Top 20 Questions To Prepare For Entry-Level Data Analyst Interview

''Data Analyst' is one of the most in-demand and heavily-relied upon roles in 2022 and will continue to be so for the foreseeable future. If you have a strong foothold in Statistics, Math, and Computer Science there is no question that you have what it takes to become a data analyst! 

Top Data Analysis Interview Questions for Entry-level

If you are preparing for your entry-level Data Analyst interview, here are some important interview questions.

Q1. What does the process of data analysis entail?

Ans: Data analysis is the process of analyzing, modelling, and interpreting data in order to extract critical business insights and make informed decisions. A Data Analyst wrangles very large amounts of data. By interpreting large sets of data, data analysts help organizations streamline their business strategies. 

The process involves the following steps: 

Q2 What are the key skills required for becoming a Data Analyst?

To become a data analyst, one needs to be well-versed with:

• Programming languages (Python, R, ETL frameworks), and databases (SQL, Db2, etc.), and also have extensive knowledge of reporting packages (Business Objects).
• Be able to organize, analyze, collect and disseminate Big Data.
• Have substantial technical knowledge in database design, data mining, and segmentation techniques.
• Have a sound knowledge of statistical packages for analyzing large datasets such as SAS (Statistical Analysis Software), Microsoft Excel, and SPSS (Statistical Package for the Social Sciences), to name a few. R and Python can also be used for statistical computing.
• Should be excellent at writing queries, reports, and presentations.
• Understanding of data visualization software including Tableau, PowerBI and Qlik. 
• Be able to create and apply accurate algorithms to datasets in order to find solutions.
• Problem-Solving, teamwork, and written and verbal Communication Skills.  

Q3 What are the key skills required for becoming a Data Analyst?

To become a data analyst, one needs to be well versed with:

• Programming languages (Python, R, ETL frameworks), and databases (SQL, Db2, etc.), and also have extensive knowledge of reporting packages (Business Objects).
• Be able to organize, analyze, collect and disseminate Big Data.
• Have substantial technical knowledge in database design, data mining, and segmentation techniques.
• Have a sound knowledge of statistical packages for analyzing large datasets such as SAS (Statistical Analysis Software), Microsoft Excel, and SPSS (Statistical Package for the Social Sciences), to name a few. R and Python can  also be used for statistical computing.
• Should be excellent at writing queries, reports, and presentations.
• Understanding of data visualization software including Tableau, PowerBI and Qlik. 
• Be able to create and apply accurate algorithms to datasets in order to find solutions.
• Problem-Solving, teamwork, and written and verbal Communication Skills.  

Q4 What is data visualization and how important is it? What tools are used for data visualization?

Data visualization is the graphical representation of information and data. It is a smart wat to view and analyze data in such a way that even non-data-savvy people can also understand the key insights. The visual elements in Data Science include diagrams, charts, and maps. These elements enable users to easily see and understand trends, patterns, and outliers. Tools such as Tableau, Wrapper, Microsoft Power BI, Google Charts, etc, can be used for data visualization purposes. 

Q5 What are the different challenges one faces during data analysis?

A Data Analyst can encounter the following issues while analyzing data: 

• Duplicate entries and spelling errors reduces the quality of data
• Data blending or integration from multiple sources is a challenge. Multiple representations of data obtained from different sources delays the process of data organization and further analysis. 
• One major challenge in data analysis is incomplete or missing data. This would invariably lead to errors or faulty results and at times adds a lot of noise when missing data is filled with other suitable data.
• Business stakeholders' unrealistic timelines and expectations  
• Inadequate data architecture and tools to achieve the goals on time.
• Data availability, security and privacy of data

Q6 Define Outlier. What are the different methods to detect outliers? 

Ans. An outlier is a value that appears to be far removed and divergent from a set pattern in a sample (reference image). There are two methods one can employ to detect outliers:

• Box plot method – The value is an outlier if it is higher or lesser than 1.5*IQR (interquartile range), in such a way that it lies below the lower quartile (Q1) or above the upper quartile (Q3).

