20+ Data Science Analyst Interview Questions and Answers

To analyze the problem of poor performance of certain pizza outlets after the pandemic, start by identifying potential factors and gathering data.

• Identify potential factors such as changes in consumer behavior, supply chain disruptions, or local regulations

• Gather data on sales, customer feedback, employee turnover, and operational costs

• Analyze the data to identify patterns and correlations

• Develop hypotheses and test them through further analysis or experiments

• Recommend soluti...read more

Evaluation metrics for classification and regression models are different. Bias and variance are important factors to consider.

• Classification metrics include accuracy, precision, recall, F1 score, ROC curve, and AUC.

• Regression metrics include mean squared error, mean absolute error, R-squared, and adjusted R-squared.

• Bias refers to the difference between the predicted values and the actual values, while variance refers to the variability of the model's predictions.

• High bias in...read more

Yes, logistic regression can be used for multi class classification by using techniques like one-vs-rest or softmax.

• Logistic regression is typically used for binary classification, but it can be extended to handle multiple classes.

• One common approach is to use one-vs-rest (OvR) strategy, where a separate binary logistic regression model is trained for each class.

• Another approach is to use softmax regression, which is a generalization of logistic regression to multiple classes...read more

Decision Trees are a type of supervised learning algorithm used for classification and regression tasks.

• Decision Trees are used to create a model that predicts the value of a target variable based on several input variables.

• The algorithm splits the data into subsets based on the most significant attribute and continues recursively until a leaf node is reached.

• Some of the algorithms used in my project include Random Forest, Gradient Boosting, and XGBoost.

• Random Forest is an en...read more

List is mutable, Tuple is immutable in Python.

• List can be modified after creation, Tuple cannot be modified.

• List is defined using square brackets [], Tuple is defined using parentheses ().

• List is used for collections of items that may change, Tuple is used for fixed collections.

• Example: list_example = [1, 2, 3], tuple_example = (4, 5, 6)

Normalization and standardization are techniques used to transform data into a common scale.

• Normalization scales the data between 0 and 1, making it easier to compare different features.

• Standardization transforms the data to have a mean of 0 and standard deviation of 1, making it easier to compare different samples.

• Normalization is useful when the scale of the features varies widely, while standardization is useful when the data has outliers or follows a normal distribution.

• E...read more

Merge and concat in Python are used to combine data frames, while union and joins in SQL are used to combine tables.

• Merge in Python combines data frames based on a common column or index.

• Concat in Python combines data frames along a particular axis.

• Union in SQL combines the results of two or more SELECT statements.

• Joins in SQL combine rows from two or more tables based on a related column between them.

Variance and standard deviation are measures of spread or dispersion of a dataset.

• Variance is the average of the squared differences from the mean.

• Standard deviation is the square root of variance.

• They are used to understand the distribution of data and to compare different datasets.

• Higher variance or standard deviation indicates more spread or variability in the data.

• Lower variance or standard deviation indicates less spread or variability in the data.

Precision and recall are two metrics used to evaluate the performance of a classification model.

• Precision measures the proportion of true positives among all positive predictions.

• Recall measures the proportion of true positives among all actual positives.

• Both metrics are important in different scenarios, depending on the cost of false positives and false negatives.

• For example, in a medical diagnosis scenario, recall may be more important to avoid missing a potentially life-th...read more

Decorators in Python are functions that modify the behavior of other functions.

• Decorators are denoted by the @ symbol followed by the decorator name.

• They are commonly used for logging, timing, authentication, etc.

• Decorators can be used to add functionality to existing functions without modifying their code.

To estimate the number of ACs sold in Bangalore in a year, we can consider factors like population, income levels, climate, and market trends.

• Consider the population of Bangalore and the percentage of households that can afford ACs.

• Analyze income levels in Bangalore to determine the purchasing power of residents.

• Take into account the climate of Bangalore, as hotter regions may have higher demand for ACs.

• Look at market trends and sales data from previous years to make a more a...read more

A lambda function is a small anonymous function defined without a name.

• Lambda functions are used for creating small, one-time use functions.

• They can take any number of arguments, but can only have one expression.

• Lambda functions are often used in conjunction with higher-order functions like map, filter, and reduce.

• Example: lambda x: x*2 defines a lambda function that doubles the input x.

Data science interests me due to its ability to extract valuable insights from data and make informed decisions.

• I am fascinated by the power of data to drive business strategies and improve decision-making processes.

• I enjoy the challenge of analyzing complex datasets and finding patterns that can lead to actionable outcomes.

• Data science allows me to combine my analytical skills with my passion for problem-solving and innovation.

• I am excited about the potential of data science...read more

Built-in data refers to pre-existing datasets or information that is already included in a software or system.

• Built-in data is typically provided by the software or system for analysis or processing.

• Examples include sample datasets in statistical software like R or Python libraries like scikit-learn.

• Built-in data can also refer to default datasets in databases or data warehouses.

• It can save time and effort by providing ready-to-use data for analysis or testing.

Proficient in Python with experience in data analysis and visualization.

• Experience in using Python libraries such as Pandas, NumPy, and Matplotlib.

• Ability to write efficient and optimized code for data manipulation and analysis.

• Familiarity with machine learning algorithms and their implementation in Python.

• Experience in web scraping and data extraction using Python.

• Proficient in using Jupyter Notebook for data analysis and visualization.

Proficient in Python, SQL, and PowerBI for data analysis and visualization.

• Extensive experience using Python for data manipulation and analysis

• Strong SQL skills for querying databases and extracting relevant information

• Proficient in creating interactive dashboards and reports using PowerBI

• Ability to integrate Python scripts with PowerBI for advanced analytics

• Experience in data visualization techniques to communicate insights effectively

Outliers are data points that significantly differ from the rest of the data in a dataset.

• Outliers can skew statistical analyses and machine learning models.

• Outliers can be caused by errors in data collection or measurement, or they may represent true anomalies in the data.

• Examples of outliers include unusually high or low values in a dataset.

Code to find anagrams in an array of strings

• Iterate through the array of strings

• Sort each string alphabetically

• Check if the sorted strings are equal to identify anagrams