Publicis Sapient Data Science Intern Interview Questions and Answers

They gave a span of 3 days to build an AI-powered webapp

Q1. How would you go about learning a new skill
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Q2. Experience in cloud technologies
Ans. 
I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
• Experience in setting up and managing virtual machines, storage, and networking in cloud environments

• Knowledge of cloud services like EC2, S3, RDS, and Lambda

• Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
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Q1. Tell me about yourself
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Q2. Project details and challenges faced in the project
Ans. 
Developed a predictive model for customer churn in a telecom company
• Collected and cleaned customer data from various sources

• Performed exploratory data analysis to identify key factors influencing churn

• Built and fine-tuned machine learning models to predict customer churn

• Challenges included imbalanced data, feature engineering, and model interpretability
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Q1. Extract only India Players from dictionary (using list comprehension) CSK = {"Dhoni" : "India", "Du Plessis" : "South Africa", "RituRaj": "India", "Peterson" : "England", "Lara" : "West Indies"}
Ans. 
Extract India players from a dictionary using list comprehension
• Use list comprehension to filter out players with nationality as 'India'

• Create a new list with only the India players

• Example: [player for player, nationality in CSK.items() if nationality == 'India']
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Q2. Find Common Elements in three lists using sets arr1 = [1,5,10,20,40,80,100] arr2 = [6,7,20,80,100] arr3 = [3,4,15,20,30,70,80,120]
Ans. 
Use sets to find common elements in three lists.
• Convert the lists to sets for efficient comparison.

• Use the intersection method to find common elements.

• Return the common elements as a set or list.
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Q3. Check if you found any error in the below code: school_name = 'ABC School' def __init__(self, name, roll_no): self.name = name self.roll_no = roll_no def show(self)" print('Ins...read more
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Q4. How do you print the 3, 5 and 7th row in a database (Python - use Pandas)
Ans. 
Printing specific rows from a database using Pandas in Python
• Use Pandas library to read the database into a DataFrame

• Use iloc method to select specific rows by index

• Print the selected rows
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Q5. Split Dataset in train, test and validation (import library and split dataset)
Ans. 
Use scikit-learn library to split dataset into train, test, and validation sets
• Import train_test_split from sklearn.model_selection

• Specify test_size and validation_size when splitting the dataset

• Example: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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Q6. Print Unique values in the dataset and delete the duplicate rows (SQL)
Ans. 
Use DISTINCT keyword to print unique values and DELETE with a subquery to remove duplicate rows.
• Use SELECT DISTINCT column_name FROM table_name to print unique values.

• Use DELETE FROM table_name WHERE row_id NOT IN (SELECT MAX(row_id) FROM table_name GROUP BY column_name) to delete duplicate rows.
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Q7. Print rows where a certain criterion is met (ex - in a dataset of employees select the ones whose salary is greater than 100000 - in SQL)
Ans. 
Use SQL SELECT statement with WHERE clause to filter rows based on a specific criterion.
• Use SELECT statement with WHERE clause to specify the criterion (ex: salary > 100000)

• Example: SELECT * FROM employees WHERE salary > 100000;

• Ensure proper syntax and column names are used in the query
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Q8. Print rows with the second highest criterion value without using offset function in SQL
Ans. 
Use subquery to find rows with second highest criterion value in SQL without using offset function.
• Use a subquery to find the maximum criterion value

• Then use another subquery to find the maximum value that is less than the maximum value found in the first subquery

• Finally, select rows with the second highest criterion value
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Q9. Print rows with the same set of values in column (these are not duplicates row - just the duplicates values in a column)
Ans. 
Print rows with the same set of values in a column
• Identify unique sets of values in the column

• Group rows based on these unique sets of values

• Print out the rows for each unique set of values
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Q10. Difference between Power BI and Tableau
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Power BI is a Microsoft product focused on business intelligence and data visualization, while Tableau is a standalone data visualization tool.
• Power BI is more user-friendly and integrates well with other Microsoft products.

• Tableau is known for its powerful data visualization capabilities and flexibility in creating complex visualizations.

• Power BI is often preferred by organizations already using Microsoft products, wh...read more
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Q11. Limitations of Power BI and Tableau
Ans. 
Power BI and Tableau have limitations in terms of data connectivity, customization, and pricing.
• Limited data connectivity options compared to other tools

• Limited customization capabilities for advanced analytics

• High pricing for enterprise-level features

• Tableau has better visualization capabilities but can be more complex to use

• Power BI is more user-friendly but may lack certain advanced features
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Q12. What are the types of regression models, name them and explain them
Ans. 
Types of regression models include linear regression, logistic regression, polynomial regression, ridge regression, and lasso regression.
• Linear regression: used to model the relationship between a dependent variable and one or more independent variables.

• Logistic regression: used for binary classification problems, where the output is a probability value between 0 and 1.

• Polynomial regression: fits a curve to the data by...read more
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Q13. Difference in Linear and Logistic Regression
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Linear regression is used for continuous variables, while logistic regression is used for binary outcomes.
• Linear regression predicts continuous outcomes, while logistic regression predicts binary outcomes.

• Linear regression uses a linear equation to model the relationship between the independent and dependent variables.

• Logistic regression uses the logistic function to model the probability of a binary outcome.

• Linear reg...read more
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Q14. Difference in Random Forest and Decision Tree
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Random Forest is an ensemble method using multiple decision trees, while Decision Tree is a single tree-based model.
• Random Forest is a collection of decision trees that are trained on random subsets of the data.

• Decision Tree is a single tree structure that makes decisions by splitting the data based on features.

• Random Forest reduces overfitting by averaging the predictions of multiple trees.

• Decision Tree can be prone t...read more
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Q15. What is ETL and what are the types or examples of ETL tools
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ETL stands for Extract, Transform, Load. It is a process of extracting data from various sources, transforming it into a usable format, and loading it into a target database.
• ETL tools include Informatica PowerCenter, Talend, Apache Nifi, Microsoft SQL Server Integration Services (SSIS), and IBM InfoSphere DataStage.

• Extract: Data is extracted from various sources such as databases, files, APIs, etc.

• Transform: Data is cl...read more
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Q1. What is random forest
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Random forest is an ensemble learning method used for classification and regression tasks.
• Random forest is a collection of decision trees that are trained on random subsets of the data.

• Each tree in the random forest independently predicts the target variable, and the final prediction is made by averaging the predictions of all trees.

• Random forest is robust to overfitting and noisy data, and it can handle large datasets...read more
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Q2. Bias variance trade off
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Q1. Tell me about your self?
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Q2. What is maths and stats
Ans. 
Maths and stats refer to the study of mathematical concepts and statistical methods for analyzing data.
• Maths involves the study of numbers, quantities, shapes, and patterns.

• Stats involves collecting, analyzing, interpreting, and presenting data.

• Maths is used to solve equations, calculate probabilities, and model real-world phenomena.

• Stats is used to make informed decisions, draw conclusions, and test hypotheses.

• Both ma...read more
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Confusion matrix what are your job rolls explain me Gradient boosting algorithm?

Asked the question about ml and basic python questions

Q1. 1. Basics of Python 2. Basics of ML like supervised and unsupervised learning
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Basic DP, Array Questions

Q1. Resume Walkthrough and Discussion, Medium level coding questions
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Q1. Discussion with Manager
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Q1. Normal HR round
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Easy array questions.

Q1. Explain projects from resume.
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Used Python and scikit-learn to preprocess data and build the model

• Performed feature engineering to improve model performance

• Evaluated model using metrics like accuracy, precision, and recall
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Q2. Questions on image processing and computer vision.
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