• Standard deviation method – According to this method, if a value is higher or lower than the mean ± (3*standard deviation), the value is an outlier.

Outliers in box plot. 

Source:https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51

Q7 Name the statistical methods that are highly beneficial for data analysts?

• Spatial and cluster processes

• Bayesian method

• Markov process

• Imputation techniques

• Mathematical optimization

• Rank statistics, percentile, outliers detection

• Simplex algorithm

Q8  Explain Gaussian Distribution and the ‘68-95-99.7’ rule for a Gaussian Distribution.

Ans. Gaussian distribution, otherwise called Normal distribution, refers to a continuous probability distribution where values lie symmetrically and are mostly located around the mean. It plays a key role in statistics and forms the basis of Machine Learning. It defines and measures how the values of a variable are distributed, that is, how the values differ in their means and standard deviations. 

The Gaussian distribution is mostly associated with the ‘68-95-99.7’ rule (reference image). 99.7% of the data is within 3 standard deviations (σ) of the mean (μ), 95% of the data is within 2 standard deviations (σ) of the mean (μ), 68% of the data is within 1 standard deviation (σ) of the mean (μ).

Figure: Gaussian distribution curve

(Source:https://towardsdatascience.com/understanding-the-68-95-99-7-rule-for-a-normal-distribution-b7b7cbf760c2)

Q9 What are linear regression and logistic regression?

Ans. Linear regression is a form of statistical technique in which the score of a dependent variable ‘Y’ is predicted on the basis of the score of a second variable X, referred to as the predictor variable. The Y variable is also called the criterion variable.

Logistic regression, or the logit model, is a statistical technique for predicting the binary outcome from a linear combination of predictor variables. While logistic regression is used for solving classification problems, linear regression is used for solving regression problems.

Q10. What do you mean by collisions in a hash table? Explain the ways to avoid it.

Ans. Hash table collisions are typically caused when two keys have the same index. As two elements cannot share the same slot in an array, collisions result in a problem. 

You can use the following methods to avoid such collisions:

• Separate chaining – In this method, a data structure is used to store multiple items hashing to a common slot.

• Open addressing – This method looks out for empty slots and stores the item in the very first empty slot available

Q11. What are lambda functions in python?

Ans. In Python language, a lambda function also referred to as an  ‘Anonymous’ function is similar to other functions in python but can be defined without a name. Even though a lambda function can take any number of arguments, it can only have one expression. Lambda keywords are used to define these functions.

The syntax for the lambda function is as below:

lambda arguments: expression

Here is an example of using the lambda function that returns the product of two variables:

product = lambda x,y: x * y

print(product(2,5))

Output : 10

Q12. What is functional programming in Python? Briefly explain the concepts of Map, Filter, and Reduce functions in Python.

Functional programming in Python computes by evaluating functions. It allows us to write shorter code with faster implementation techniques. In functional programming, code depends on the evaluation of pure functions. 

Map, filter, and reduce are functions that help us handle all kinds of data collections. These functions are leveraged in modern technologies such as Spark and various other storage and data manipulation frameworks. But they can also be very powerful helpers when working with pure Python language. 

Map function: applies a function to all the items in an input_list. Here’s the syntax:

map(function_to_apply, sequence of elements)

Filter function: creates a list of elements for which a function returns true. Syntax:

filter(function_to_apply, sequence of elements)

Reduce function:  The reduce function is used to apply a particular function passed in its argument to all of the list elements mentioned in the sequence passed along. This function is defined in the “functools” module. Syntax:

import functools

functools.reduce(function_to_apply, sequence of elements)

Q13. List some of the Python libraries used by Data Analysts.

• NumPy

• Matplotlib

• Pandas

• Seaborn

• SciPy

• SciKit

Q14. What is the difference between DDL and DML in SQL?

Ans. Structured Query Language(SQL) is a database language that is used to perform certain operations on the existing database. This language is also used to create a database. SQL uses commands like Insert, Create, Drop, and so on, to carry out the required tasks.

DDL: Data Definition Language consists of the SQL commands that are used to define the database schema. It is used to create and modify the structure of database objects in the database and deals with descriptions of the database schema. 

