Are you ready to dive into the world of data science and analytics? Nielsen Company, a global leader in market research and consumer insights, presents a challenging yet rewarding opportunity for data enthusiasts. To help you prepare for the journey ahead, let’s explore some common interview questions and insightful answers you might encounter during the process.
Table of Contents
Technical Interview Questions
Question: Explain Bayesian statistics.
Answer: Bayesian statistics is a method for updating our beliefs about the likelihood of events as we gather new evidence. It involves using prior knowledge, represented as probabilities, and updating them with new data to get a “posterior” probability. This approach helps in making predictions or decisions by incorporating both prior information and current data, resulting in more accurate estimates.
Question: What is the most fundamental data structure in pandas?
Answer: The most fundamental data structure in pandas is the DataFrame. It’s like a table where data is stored in rows and columns, allowing for easy manipulation, analysis, and cleaning of data. DataFrames are incredibly versatile and can handle various data types, making them a powerful tool for data analysis in Python.
Question: What type of objects can be used as keys in Dictionaries?
Answer: In Python, keys in dictionaries can be any immutable data type, such as strings, numbers, or tuples. These objects must be hashable, meaning they have a fixed hash value throughout their lifetime, allowing Python to retrieve the dictionary value efficiently. Mutable types like lists or other dictionaries cannot be used as keys due to their changeable nature, which would affect the hash value.
Question: Which function is used to get the list of column headers of a pandas DataFrame?
Answer: The function used to get the list of column headers of a pandas DataFrame is columns. This function returns a list containing the names of all the columns in the DataFrame, allowing easy access to the column names for further analysis or manipulation.
Question: Explain Linear Regression.
Answer: Linear regression is a statistical method used to understand the relationship between two continuous variables. It aims to find the best-fit straight line that describes the linear relationship between the independent variable (X) and the dependent variable (Y). The goal is to minimize the difference between the actual Y values and the predicted Y values by the linear equation (Y = mX + b), where ‘m’ is the slope of the line and ‘b’ is the intercept. This technique is often used for prediction and inference, providing insights into how changes in the independent variable affect the dependent variable.
Question: What is Precision?
Answer: Precision in the context of predictive modeling and statistics is the measure of how many of the items identified as positive by a model are positive. It is calculated by dividing the number of true positive results by the sum of true positive and false positive results. In simple terms, precision answers the question: “Out of all the items labeled as positive, how many are positive?” High precision indicates a low rate of false positive errors, making it crucial in scenarios where the cost of a false positive is high.
Question: What is the Bias–Variance tradeoff?
Answer: The bias-variance tradeoff is a fundamental concept in machine learning that deals with the balance between a model’s simplicity and its ability to capture the complexity of the underlying data.
- Bias: This refers to the error introduced by approximating a real-life problem, which may be complex, with a simpler model. A high-bias model makes strong assumptions about the form of the underlying data, which may cause it to consistently miss relevant patterns.
- Variance: This refers to the model’s sensitivity to changes in the training data. A high variance model is very flexible and can closely fit the training data, but it may fail to generalize well to new, unseen data.
Question: What factors should you use in market research?
Answer: In market research, consider target audience demographics, consumer behavior, market size, competition, and industry trends. Collect customer feedback through surveys and focus groups. Analyze market segmentation for targeted marketing. Evaluate SWOT factors and economic influences. Use this data to create effective strategies, products, and marketing campaigns aligned with consumer needs and market dynamics.
Question: What is a confusion matrix?
Answer: A confusion matrix is a tool used in machine learning to visualize the performance of a classification algorithm. It is a table that breaks down the predictions made by a model to show the actual vs. predicted classifications. The matrix compares the true target values with those predicted by the model, allowing for a detailed analysis of how well the model is performing in terms of accuracy, precision, recall, and specificity.
Question: Explain any ML algorithm.
Answer: The Random Forest algorithm works by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. It belongs to the ensemble learning family, where multiple models are used to solve the same problem and improve the overall performance.
Key points about Random Forest:
- Versatility: It can be used for both classification and regression tasks, and it works well on both linear and non-linear problems.
- Handling Overfitting: Aggregating the results of various decision trees, reduces the risk of overfitting, which is a common problem with single decision trees.
- Handling Missing Values: Random Forest can handle missing values. When there is a missing value in a variable, the algorithm will find the best surrogate variable to split among the other variables.
- Feature Importance: It gives insights into which features are important for making predictions, which can be valuable for feature selection.
Question: Explain Statistical significance.
Answer: Statistical significance is a measure used to determine if the results of an experiment or study are likely not due to chance. It helps researchers understand whether a relationship or difference observed in their data can be attributed to a specific intervention or factor, rather than random variability.
Question: What is the p-value?
Answer: The p-value is a statistical measure that helps scientists and researchers determine the significance of their results. It represents the probability of observing the collected data, or something more extreme if the null hypothesis of a study is true. The null hypothesis typically suggests that there is no effect or no difference between groups.
Question: What do you know about word embeddings?
Answer: Word embeddings are a type of word representation used in natural language processing (NLP) and machine learning tasks. They are dense, real-valued vector representations of words in a continuous vector space, where each word is mapped to a point in this space.
Question: Describe a random forest.
Answer: Random Forest is a powerful and versatile ensemble learning algorithm used for both classification and regression tasks in machine learning. It operates by constructing multiple decision trees during training and outputs the mode of the classes for classification or the mean prediction for regression from the individual trees.
