling of the multiple features make it suitable for tasks like data analysis. |
| Each element of the Series is associated with its label known as the index | DataFrames can be assumed as a collection of multiple Series, where each column shares the same index. |
One-hot encoding is a technique used for converting categorical data into a format that machine learning algorithms can understand. Categorical data is data that is categorized into different groups, such as colors, nations, or zip codes. Because machine learning algorithms often require numerical input, categorical data is represented as a sequence of binary values using one-hot encoding.
To one-hot encode a categorical variable, we generate a new binary variable for each potential value of the category variable. For example, if the category variable is “color” and the potential values are “red,” “green,” and “blue,” then three additional binary variables are created: “color_red,” “color_green,” and “color_blue.” Each of these binary variables would have a value of 1 if the matching category value was present and 0 if it was not.
A boxplot is a graphic representation of data that shows the distribution of the data. It is a standardized method of the distribution of a data set based on its five-number summary of data points: the minimum, first quartile [Q1], median, third quartile [Q3], and maximum.
Boxplot is used for detection the outliers in the dataset by visualizing the distribution of data.
Descriptive statistics and inferential statistics are the two main branches of statistics
Measures of central tendency are the statistical measures that represent the centre of the data set. It reveals where the majority of the data points generally cluster. The three most common measures of central tendency are:
Measures of dispersion , also known as measures of variability or spread, indicate how much the values in a dataset deviate from the central tendency. They help in quantifying how far the data points vary from the average value.
Some of the common Measures of dispersion are as follows:
A probability distribution is a mathematical function that estimates the probability of different possible outcomes or events occurring in a random experiment or process. It is a mathematical representation of random phenomena in terms of sample space and event probability , which helps us understand the relative possibility of each outcome occurring.
There are two main types of probability distributions:
A normal distribution , also known as a Gaussian distribution, is a specific type of probability distribution with a symmetric, bell-shaped curve. The data in a normal distribution clustered around a central value i.e mean, and the majority of the data falls within one standard deviation of the mean. The curve gradually tapers off towards both tails, showing that extreme values are becoming
distribution having a mean equal to 0 and standard deviation equal to 1 is known as standard normal distribution and Z-scores are used to measure how many standard deviations a particular data point is from the mean in standard normal distribution.
Normal distributions are a fundamental concept that supports many statistical approaches and helps researchers understand the behaviour of data and variables in a variety of scenarios.
The Central Limit Theorem (CLT) is a fundamental concept in statistics that states that, under certain conditions, the distribution of sample means approaches a normal distribution as sample size rises, regardless of the the original population distribution. In other words, even if the population distribution is not normal, when the sample size is high enough, the distribution of sample means will tend to be normal.
The Central Limit Theorem has three main assumptions:
In statistics, the null and alternate hypotheses are two mutually exclusive statements regarding a population parameter. A hypothesis test analyzes sample data to determine whether to accept or reject the null hypothesis. Both null and alternate hypotheses represent the opposing statements or claims about a population or a phenomenon under investigation.
A p-value , which stands for “probability value,” is a statistical metric used in hypothesis testing to measure the strength of evidence against a null hypothesis. When the null hypothesis is considered to be true, it measures the chance of receiving observed outcomes (or more extreme results). In layman’s words, the p-value determines whether the findings of a study or experiment are statistically significant or if they might have happened by chance.
The p-value is a number between 0 and 1, which is frequently stated as a decimal or percentage. If the null hypothesis is true, it indicates the probability of observing the data (or more extreme data).
The significance level , often denoted as α (alpha), is a critical parameter in hypothesis testing and statistical analysis. It defines the threshold for determining whether the results of a statistical test are statistically significant. In other words, it sets the standard for deciding when to reject the null hypothesis (H0) in favor of the alternative hypothesis (Ha).
If the p-value is less than the significance level, we reject the null hypothesis and conclude that there is a statistically significant difference between the groups.
The choice of a significance level involves a trade-off between Type I and Type II errors. A lower significance level (e.g., α = 0.01) decreases the risk of Type I errors while increasing the chance of Type II errors (failure to identify a real impact). A higher significance level (e.g., = 0.10), on the other hand, increases the probability of Type I errors while decreasing the chance of Type II errors.
