Stepping into the realm of data science interviews can feel like embarking on a thrilling adventure filled with puzzles to solve and insights to uncover. If your path leads you to Tredence, a leading name in data science consulting, preparation is key to success. To guide you on this exciting journey, we’ve curated a collection of essential interview questions and expert answers tailored for Tredence’s data science interviews. Whether you’re diving into machine learning algorithms, discussing impactful projects, or navigating the nuances of data cleaning, this guide will equip you with the knowledge and confidence needed to excel in your Tredence interview. Let’s dive in!
Table of Contents
SQL Questions
Question: What is SQL?
Answer: SQL (Structured Query Language) is a standard language for managing relational databases. It is used to perform various tasks such as querying data, updating data, and defining and modifying the structure of databases.
Question: What are the different types of SQL commands?
Answer:
- Data Query Language (DQL): Used to retrieve data from the database. Example: SELECT
- Data Definition Language (DDL): Used to define the structure of the database objects. Example: CREATE, ALTER, DROP
- Data Manipulation Language (DML): Used to manipulate the data in the database. Example: INSERT, UPDATE, DELETE
- Data Control Language (DCL): Used to manage the permissions on the database objects. Example: GRANT, REVOKE
Question: Explain the difference between WHERE and HAVING clauses.
Answer:
- WHERE clause: Used to filter rows before data is grouped and returned. It is applied to individual rows before they are grouped.
- HAVING clause: Used to filter groups of rows after data has been grouped and summarized. It is applied to the results of the grouping.
Question: What is the difference between CHAR and VARCHAR data types?
Answer:
CHAR: Fixed-length character data type where you define the length of the string. It will pad spaces to the right if the string length is less than the defined length.
VARCHAR: Variable-length character data type where you define the maximum length of the string, but it will only use as much space as needed.
Question: Explain the difference between TRUNCATE and DELETE commands.
Answer:
- DELETE: Removes rows one by one and logs individual row deletions. It also allows the use of a WHERE clause to delete specific rows.
- TRUNCATE: Removes all rows from a table but does not log individual row deletions. It is faster than DELETE as it deallocates the data pages used to store the table data.
Question: What is a Primary Key?
Answer: A Primary Key is a column (or a set of columns) that uniquely identifies each row in a table. It must have unique values and cannot contain NULL values.
Question: What is a Foreign Key?
Answer: A Foreign Key is a column or a set of columns in a table that refers to the Primary Key in another table. It establishes a link between two tables by enforcing referential integrity.
Question: What is a View in SQL?
Answer: A View is a virtual table based on the result set of an SQL statement. It does not store data itself but instead displays data from the tables on which it is based. Views can simplify complex queries and provide an additional level of security by restricting access to certain columns.
Question: Explain the difference between UNION and UNION ALL.
Answer:
- UNION: Combines the result sets of two or more SELECT statements into a single result set, removing duplicate rows.
- UNION ALL: Also combines the result sets of two or more SELECT statements into a single result set but does not remove duplicate rows. It is generally faster than UNION as it does not perform the duplicate removal operation
SQL queries on Joins
Question: What are the different types of joins in SQL?
Answer:
- Inner Join: Returns records that have matching values in both tables.
- Left Join (or Left Outer Join): Returns all records from the left table (Table A), and the matched records from the right table (Table B). If there’s no match, NULL values are returned from the right side.
- Right Join (or Right Outer Join): Returns all records from the right table (Table B), and the matched records from the left table (Table A). If there’s no match, NULL values are returned from the left side.
- Full Join (or Full Outer Join): Returns records when there is a match in either the left or right table. If there’s no match, NULL values are returned on the opposite side.
Question: Explain an Inner Join and provide an example.
Answer: An Inner Join returns rows when there is at least one match in both tables.
Example:
SELECT Orders.OrderID, Customers.CustomerName
FROM Orders
INNER JOIN Customers ON Orders.CustomerID = Customers.CustomerID;
Question: What is the difference between a Left Join and a Right Join?
Answer: The main difference is in which side of the join retains all of its rows. In a Left Join, all rows from the left table are retained, with matching rows from the right table. In a Right Join, all rows from the right table are retained, with matching rows from the left table.
Question: Provide an example of a Left Join.
Answer:
SELECT Employees.LastName, Employees.FirstName, Orders.OrderID
FROM Employees
LEFT JOIN Orders ON Employees.EmployeeID = Orders.EmployeeID
ORDER BY Employees.LastName;
Question: Explain a Full Join and when it might be useful.
Answer: A Full Join returns all records when there is a match in either the left or right table. It can be useful when you want to see all the records from both tables, whether they have matches or not.
Question: Write an example of a Full Join.
Answer:
SELECT Customers.CustomerName, Orders.OrderID
FROM Customers FULL JOIN Orders ON Customers.CustomerID = Orders.CustomerID ORDER BY Customers.CustomerName;
Question: What is a Self-Join?
Answer: A Self-Join is a regular join, but the table is joined with itself. It is used to join a table to itself as if the table were two tables, temporarily renaming at least one table in the SQL statement.
Question: Provide an example of a Self-Join.
Answer:
SELECT e1.EmployeeName, e2.EmployeeName AS Supervisor
FROM Employees e1
JOIN Employees e2 ON e1.SupervisorID = e2.EmployeeID;
Question: What is a Cross Join?
Answer: A Cross Join returns the Cartesian product of the two tables, which means it will return all possible combinations of rows between the two tables.