Question: Describe xgboost.
Answer: XGBoost, short for “Extreme Gradient Boosting,” is an optimized and efficient implementation of the gradient boosting machine learning algorithm. It’s renowned for its speed, performance, and effectiveness in both regression and classification tasks.
Question: Describe k-means.
Answer: K-means clustering is an unsupervised machine learning algorithm used for partitioning a dataset into K distinct, non-overlapping clusters. The goal is to group similar data points and discover underlying patterns or structures within the data.
Python Interview Questions
Question: What is the difference between a list and a tuple in Python?
Answer:
- List: Lists are mutable, meaning their elements can be changed after creation. They are defined with square brackets [ ] and support operations like appending, removing, and modifying elements.
- Tuple: Tuples are immutable, meaning their elements cannot be changed after creation. They are defined with parentheses ( ) and are typically used for fixed collections of items.
Question: Explain the use of lambda functions in Python.
Answer: lambda functions are anonymous functions defined using the lambda keyword.
They are typically used for short, simple operations where a full function definition is unnecessary.
lambda functions can take any number of arguments but can only have one expression.
Question: What is the purpose of the __init__ method in Python classes?
Answer: The __init__ method is a special method used to initialize newly created objects.
It is called automatically when a new instance of the class is created.
It allows the class to initialize attributes and perform any setup that is necessary before the object is used.
Question: What is the difference between == and is in Python?
Answer: == is used to compare the values of two objects, checking if they are equal.
is used to check if two variables point to the same object in memory, i.e., if they are the same instance.
Question: How does exception handling work in Python?
Answer: Exceptions in Python are raised when an error occurs during execution.
try, except, finally, and else are used for exception handling.
try block contains the code where exceptions may occur, except block catches and handles exceptions, finally, block executes cleanup code whether an exception occurs or not, and else block executes code if the try block does not raise an exception.
Question: Explain the purpose of the __str__ method in Python classes.
Answer: The __str__ method is a special method used to return a string representation of an object.
It is called when the str() function is used on an object or when the object is printed.
By defining the __str__ method in a class, you can customize how the object is represented as a string.
SQL Interview Questions
Question: What is SQL?
Answer: SQL stands for Structured Query Language. It is a standard language used for managing and manipulating relational databases. SQL allows users to perform various operations such as retrieving, inserting, updating, and deleting data from databases.
Question: What is the difference between WHERE and HAVING clauses in SQL?
Answer: The WHERE clause is used to filter rows from a table based on a specified condition.
The HAVING clause is used to filter groups from the result of a GROUP BY clause based on a specified condition.
Question: Explain the difference between INNER JOIN and LEFT JOIN in SQL.
Answer: INNER JOIN returns rows when there is at least one match in both tables.
LEFT JOIN returns all rows from the left table (the first table mentioned in the query) and the matched rows from the right table. If there are no matches in the right table, NULL values are returned.
Question: What is a subquery in SQL?
Answer: A subquery, also known as a nested query or inner query, is a query within another SQL query. It is used to retrieve data from one or more tables based on a specified condition. The result of the subquery can be used as a condition or value in the outer query.
Question: Explain the difference between DELETE and TRUNCATE commands in SQL.
Answer: DELETE is a DML (Data Manipulation Language) command used to remove rows from a table based on a condition. It is slower because it generates an entry in the transaction log for each deleted row.
TRUNCATE is a DDL (Data Definition Language) command used to remove all rows from a table. It is faster than DELETE because it does not generate an entry in the transaction log for each deleted row. However, TRUNCATE cannot be rolled back.
Question: What is a primary key in SQL?
Answer: A primary key is a column or a set of columns that uniquely identifies each row in a table. It ensures that there are no duplicate rows in the table and provides a way to link data across tables through foreign keys.
Question: Explain the difference between UNION and UNION ALL in SQL.
Answer: UNION is used to combine the result sets of two or more SELECT statements into a single result set. It removes duplicate rows from the final result.
UNION ALL, on the other hand, also combines the result sets of two or more SELECT statements into a single result set but includes all rows, including duplicates, in the final result.
Technical Topics
- Linear optimization
- Statistics
- Convex optimization
- Mathematical question,
- Logical reasoning,
- English language
- Standard ML questions and
- Behavior type of questions
- R questions
General Behavioral Questions
Que: Do you know what we are doing at Nielsen?
Que: You have an “A” table and a “B” table. How can you write a query that returns the rows that “B” has but “A” does not have?
Que: How can you know whether your model is good or bad?
Que: What is the largest data set you have ever worked with?
Que: How would you tell a client that you made a mistake?
Que: How much do you know about ML algorithms?
Que: How would you explain statistical significance and p-value to someone who does not know statistics?
Que: Do you have experience with pandas, shell scripting, spark, etc
Que: What is your favorite statistical test?
Que: How do you handle an unbalanced dataset?
Que: What do you see yourself in 3 years?
Que: Can you derive the KKT condition?
Que: How would you get all the unique values from an attribute in R?
Conclusion
Prepare yourself for the journey into the fascinating realm of data science and analytics with these insightful interview questions and answers tailored for your Nielsen Company interview. Best of luck in your pursuit of unraveling insights and driving impactful decisions through the power of data!
