Are you gearing up for a data analytics interview? Whether you’re a seasoned professional or just starting your journey in the field, mastering the interview process is crucial for landing your dream role. In this comprehensive guide, we’ll walk you through essential data analytics interview questions and provide expert answers tailored for success at Tiger Analytics – a premier firm in the industry. From understanding the fundamentals to acing the toughest queries, let’s dive into the world of data analytics interviews and unlock the key to your next career milestone.
Table of Contents
Technical Questions
Question: What are the operators in SQL?
Answer: SQL utilizes various operators to perform operations on data within relational databases.
Arithmetic operators such as addition, subtraction, multiplication, division, and modulus are used for mathematical computations.
Comparison operators like equal to, not equal to, greater than, and less than, along with logical operators (AND, OR, NOT) facilitate comparisons and logical operations.
Additional operators include concatenation, NULL comparison, pattern matching, IN, BETWEEN, and EXISTS, each serving specific functions in querying and manipulating data.
Question: What are Tuple, List, and Dictionary in Python
Answer:
- Tuple: An ordered, immutable collection of elements denoted by parentheses ().
- List: An ordered, mutable collection of elements denoted by square brackets [].
- Dictionary: An unordered collection of key-value pairs, with keys associated with corresponding values, denoted by curly braces {}.
Each data structure serves different purposes: tuples for immutable sequences, lists for mutable sequences, and dictionaries for key-value mappings.
Question: What is the primary key?
Answer: A primary key is a unique identifier for each record in a database table. It ensures that each row in the table is uniquely identifiable and serves as a reference point for establishing relationships with other tables in a relational database. Primary keys must have unique values and cannot contain NULL values. They are typically defined when creating a table and can consist of one or multiple columns, though it’s common to use a single column as the primary key.
Question: What is DBMS and RDBMS?
Answer: A Database Management System (DBMS) is software that facilitates the creation, maintenance, and management of databases. It offers functionalities like data organization, retrieval, security, and concurrency control.
A Relational Database Management System (RDBMS) is a specific type of DBMS that follows the relational model of data. RDBMS organizes data into tables with rows and columns, emphasizing relationships between data entities. It enforces data integrity through features like primary keys, foreign keys, and normalization.
Examples of RDBMS include MySQL, PostgreSQL, Oracle Database, SQL Server, and SQLite, each providing robust features for managing relational databases efficiently.
Intermediate SQL and basic Python questions
Question: What is a JOIN in SQL?
Answer: A JOIN operation is used to combine rows from two or more tables based on a related column between them.
Question: What is the difference between INNER JOIN and OUTER JOIN?
Answer: INNER JOIN returns only the rows that have matching values in both tables, while OUTER JOIN returns all rows from both tables, with NULL values where there is no match.
Question: What is a subquery?
Answer: A subquery is a query nested within another query. It can be used within SELECT, INSERT, UPDATE, or DELETE statements to perform operations based on the results of the subquery.
Question: What is normalization in databases?
Answer: Normalization is the process of organizing data in a database efficiently. It involves reducing data redundancy and dependency by dividing large tables into smaller ones and defining relationships between them.
Question: Explain the difference between UNION and UNION ALL.
Answer: UNION removes duplicate rows from the combined result set, while UNION ALL retains all rows, including duplicates.
Python Interview Questions
Question: What is the difference between a list and a tuple in Python?
Answer: Lists are mutable, meaning their elements can be changed after creation, while tuples are immutable, meaning their elements cannot be changed after creation.
Question: What is the difference between ‘==’ and ‘is’ in Python?
Answer: ‘==’ is used to compare the values of two objects, while ‘is’ is used to check if two objects refer to the same memory location.
Question: What is dictionary comprehension in Python?
Answer: Dictionary comprehension is a concise way to create dictionaries in Python using a single line of code, similar to list comprehension but with key-value pairs.
Question: Explain the difference between ‘append()’ and ‘extend()’ methods in Python lists.
Answer: ‘append()’ is used to add a single element to the end of a list, while ‘extend()’ is used to add multiple elements, such as another list, to the end of a list.
Question: What is the purpose of the ‘if name == “main”‘ statement in Python?
Answer: It allows a Python file to be both imported as a module and run as a standalone script. Code within the ‘if name == “main”‘ block will only execute when the script is run directly, not when it’s imported as a module.
Question: Difference between a list and a tuple and a dictionary?
- List:
Mutable: Elements can be modified after creation.
Denoted by square brackets [].
Ordered collection: Elements are indexed and stored in the order they were added.
Example: my_list = [1, 2, ‘apple’].
- Tuple:
Immutable: Once created, elements cannot be changed.
Denoted by parentheses ().
Ordered collection: Elements are indexed and stored in the order they were added.
Example: my_tuple = (1, 2, ‘apple’).
- Dictionary:
Mutable: Key-value pairs can be added, modified, or deleted after creation.
Denoted by curly braces {}.
Unordered collection: Elements are not indexed and do not have an inherent order.
Example: my_dict = {‘name’: ‘John’, ‘age’: 30}.
Question: What are joins in SQL?
Answer: In SQL, joins are used to combine rows from two or more tables based on a related column between them. The JOIN operation allows you to retrieve data from multiple tables in a single query by specifying how the tables are related to each other. There are different types of joins, including:
- INNER JOIN: Returns only the rows that have matching values in both tables.
- LEFT JOIN (or LEFT OUTER JOIN): Returns all rows from the left table and matching rows from the right table. If there is no match, NULL values are returned for the columns from the right table.
- RIGHT JOIN (or RIGHT OUTER JOIN): Returns all rows from the right table and matching rows from the left table. If there is no match, NULL values are returned for the columns from the left table.
- FULL JOIN (or FULL OUTER JOIN): Returns all rows from both tables and matches rows from both tables where available. If there is no match, NULL values are returned for the columns from the table without a matching row.
- CROSS JOIN: Returns the Cartesian product of the two tables, meaning each row from the first table is combined with every row from the second table, resulting in a potentially large result set.
Question: What is Hypothesis Testing
Answer: Hypothesis testing is a statistical method used to make inferences about population parameters from sample data. It involves formulating null and alternative hypotheses, selecting an appropriate test, calculating a test statistic, and determining the likelihood of observing the test statistic under the null hypothesis (p-value). By comparing the p-value to a significance level, researchers decide whether to reject the null hypothesis, enabling valid conclusions to be drawn based on statistical evidence.
Other questions:
- Basic Probability, and Basic Coding in Python.
- Tell me something not in your resume.
- SQL queries.
- Write Python code to reverse a string.
- Create a dictionary that counts the number of characters in a word.
- Scenario-based questions on missing values, imputation, and outliers.
- Swap three variables.
- Pandas DataFrames— joins syntax.
- Basic questions on strings and arrays.
- Hypothesis Testing, Basic Probability, and Basic Coding in Python.
- MCQs on statistics and mathematical functions.
- SQL, python, ML, probability, and inferential stats.
- Questions based on OOPS.
- Questions Database and big data.
- Finding out the prime number from a given sequence.
- Basic level programming questions.
- Write a code to generate the Fibonacci series.
- Looping related questions on FOR loop.
Conclusion
Navigating a data analytics interview successfully requires a blend of technical expertise and communication prowess. By familiarizing yourself with common questions and crafting concise, insightful answers, you’ll be better prepared to showcase your skills and land your dream job in the data analytics field. Whether you’re aiming to join Tiger Analytics or another leading firm, mastering these interview essentials will set you on the path to success. Remember, preparation is key, so dive into these questions, hone your responses, and approach your interview with confidence. Here’s to your future in data analytics!