List of a few DDL commands: 

CREATE: This command creates the database or its objects (like table, function, index, triggers, views, store procedure).

DROP: This command is used to delete objects from the database.

ALTER: This is used to alter the structure of the database. 

DML: Data Manipulation Language is used to manipulate data. All the SQL commands that deal with data manipulation present in the database belong to Data Manipulation Language. This includes most of the SQL statements. For instance, 

INSERT: This command helps you insert data into a table.

UPDATE: It is used to update existing data within a table.

DELETE : This command is used when you need to delete records from a database table.

Q15: What is a database management system and specifically comment on RDBMS.

Ans. A database is a collection of related data which represents a few aspects of the real world. A database system is designed to be populated by data for a particular task. A Database Management System (DBMS) is a software that stores and retrieves users’ data in accordance with the right security measures. It constitutes a set of programs that can manipulate the database. This system accepts the request for data from an application and provides instructions to the OS to provide the specific data. 

An RDBMS is a DBMS specifically designed for relational databases. A relational database stores data in a structured format, that is, using rows and columns. Accessing and locating specific values within this database is therefore easy. It is called a "relational database" because the values in each table are related to each other. Tables may also have relations with other tables. This relational structure enables us to run queries across multiple tables in one go. 

Q16:  What is a WHERE clause and a HAVING clause in SQL?

Ans. WHERE Clause in SQL is used to either filter the records from the table or can also be used while joining more than one table. Only those records will be extracted that satisfy the specified condition in the WHERE clause. This clause can be used with SELECT, UPDATE, and DELETE statements.

HAVING Clause filters the records from the groups on the basis of the given condition in the HAVING Clause. Those groups which satisfy the given condition will appear in the final result. HAVING Clause can only be used with a SELECT statement.

Q17:  Discuss the different types of joins in SQL.

Ans. A SQL join is an instruction used within the SQL (Structured Query Language) to merge data from two different tables on a key. There are 4 ways to join this data together: inner join, full join, left join, or right join.

INNER JOIN

An inner join allows you to output records that are referenced in both Table A and Table B. If there is a discrepancy between records in Table A and Table B, those records will not appear in the output.

FULL JOIN

A full join lists down all records from both the tables, whether the records have a match in the other table or not.

LEFT JOIN

A left join, as illustrated, will return all records from the left table, and any matching records from the right table.

RIGHT JOIN

A right join will return all records from the right table and any matching records in the left table. 

Q18. What is Normalization and what are its various forms?

Ans. Normalization refers to the process of organizing structured data in the database. The process includes the creation of tables, establishing relationships between the tables, and defining rules for the relationships. These rules will help you keep the inconsistency and redundancy in check based on these rules, thus adding flexibility to the database.

Normal Forms are used to either eliminate or reduce the redundancy in database tables. Normal forms can be First Normal form, Second Normal form, or Third Normal form.

Q19. List some common clauses used with SELECT query in SQL?

• WHERE clause : filters records that are inevitable, based on very specific conditions.
• ORDER BY clause :  sorts the records based on some field(s) in ascending (ASC) or descending order (DESC).
• GROUP BY :  groups records with identical data and can be used with some aggregation functions to produce summarized results from the database.
• HAVING clause : filters records in combination with the GROUP BY clause.

Q20 What are the differences between OLTP and OLAP?

Ans. OLTP or Online Transaction Processing is a software application class that is capable of supporting transaction-oriented programs. An important attribute of an OLTP system is its ability to maintain concurrency. These systems follow a decentralized architecture to do away with single points of failure. These systems are generally designed for a large audience of end-users who conduct short transactions. Queries involved in these kinds of databases are simple, require fast response times, and return comparatively few records. A number of transactions per second is an effective measure for these systems.

OLAP or Online Analytical Processing is a class of software programs that are characterized by the relatively low frequency of online transactions. Queries are very often too complicated and include a bunch of aggregations. For OLAP systems, the effectiveness measure relies highly on response time. These systems are used for data mining or maintaining aggregated, historical data within multi-dimensional schemas.

Conclusion

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