In hypothesis testing, When deciding between the null hypothesis (H0) and the alternative hypothesis (Ha), two types of errors may occur. These errors are known as Type I and Type II errors, and they are important considerations in statistical analysis.
The confidence interval is a statistical concept used to estimates the uncertainty associated with estimating a population parameter (such as a population mean or proportion) from a sample. It is a range of values that is likely to contain the true value of a population parameter along with a level of confidence in that statement.
The relationship between point estimates and confidence intervals can be summarized as follows:
For example, A 95% confidence interval indicates that you are 95% confident that the real population parameter falls inside the interval. A 95% confidence interval for the population mean (μ) can be expressed as :
where x̄ is the point estimate (sample mean), and the margin of error is calculated using the standard deviation of the sample and the confidence level.
ANOVA , or Analysis of Variance, is a statistical technique used for analyzing and comparing the means of two or more groups or populations to determine whether there are statistically significant differences between them or not. It is a parametric statistical test which means that, it assumes the data is normally distributed and the variances of the groups are identical. It helps researchers in determining the impact of one or more categorical independent variables (factors) on a continuous dependent variable.
ANOVA works by partitioning the total variance in the data into two components:
Depending on the investigation’s design and the number of independent variables, ANOVA has numerous varieties:
Correlation is a statistical term that analyzes the degree of a linear relationship between two or more variables. It estimates how effectively changes in one variable predict or explain changes in another.Correlation is often used to access the strength and direction of associations between variables in various fields, including statistics, economics.
The correlation between two variables is represented by correlation coefficient, denoted as “r”. The value of “r” can range between -1 and +1, reflecting the strength of the relationship:
The Z-test, t-test, and F-test are statistical hypothesis tests that are employed in a variety of contexts and for a variety of objectives.
The key differences between the Z-test, T-test, and F-test are as follows:
Linear regression is a statistical approach that fits a linear equation to observed data to represent the connection between a dependent variable (also known as the target or response variable) and one or more independent variables (also known as predictor variables or features). It is one of the most basic and extensively used regression analysis techniques in statistics and machine learning. Linear regression presupposes that the independent variables and the dependent variable have a linear relationship.
A simple linear regression model can be represented as:
[Tex]Y = \beta_0 + \beta_1X + \epsilon [/Tex]
DBMS stands for Database Management System. It is software designed to manage, store, retrieve, and organize data in a structured manner. It provides an interface or a tool for performing CRUD operations into a database. It serves as an intermediary between the user and the database, allowing users or applications to interact with the database without the need to understand the underlying complexities of data storage and retrieval.
SQL CRUD stands for CREATE, READ(SELECT), UPDATE, and DELETE statements in SQL Server. CRUD is nothing but Data Manipulation Language (DML) Statements. CREATE operation is used to insert new data or create new records in a database table, READ operation is used to retrieve data from one or more tables in a database, UPDATE operation is used to modify existing records in a database table and DELETE is used to remove records from the database table based on specified conditions. Following are the basic query syntax examples of each operation:
CREATE
It is used to create the table and insert the values in the database. The commands used to create the table are as follows:
INSERT INTO employees (first_name, last_name, salary)
VALUES ('Pawan', 'Gunjan', 50000);
READ
Used to retrive the data from the table
SELECT * FROM employees;
UPDATE
Used to modify the existing records in the database table
UPDATE employees
SET salary = 55000
WHERE last_name = 'Gunjan';
DELETE
Used to remove the records from the database table
DELETE FROM employees
WHERE first_name = 'Pawan';
We use the ‘ INSERT ‘ statement to insert new records into a table. The ‘INSERT INTO’ statement in SQL is used to add new records (rows) to a table.
Syntax
INSERT INTO table_name (column1, column2, column3, . )
VALUES (value1, value2, value3, . );
Example
INSERT INTO Customers (CustomerName, City, Country)
VALUES ('Shivang', 'Noida', 'India');
We can filter records using the ‘ WHERE ‘ clause by including ‘WHERE’ clause in ‘SELECT’ statement, specifying the conditions that records must meet to be included.
Syntax
SELECT column1, column2, .
FROM table_name
WHERE condition;
Example : In this example, we are fetching the records of employee where job title is Developer.
