Data Analyst Interview Resources

Data Analyst interview questions 1) What joins are mostly used in SQL? 2) Use cases of Cross and Self Joins? 3) Write a query to exclude weekends from a table? 4) What are Window Functions? 5) What is the difference between CTEs and Subqueries? 6) How can you optimize SQL queries? 7) How can you convert data from rows into columns? 8) If there are 10 different KPIs calculated from different tables on a daily basis, how would you compile them into a single report? I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)

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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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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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Different Types of Data Analyst Interview Questions 👇👇 Technical Skills: These questions assess your proficiency with data analysis tools, programming languages (e.g., SQL, Python, R), and statistical methods. Case Studies: You might be presented with real-world scenarios and asked how you would approach and solve them using data analysis. Behavioral Questions: These questions aim to understand your problem-solving abilities, teamwork, communication skills, and how you handle challenges. Statistical Questions: Expect questions related to descriptive and inferential statistics, hypothesis testing, regression analysis, and other quantitative techniques. Domain Knowledge: Some interviews might delve into your understanding of the specific industry or domain the company operates in. Machine Learning Concepts: Depending on the role, you might be asked about your understanding of machine learning algorithms and their applications. Coding Challenges: These can assess your programming skills and your ability to translate algorithms into code. Communication: You might need to explain technical concepts to non-technical stakeholders or present your findings effectively. Problem-Solving: Expect questions that test your ability to approach complex problems logically and analytically. Remember, the exact questions can vary widely based on the company and the role you're applying for. It's a good idea to review the job description and the company's background to tailor your preparation.