200+ Senior Data Analyst Interview Questions and Answers

Least Squares Method and Maximum Likelihood are both used to estimate parameters, but differ in their approach.

• Least Squares Method minimizes the sum of squared errors between the observed and predicted values.

• Maximum Likelihood estimates the parameters that maximize the likelihood of observing the given data.

• Least Squares Method assumes that the errors are normally distributed and independent.

• Maximum Likelihood does not make any assumptions about the distribution of errors.

• L...read more

To analyze a complex dataset, start by understanding the data, cleaning and structuring it, performing exploratory data analysis, applying statistical methods, and creating visualizations for insights.

• Understand the business objectives and goals to align the analysis with company's growth strategy.

• Clean and structure the dataset by identifying and handling missing values, outliers, and inconsistencies.

• Perform exploratory data analysis to understand the distribution, relations...read more

To improve the performance of Linear Regression, you can consider feature engineering, regularization, and handling outliers.

• Perform feature engineering to create new features that capture important information.

• Apply regularization techniques like L1 or L2 regularization to prevent overfitting.

• Handle outliers by either removing them or using robust regression techniques.

• Check for multicollinearity among the independent variables and consider removing highly correlated variabl...read more

Calculate the number of matches won and lost by each team based on the given data in the matches table.

• Group the data by team and count the number of matches won and lost for each team.

• Use the winner column to determine the outcome of each match.

• Create a query to calculate the number of matches won and lost for each team.

• Example: Team A won 2 matches and lost 1 match.

• Example: Team B won 1 match and lost 2 matches.

Overfitting in decision trees can be handled by pruning, reducing tree depth, increasing dataset size, and using ensemble methods.

• Prune the tree to remove unnecessary branches

• Reduce tree depth to prevent overfitting

• Increase dataset size to improve model generalization

• Use ensemble methods like Random Forest to reduce overfitting

• Underfitting can be handled by increasing tree depth, adding more features, and reducing regularization

• Regularization can be used to prevent overfittin...read more

Metrics used to evaluate classification models

• Accuracy

• Precision

• Recall

• F1 Score

• ROC Curve

• Confusion Matrix

Metrics used to evaluate Linear Regression

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

• R-squared (R²)

• Adjusted R-squared (Adj R²)

• Mean Absolute Error (MAE)

• Residual Sum of Squares (RSS)

• Akaike Information Criterion (AIC)

• Bayesian Information Criterion (BIC)

Encountered a complex data analysis problem and successfully navigated through it

• Encountered a data set with missing values and outliers

• Utilized data cleaning techniques such as imputation and outlier detection

• Applied statistical analysis and machine learning algorithms to identify patterns and trends

• Visualized the data using tools like Tableau for better understanding

• Collaborated with domain experts to gain insights and validate findings

Overfitting in Linear Regression can be handled by using regularization techniques.

• Regularization techniques like Ridge regression and Lasso regression can help in reducing overfitting.

• Cross-validation can be used to find the optimal regularization parameter.

• Feature selection and dimensionality reduction techniques can also help in reducing overfitting.

• Collecting more data can help in reducing overfitting by providing a more representative sample.

Assumptions in Linear Regression

• Linear relationship between independent and dependent variables

• Homoscedasticity (constant variance) of residuals

• Independence of residuals

• Normal distribution of residuals

• No multicollinearity among independent variables

Views are virtual tables that display data from one or more tables, while temp tables are temporary tables that store data temporarily.

• Views are virtual tables created by a query, while temp tables are physical tables created in the database.

• Views do not store data themselves, but display data from underlying tables, while temp tables store data temporarily for a session or transaction.

• Views can be used for security purposes by restricting access to certain columns or rows, w...read more

Calculate the rolling sum of sales using SQL window functions for cumulative totals over a specified period.

• Use the SUM() function with the OVER() clause to calculate rolling sums.

• Example: SELECT date, sales, SUM(sales) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS rolling_sum FROM sales_data;

• You can adjust the window frame to specify different periods, e.g., LAST 7 DAYS.

• Ensure your data is ordered correctly to get accurate rolling sums.

Use SQL string functions like SUBSTRING and CHARINDEX to separate name from emails.

• Use CHARINDEX to find the position of the '@' symbol in the email address.

• Use SUBSTRING to extract the characters before the '@' symbol as the name.

• Consider handling cases where there are multiple names or special characters in the email address.

Panel data is a type of longitudinal data that involves observations on multiple subjects over multiple time periods.