SELECT * FROM employees
WHERE job_title = 'Developer';
We can sort records in ascending or descending order by using ‘ ORDER BY ; clause with the ‘SELECT’ statement. The ‘ORDER BY’ clause allows us to specify one or more columns by which you want to sort the result set, along with the desired sorting order i.e ascending or descending order.
Syntax for sorting records in ascending order
SELECT column1, column2, .
FROM table_name
ORDER BY Column_To_Sort1 ASC, Column_To_Sort2 ASC, . ;
Example: This statement selects all customers from the ‘Customers’ table, sorted ascending by the ‘Country’
SELECT * FROM Customers
ORDER BY Country ASC;
Syntax for sorting records in descending order
SELECT column1, column2, .
FROM table_name
ORDER BY column_to_sort1 DESC, column_to_sort2 DESC, . ;
Example: This statement selects all customers from the ‘Customers’ table, sorted descending by the ‘Country’ column
SELECT * FROM Customers
ORDER BY Country DESC;
The purpose of GROUP BY clause in SQL is to group rows that have the same values in specified columns. It is used to arrange different rows in a group if a particular column has the same values with the help of some functions.
Syntax
SELECT column1, function_name(column2)
FROM table_name
GROUP BY column_name(s);
Example: This SQL query groups the ‘CUSTOMER’ table based on age by using GROUP BY
SELECT AGE, COUNT(Name)
FROM CUSTOMERS
GROUP BY AGE;
An aggregate function groups together the values of multiple rows as input to form a single value of more significant meaning. It is also used to perform calculations on a set of values and then returns a single result. Some examples of aggregate functions are SUM, COUNT, AVG, and MIN/MAX.
SUM: It calculates the sum of values in a column.
Example: In this example, we are calculating sum of costs from cost column in PRODUCT table.
SELECT SUM(Cost)
FROM Products;
COUNT: It counts the number of rows in a result set or the number of non-null values in a column.
Example: Ij this example, we are counting the total number of orders in an “orders” table.
SELECT COUNT(*)
FROM Orders;
AVG: It calculates the average value of a numeric column.
Example: In this example, we are finding average salary of employees in an “employees” table.
SELECT AVG(Price)
FROM Products;
MAX: It returns the maximum value in a column.
Example: In this example, we are finding the maximum temperature in the ‘weather’ table.
SELECT MAX(Price)
FROM Orders;
MIN: It returns the minimum value in a column.
Example: In this example, we are finding the minimum price of a product in a “products” table.
SELECT MIN(Price)
FROM Products;
SQL Join operation is used to combine data or rows from two or more tables based on a common field between them. The primary purpose of a join is to retrieve data from multiple tables by linking records that have a related value in a specified column. There are different types of join i.e, INNER, LEFT, RIGHT, FULL. These are as follows:
INNER JOIN: The INNER JOIN keyword selects all rows from both tables as long as the condition is satisfied. This keyword will create the result-set by combining all rows from both the tables where the condition satisfies i.e the value of the common field will be the same.
Example:
SELECT customers.customer_id, orders.order_id
FROM customers
INNER JOIN orders
ON customers.customer_id = orders.customer_id;
LEFT JOIN: A LEFT JOIN returns all rows from the left table and the matching rows from the right table.
Example:
SELECT departments.department_name, employees.first_name
FROM departments
LEFT JOIN employees
ON departments.department_id = employees.department_id;
RIGHT JOIN: RIGHT JOIN is similar to LEFT JOIN. This join returns all the rows of the table on the right side of the join and matching rows for the table on the left side of the join.
Example:
SELECT employees.first_name, orders.order_id
FROM employees
RIGHT JOIN orders
ON employees.employee_id = orders.employee_id;
FULL JOIN: FULL JOIN creates the result set by combining the results of both LEFT JOIN and RIGHT JOIN. The result set will contain all the rows from both tables.
Example:
SELECT customers.customer_id, orders.order_id
FROM customers
FULL JOIN orders
ON customers.customer_id = orders.customer_id;
To retrieve data from multiple related tables, we generally use ‘SELECT’ statement along with help of ‘ JOIN ‘ operation by which we can easily fetch the records from the multiple tables. Basically, JOINS are used when there are common records between two tables. There are different types of joins i.e. INNER, LEFT, RIGHT, FULL JOIN. In the above question, detailed explanation is given regarding JOIN so you can refer that.
