4 Design a view in Tableau to show State-wise Sales and Profit using the Sample Superstore dataset.
From the above map, it is clear that states like Washington, California, and New York have the highest sales and profits. While Texas, Pennsylvania, and Ohio have good amounts of sales but the least profits.
SQL Interview Questions for Data Analysts
To subset or filter data in SQL, we use WHERE and HAVING clauses.
Using this table, letâs find the records for movies that were directed by Brad Bird.
Now, letâs filter the table for directors whose movies have an average duration greater than 115 minutes.
2 Using the data given below, create a pivot table to find the total sales made by each sales representative for each item. Display the sales as % of the grand total.
3 What is a Subquery in SQL?
A Subquery in SQL is a query within another query. It is also known as a nested query or an inner query. Subqueries are used to enhance the data to be queried by the main query.Â
It is of two types – Correlated and Non-Correlated Query.
Below is an example of a subquery that returns the name, email id, and phone number of an employee from Texas city.
5 How can you add a column to a Pandas Data Frame?
Suppose there is an emp data frame that has information about a few employees. Letâs add an Address column to that data frame.
Declare a list of values that will be converted into an address column.
1 In Microsoft Excel, a numeric value can be treated as a text value if it precedes with what?
What are the different types of sampling techniques used by data analysts?
Sampling is a statistical method to select a subset of data from an entire dataset (population) to estimate the characteristics of the whole population.Â
There are majorly five types of sampling methods:
Define the term ‘Data Wrangling in Data Analytics.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format. Techniques such as merging, grouping, concatenating, joining, and sorting are used to analyze the data. Thereafter it gets ready to be used with another dataset.