Data Analyst Interview Resources

Most Important Mathematical Equations in Data Science! 1️⃣ Gradient Descent: Optimization algorithm minimizing the cost function. 2️⃣ Normal Distribution: Distribution characterized by mean μ\muμ and variance σ2\sigma^2σ2. 3️⃣ Sigmoid Function: Activation function mapping real values to 0-1 range. 4️⃣ Linear Regression: Predictive model of linear input-output relationships. 5️⃣ Cosine Similarity: Metric for vector similarity based on angle cosine. 6️⃣ Naive Bayes: Classifier using Bayes’ Theorem and feature independence. 7️⃣ K-Means: Clustering minimizing distances to cluster centroids. 8️⃣ Log Loss: Performance measure for probability output models. 9️⃣ Mean Squared Error (MSE): Average of squared prediction errors. 🔟 MSE (Bias-Variance Decomposition): Explains MSE through bias and variance. 1️⃣1️⃣ MSE + L2 Regularization: Adds penalty to prevent overfitting. 1️⃣2️⃣ Entropy: Uncertainty measure used in decision trees. 1️⃣3️⃣ Softmax: Converts logits to probabilities for classification. 1️⃣4️⃣ Ordinary Least Squares (OLS): Estimates regression parameters by minimizing residuals. 1️⃣5️⃣ Correlation: Measures linear relationships between variables. 1️⃣6️⃣ Z-score: Standardizes value based on standard deviations from mean. 1️⃣7️⃣ Maximum Likelihood Estimation (MLE): Estimates parameters maximizing data likelihood. 1️⃣8️⃣ Eigenvectors and Eigenvalues: Characterize linear transformations in matrices. 1️⃣9️⃣ R-squared (R²): Proportion of variance explained by regression. 2️⃣0️⃣ F1 Score: Harmonic mean of precision and recall. 2️⃣1️⃣ Expected Value: Weighted average of all possible values. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

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Myntra interview questions for Data Analyst 2024. 1. You have a dataset with missing values. How would you use a combination of Pandas and NumPy to fill missing values based on the mean of the column? 2. How would you create a new column in a Pandas DataFrame by normalizing an existing numeric column using NumPy’s np.min() and np.max()? 3. Explain how to group a Pandas DataFrame by one column and apply a NumPy function, like np.std() (standard deviation), to each group. 4. How can you convert a time-series column in a Pandas DataFrame to NumPy’s datetime format for faster time-based calculations? 5. How would you identify and remove outliers from a Pandas DataFrame using NumPy’s Z-score method (scipy.stats.zscore)? 6. How would you use NumPy’s percentile() function to calculate specific quantiles for a numeric column in a Pandas DataFrame? 7. How would you use NumPy's polyfit() function to perform linear regression on a dataset stored in a Pandas DataFrame? 8. How can you use a combination of Pandas and NumPy to transform categorical data into dummy variables (one-hot encoding)? 9. How would you use both Pandas and NumPy to split a dataset into training and testing sets based on a random seed? 10. How can you apply NumPy's vectorize() function on a Pandas Series for better performance? 11. How would you optimize a Pandas DataFrame containing millions of rows by converting columns to NumPy arrays? Explain the benefits in terms of memory and speed. 12. How can you perform complex mathematical operations, such as matrix multiplication, using NumPy on a subset of a Pandas DataFrame? 13. Explain how you can use np.select() to perform conditional column operations in a Pandas DataFrame. 14. How can you handle time series data in Pandas and use NumPy to perform statistical analysis like rolling variance or covariance? 15. How can you integrate NumPy's random module (np.random) to generate random numbers and add them as a new column in a Pandas DataFrame? 16. Explain how you would use Pandas' applymap() function combined with NumPy’s vectorized operations to transform all elements in a DataFrame. 17. How can you apply mathematical transformations (e.g., square root, logarithm) from NumPy to specific columns in a Pandas DataFrame? 18. How would you efficiently perform element-wise operations between a Pandas DataFrame and a NumPy array of different dimensions? 19. How can you use NumPy functions like np.linalg.inv() or np.linalg.det() for linear algebra operations on numeric columns of a Pandas DataFrame? 20. Explain how you would compute the covariance matrix between multiple numeric columns of a DataFrame using NumPy. 21. What are the key differences between a Pandas DataFrame and a NumPy array? When would you use one over the other? 22. How can you convert a NumPy array into a Pandas DataFrame, and vice versa? Provide an example. You can find the answers here 👇👇 https://medium.com/@data_analyst/myntra-data-analyst-interview-questions-with-answers-97ed86953204 I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

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Python Command Cheatsheet

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Data Analyst Interview Topics

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Data Analyst INTERVIEW QUESTIONS AND ANSWERS 👇👇 1.Can you name the wildcards in Excel? Ans: There are 3 wildcards in Excel that can ve used in formulas. Asterisk (*) – 0 or more characters. For example, Ex* could mean Excel, Extra, Expertise, etc. Question mark (?) – Represents any 1 character. For example, R?ain may mean Rain or Ruin. Tilde (~) – Used to identify a wildcard character (~, *, ?). For example, If you need to find the exact phrase India* in a list. If you use India* as the search string, you may get any word with India at the beginning followed by different characters (such as Indian, Indiana). If you have to look for India” exclusively, use ~. Hence, the search string will be india~*. ~ is used to ensure that the spreadsheet reads the following character as is, and not as a wildcard. 2.What is cascading filter in tableau? Ans: Cascading filters can also be understood as giving preference to a particular filter and then applying other filters on previously filtered data source. Right-click on the filter you want to use as a main filter and make sure it is set as all values in dashboard then select the subsequent filter and select only relevant values to cascade the filters. This will improve the performance of the dashboard as you have decreased the time wasted in running all the filters over complete data source. 3.What is the difference between .twb and .twbx extension? Ans: A .twb file contains information on all the sheets, dashboards and stories, but it won’t contain any information regarding data source. Whereas .twbx file contains all the sheets, dashboards, stories and also compressed data sources. For saving a .twbx extract needs to be performed on the data source. If we forward .twb file to someone else than they will be able to see the worksheets and dashboards but won’t be able to look into the dataset. 4.What are the various Power BI versions? Power BI Premium capacity-based license, for example, allows users with a free license to act on content in workspaces with Premium capacity. A user with a free license can only use the Power BI service to connect to data and produce reports and dashboards in My Workspace outside of Premium capacity. They are unable to exchange material or publish it in other workspaces. To process material, a Power BI license with a free or Pro per-user license only uses a shared and restricted capacity. Users with a Power BI Pro license can only work with other Power BI Pro users if the material is stored in that shared capacity. They may consume user-generated information, post material to app workspaces, share dashboards, and subscribe to dashboards and reports. Pro users can share material with users who don’t have a Power BI Pro subscription while workspaces are at Premium capacity. ENJOY LEARNING 👍👍

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Data structure Cheatsheet

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Data Analyst Starter Kit

Some tips to Sharpen Your analytical Thinking: 🤔💭 1. Use the 80/20 Rule: Identify the 20% of activities that lead to 80% of your results. 2. Master learning with the Feynman Technique: Teach others, identify gaps, & simplify. 3. "You must not fool yourself; you are the easiest person to fool." -Richard Feynman