A subquery is defined as query with another query. A subquery is a query embedded in WHERE clause of another SQL query. Subquery can be placed in a number of SQL clause: WHERE clause, HAVING clause, FROM clause. Subquery is used with SELECT, INSERT, DELETE, UPDATE statements along with expression operator. It could be comparison or equality operator such as =>,=,
Example 1: Subquery in the SELECT Clause
SELECT customer_name,
(SELECT COUNT(*) FROM orders WHERE orders.customer_id = customers.customer_id) AS order_count
FROM customers;
Example 2: Subquery in the WHERE Clause
SELECT employee_name, salary
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);
Example 3: Subquery in the FROM Clause (Derived Tables)
SELECT category, SUM(sales) AS total_sales
FROM (SELECT product_id, category, sales FROM products) AS derived_table
GROUP BY category;
46. Can you give an example of using a subquery in combination with an IN or EXISTS condition?
We can use subquery in combination with IN or EXISTS condition. Example of using a subquery in combination with IN is given below. In this example, we will try to find out the geek’s data from table geeks_data, those who are from the computer science department with the help of geeks_dept table using sub-query.
Using a Subquery with IN
SELECT f_name, l_name
FROM geeks_data
WHERE dept IN
(SELECT dep_name FROM geeks_dept WHERE dept_id = 1);
Using a Subquery with EXISTS:
SELECT DISTINCT store_t
FROM store
WHERE EXISTS (SELECT * FROM city_store WHERE city_store.store_t = store.store_t);
In SQL, the HAVING clause is used to filter the results of a GROUP BY query depending on aggregate functions applied to grouped columns. It allows you to filter groups of rows that meet specific conditions after grouping has been performed. The HAVING clause is typically used with aggregate functions like SUM, COUNT, AVG, MAX, or MIN.
The main differences between HAVING and WHERE clauses are as follows:
SELECT customer_id, SUM(order_total) AS total_order_amount
FROM orders
GROUP BY customer_id
HAVING SUM(order_total) > 1000;
SELECT customer_id, SUM(order_total) AS total_order_amount
FROM orders
GROUP BY customer_id
WHERE total_order_amount > 1000;
In SQL, the UNION and UNION ALL operators are used to combine the result sets of multiple SELECT statements into a single result set. These operators allow you to retrieve data from multiple tables or queries and present it as a unified result. However, there are differences between the two operators:
The UNION operator returns only distinct rows from the combined result sets. It removes duplicate rows and returns a unique set of rows. It is used when you want to combine result sets and eliminate duplicate rows.
Syntax:
SELECT column1, column2, .
FROM table1
UNION
SELECT column1, column2, .
FROM table2;
Example:
select name, roll_number
from student
UNION
select name, roll_number
from marks
The UNION ALL operator returns all rows from the combined result sets, including duplicates. It does not remove duplicate rows and returns all rows as they are. It is used when you want to combine result sets but want to include duplicate rows.
Syntax:
SELECT column1, column2, .
FROM table1
UNION ALL
SELECT column1, column2, .
FROM table2;
Example:
select name, roll_number
from student
UNION ALL
select name, roll_number
from marks
Database Normalization is the process of reducing data redundancy in a table and improving data integrity. It is a way of organizing data in a database. It involves organizing the columns and tables in the database to ensure that their dependencies are correctly implemented using database constraints.
It is important because of the following reasons:
Normalization can take numerous forms, the most frequent of which are 1NF (First Normal Form), 2NF (Second Normal Form), and 3NF (Third Normal Form). Here’s a quick rundown of each:
In SQL, window functions provide a way to perform complex calculations and analysis without the need for self-joins or subqueries.
SELECT col_name1,
window_function(col_name2)
OVER([PARTITION BY col_name1] [ORDER BY col_name3]) AS new_col
FROM table_name;provides
Example :
SELECT
department,
AVG(salary) OVER(PARTITION BY department ORDER BY employee_id) AS avg_salary
FROM
employees;
Primary keys and foreign keys are two fundamental concepts in SQL that are used to build and enforce connections between tables in a relational database management system (RDBMS).