• Panel data is also known as longitudinal data or cross-sectional time series data.

• It allows for the analysis of both individual and time effects.

• Examples include tracking the same group of individuals over time to study changes in their behavior or characteristics.

• Panel data is commonly used in economics, sociology, and political science research.

Scan data refers to the information collected from scanning barcodes or QR codes, typically used in retail to track sales and inventory.

• Scan data is collected by scanning barcodes or QR codes on products.

• It is commonly used in retail to track sales, inventory levels, and pricing.

• Scan data can provide valuable insights into consumer behavior and preferences.

• Examples of scan data systems include point-of-sale (POS) systems and inventory management software.

To share a Power BI report with an external user, we can use the Publish to Web feature or share it via email.

• Use the Publish to Web feature to generate an embed code that can be shared with external users

• Ensure that the report contains only non-sensitive data before using the Publish to Web feature

• Alternatively, share the report via email by granting access to the external user's email address

• The external user must have a Power BI account to view the report

I have dealt with terabytes of data in my previous role as a Data Analyst.

• Managed and analyzed terabytes of data from various sources

• Utilized big data tools such as Hadoop and Spark to process large datasets

• Performed complex data analysis and visualization on massive datasets

Types of bar charts in Tableau include standard bar, stacked bar, and side-by-side bar.

• Standard bar chart displays individual bars for each category

• Stacked bar chart shows the total value broken down into sub-categories

• Side-by-side bar chart compares multiple measures across categories

• Example: Stacked bar chart can be used to show sales by region, with each region broken down by product category

Utilize data visualization, statistical analysis, and machine learning techniques to identify patterns and correlations in large datasets for strategic decision making.

• Perform exploratory data analysis to understand the structure and relationships within the dataset

• Utilize data visualization techniques such as scatter plots, histograms, and heatmaps to identify patterns and correlations

• Conduct statistical analysis including correlation analysis, regression analysis, and hypot...read more

There are four types of connections available in Power BI.

• Power BI Desktop Connection

• Power BI Service Connection

• Power BI Mobile Connection

• Power BI Gateway Connection

Nielsen is a global measurement and data analytics company that provides insights into consumer behavior.

• Nielsen is known for its TV ratings system, which measures viewership for television programs.

• They also provide data on consumer purchasing habits and trends for various industries.

• Nielsen operates in over 100 countries and has a wide range of services including market research, audience measurement, and advertising effectiveness.

• The company was founded in 1923 by Arthur C...read more

ESG factors can have a significant impact on an investor's decision making, influencing both financial performance and sustainability.

• ESG factors can help investors assess the long-term sustainability and risk profile of a company.

• Investors increasingly consider ESG factors as a way to mitigate risks and identify opportunities for long-term value creation.

• ESG integration can lead to better financial performance and resilience in the face of environmental, social, and governan...read more

Calculated columns are row-wise computed and stored, while measures are dynamic calculations based on filter context at query time.

• Calculated Columns: Created during data loading, computed for each row, and stored in the data model. Example: A column calculating 'Total Sales' as 'Quantity * Price'.

• Measures: Dynamic calculations performed at query time, based on the current filter context. Example: A measure calculating 'Total Revenue' using SUM(Sales[Revenue]).

• Storage: Calcul...read more

Type I error is rejecting a true null hypothesis, while Type II error is failing to reject a false null hypothesis.

• Type I error is also known as a false positive

• Type II error is also known as a false negative

• Type I error occurs when the significance level is set too high

• Type II error occurs when the significance level is set too low

• Examples: Type I error - Convicting an innocent person, Type II error - Failing to convict a guilty person

• Type I error is more serious in medical ...read more

Cost function measures the difference between predicted and actual values. Error function measures the average of cost function.

• Cost function is used to evaluate the performance of a machine learning model.

• It measures the difference between predicted and actual values.

• Error function is the average of cost function over the entire dataset.

• It is used to optimize the parameters of the model.

• Examples of cost functions are mean squared error, mean absolute error, and cross-entropy...read more

There are three types of filters available in Power BI.

• Visual level filters

• Page level filters

• Report level filters

Tokens are individual units of text processed in NLP; unknown tokens can lead to challenges in model performance.

• Tokens are the smallest units of text, such as words or subwords, used in Natural Language Processing (NLP).

• For example, the sentence 'I love data analysis' can be tokenized into ['I', 'love', 'data', 'analysis'].

• If a token is not present in the model's vocabulary, it is often replaced with a special token like '<UNK>' (unknown).

• This can lead to loss of information...read more