Other Technical Questions
Question: What is difference between list and set
Answer:
List:
- Maintains the order of elements, preserving their position.
- Allows duplicate elements within the list.
- Accessed by index for direct element retrieval.
- Mutable, enabling changes, additions, and deletions after creation.
- Defined using square brackets [] in languages like Python.
Set:
- Does not preserve the order of elements, storing them in an unordered way.
- Ensures all elements are unique, disallowing duplicates.
- Unavailable for direct element access by index due to the unordered nature.
- Mutable, allowing additions and removals, but not individual element changes.
- Defined using curly braces {} in languages like Python.
Question: What is Logistic regression?
Answer: Logistic regression is a statistical method used for binary classification tasks, where the goal is to predict a binary outcome (such as “yes” or “no”, “1” or “0”, “true” or “false”) based on input features. Despite its name, it is a classification algorithm, not a regression algorithm.
Question: What is Clustering?
Answer: Clustering is a type of unsupervised learning technique in machine learning and data mining. It involves grouping a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. The goal of clustering is to find inherent structures or patterns in the data without any prior knowledge of labels.
Question: What is Boosting?
Answer: Boosting is a machine learning ensemble technique used to improve the accuracy of models by combining the predictions of multiple individual models (often called “weak learners”) sequentially. The primary idea behind boosting is to train a series of weak learners sequentially, where each learner corrects their predecessor’s mistakes, ultimately leading to a strong learner.
Question: What is Bagging?
Answer: Bagging, short for Bootstrap Aggregating, is an ensemble machine learning technique that aims to improve the stability and accuracy of models by training multiple instances of the same base learning algorithm on different subsets of the training data. The basic idea behind bagging is to reduce variance and prevent overfitting by averaging or voting the predictions of these individual models.
Question: Over-fitting prevention techniques?
Answer: Overfitting occurs when a machine learning model learns the training data too well, capturing noise and random fluctuations in the data as if they were meaningful patterns. This leads to poor generalization, where the model performs well on the training data but fails to generalize to unseen data. To prevent overfitting, various techniques can be employed:
Question: Cross-Validation: Split data into k subsets, training on k-1 and validating on 1, repeating k times.
Answer:
- Regularization: L1 (Lasso) and L2 (Ridge) penalties to limit coefficient size and complexity.
- Early Stopping: Halt training when performance on a validation set starts to decline.
- Feature Selection: Remove irrelevant features to focus on important ones.
- Ensemble Methods: Bagging (Random Forest) and Boosting (AdaBoost, Gradient Boosting) combine models to improve generalization.
Question: Explain PCA.
Answer: PCA, or Principal Component Analysis, is a dimensionality reduction technique used to transform high-dimensional data into a new coordinate system called principal components. The main goal of PCA is to reduce the number of dimensions (features) in the data while preserving as much variance as possible. This helps in visualizing and understanding the underlying structure of the data, removing noise, and speeding up machine learning algorithms.
Question: What is regression?
Answer: Regression is a statistical method used in machine learning and data analysis to model the relationship between a dependent variable (also called the target or outcome variable) and one or more independent variables (also called predictors or features). The primary goal of regression analysis is to predict the value of the dependent variable based on the values of the independent variables.
Question: What are the Different types of regression?
Answer:
Linear Regression:
- Assumes a linear relationship between variables x and y.
- Used for predicting continuous outcomes.
- Examples include Simple Linear Regression and Multiple Linear Regression.
Logistic Regression:
- Predicts the probability of binary outcomes (0 or 1).
- Useful for classification tasks, despite its name.
- Equation involves a sigmoid function for probability estimation.
Polynomial Regression:
- Captures non-linear relationships by adding polynomial terms.
- Allows modeling of curved patterns in the data.
- Examples include Quadratic and Cubic Regression.
Ridge Regression:
- Variation of linear regression with regularization.
- Adds a penalty term to the loss function to prevent overfitting.
- Particularly useful for handling multicollinearity.
Lasso Regression:
- Another form of regularized linear regression.
- Encourages sparsity by shrinking some coefficients to zero.
- Helps in feature selection and reducing model complexity.
Other Questions
Question: What are Subqueries?
Question: Model evaluation metrics?
Question: What are Bias variance tradeoff?
Question: How to tackle overfitting?
Question: Unbalanced dataset problem?
Question: Questions on regression
Question: How regression algorithm works in backend?
Question: Medium-level SQL queries were asked on joins, and pivot.
Technical Topics to Prepare
SQL, Python, and basics of ML and Stats.
Most questions will revolve around projects.
ML, algo, oops, python, puzzle
SQL questions and DSA (Array, Tree)
Rank Data Structures – Dictionary, Tuple, and Mid DS question
Puzzle Questions
Question: Rate yourself in SQL and Python.
Question: Given a two-hourglass, one measuring 4 mins and the other 7 mins, how to measure 9 mins.
Question: Guesstimate on how many people use mobile phones in India.
Conclusion
preparing for a data science interview at Tredence requires a solid understanding of core concepts, practical experience with machine learning algorithms, and the ability to articulate your insights effectively. By familiarizing yourself with these common interview questions and crafting thoughtful responses, you’ll be ready to showcase your skills and passion for data analytics. Remember to emphasize your problem-solving abilities, real-world project experiences, and enthusiasm for the future of AI and data science. With thorough preparation and a confident approach, you’ll be on your way to success in landing your dream data science role at Tredence. Best of luck on your interview journey!