Database transactions are the set of operations that are usually used to perform logical work. Database transactions mean that data in the database has been changed. It is one of the major characteristics provided in DBMS i.e. to protect the user’s data from system failure. It is done by ensuring that all the data is restored to a consistent state when the computer is restarted. It is any one execution of the user program. Transaction’s one of the most important properties is that it contains a finite number of steps.
They are important to maintain data integrity because they ensure that the database always remains in a valid and consistent state, even in the presence of multiple users or several operations. Database transactions are essential for maintaining data integrity because they enforce ACID properties i.e, atomicity, consistency, isolation, and durability properties. Transactions provide a solid and robust mechanism to ensure that the data remains accurate, consistent, and reliable in complex and concurrent database environments. It would be challenging to guarantee data integrity in relational database systems without database transactions.
In SQL, NULL is a special value that usually represents that the value is not present or absence of the value in a database column. For accurate and meaningful data retrieval and manipulation, handling NULL becomes crucial. SQL provides IS NULL and IS NOT NULL operators to work with NULL values.
IS NULL: IS NULL operator is used to check whether an expression or column contains a NULL value.
Syntax:
SELECT column_name(s) FROM table_name WHERE column_name IS NULL;
Example: In the below example, the query retrieves all rows from the employee table where the middle name contains NULL values.
SELECT * FROM employees WHERE mid_name IS NULL;
IS NOT NULL: IS NOT NULL operator is used to check whether an expression or column does not contain a NULL value.
Syntax:
SELECT column_name(s) FROM table_name WHERE column_name IS NOT NULL;
Example: In the below example, the query retrieves all rows from the employee table where the first name does not contains NULL values.
SELECT * FROM employees WHERE first_name IS NOT NULL;
Normalization is used in a database to reduce the data redundancy and inconsistency from the table. Denormalization is used to add data redundancy to execute the query as quick as possible.
In Tableau, dimensions and measures are two fundamental types of fields used for data visualization and analysis. They serve distinct purposes and have different characteristics:
Tableau is a robust data visualization and business intelligence solution that includes a variety of components for producing, organizing, and sharing data-driven insights. Here’s a rundown of some of Tableau’s primary components:
The different products of Tableau are as follows :
In Tableau, joining and blending are ways for combining data from various tables or data sources. However, they are employed in various contexts and have several major differences:
In Tableau, fields can be classified as discrete or continuous, and the categorization determines how the field is utilized and shown in visualizations. The following are the fundamental distinctions between discrete and continuous fields in Tableau:
In Tableau, There are two ways to attach data to visualizations: live connections and data extracts (also known as extracts). Here’s a rundown of the fundamental distinctions between the two:
Tableau allows you to make many sorts of joins to mix data from numerous tables or data sources. Tableau’s major join types are:
You may use calculated fields in Tableau to make calculations or change data based on your individual needs. Calculated fields enable you to generate new values, execute mathematical operations, use conditional logic, and many other things. Here’s how to add a calculated field to Tableau:
Tableau has many different data aggregation functions used in tableau:
The Difference Between .twbx And .twb are as follows:
Tableau supports 7 variousvarious different data types:
The parameter is a dynamic control that allows a user to input a single value or choose from a predefined list of values. In Tableau, dashboards and reports, parameters allow for interactivity and flexibility by allowing users to change a variety of visualization-related elements without having to perform substantial editing or change the data source.
Filters are the crucial tools for data analysis and visualization in Tableau. Filters let you set the requirements that data must meet in order to be included or excluded, giving you control over which data will be shown in your visualizations.
There are different types of filters in Tableau:
The difference between Sets and Groups in Tableau are as follows:
Tableau offers a wide range of charts and different visualizations to help users explore and present the data effectively. Some of the charts in Tableau are:
The key steps to create a map in Tableau are:
The key steps to create a doughnut chart in tableau:
The key steps to create a dual-axis chart in tableau are as follows:
A Gantt Chart has horizontal bars and sets out on two axes. The tasks are represented by Y-axis, and the time estimates are represented by the X-axis. It is an excellent approach to show which tasks may be completed concurrently, which needs to be prioritized, and how they are dependent on one another.
Gantt Chart is a visual representation of project schedules, timelines or task durations. To illustrate tasks, their start and end dates, and their dependencies, this common form of chat is used in project management. Gantt charts are a useful tool in tableau for tracking and analyzing project progress and deadlines since you can build them using a variety of dimensions and measures.
The Difference Between Treemaps and Heat Maps are as follows:
| Basis | Tree Maps | Heat Maps |
|---|---|---|
| Representation | Tree maps present hierarchical data in a nested, rectangular format. The size and color of each rectangle, which each represents a category or subcategory, conveys information. | Heat maps uses color intensity to depict values in a grid. They are usually used to depict the distribution or concentration of data points in a 2D space. |
| Data Type | They are used to display hierarchical and categorical data. | They are used to display continuous data such as numeric values. |
| Color Usage | Color is frequently used n tree maps to represent a particular attribute or measure. The intensity of the color can convey additional information. | In heat maps, values are typically denoted by color intensity. Lower values are represented by lighter colors and higher values by brighter or darker colors. |
| Interactivity | It is possible for tree maps to be interactive, allowing users to click on the rectangle to uncover subcategories and drill down into hierarchical data. | Heat maps can be interactive, allowing users to hover over the cells to see specific details or values. |
| Use Case | They are used for visualizing organizational structures, hierarchical data and categorical data. | They are used in various fields like finance, geographic data, data analysis, etc. |
If two measures have the same scale and share the same axis, they can be combined using the blended axis function. The trends could be misinterpreted if the scales of the two measures are dissimilar.
77. What is the Level of Detail (LOD) Expression in Tableau?
A Level of Detail Expression is a powerful feature that allows you to perform calculations at various levels of granularity within your data visualization regardless of the visualization’s dimensions and filters. For more control and flexibility when aggregating or disaggregating data based on the particular dimensions or fields, using LOD expressions.
There are three types of LOD:
Handling null values, erroneous data types, and unusual values is an important element of Tableau data preparation. The following are some popular strategies and recommended practices for coping with data issues:
To create dynamic webpages with interactive tableau visualizations, you can embed tableau dashboard or report into a web application or web page. It provides embedding options and APIs that allows you to integrate tableau content into a web application.
Following steps to create a dynamic webpage in tableau:
Key Performance Indicators or KPI are the visual representations of the significant metrics and performance measurements that assist organizations in monitoring their progress towards particular goals and objectives. KPIs offer a quick and simple approach to evaluate performance, spot patterns, and make fact-based decisions.
Context filter is a feature that allows you to optimize performance and control data behavior by creating a temporary data subset based on a selected filter. When you designate a filter as a context filter, tableau creates a smaller temporary table containing only the data that meets the criteria of that particular filter. This decrease in data capacity considerably accelerates processing and rendering for visualization, which is especially advantageous for huge datasets. When handling several filters in a workbook, context filters are useful because they let you select the order in which filters are applied, ensuring a sensible filtering process.
You can create a dynamic title for a worksheet by using parameters, calculated fields and dashboards. Here are some steps to achieve this:
Data Source filtering is a method used in reporting and data analysis applications like Tableau to limit the quantity of data obtained from a data source based on predetermined constraints or criteria. It affects performance by lowering the amount of data that must be sent, processed, and displayed, which may result in a quicker query execution time and better visualization performance. It involves applying filters or conditions at the data source level, often within
the SQL query sent to the database or by using mechanisms designed specially for databases.
Impact on performance:
Data source filtering improves performance by reducing the amount of data retrieved from the source. It leads to faster query execution. shorter data transfer times, and quick visualization rendering. by applying filters based on criteria minimizes resource consumption and optimizes network traffic, resulting in a more efficient and responsive data analysis process.
To link R and Tableau, we can use R integration features provided by Tableau. Here are the steps to do so:
Exporting tableau visualizations to other formats such as PDF or images, is a common task for sharing or incorporating your visualizations into reports or presentations. Here are the few steps to do so:
To sum up, data is like gold in the modern age, and being a data analyst is an exciting career. Data analysts work with information, using tools to uncover important insights from sources like business transactions or social media. They help organizations make smart decisions by cleaning and organizing data, spotting trends, and finding patterns. If you’re interested in becoming a data analyst, don’t worry about interview questions.
This article introduces the top 85 common questions and answers, making it easier for you to prepare and succeed in your data analyst interviews. Let’s get started on your path to a data-driven career!
