MADHU THANGELLA on LinkedIn: Practical interview question for an entry-level data analyst role in… (2025)

Practical interview question for an entry-level data analyst role in #Power_BI:Complex DAX Challenge: Describe how you would use DAX to calculate year-over-year growth in a dataset.👉 Date Table: Ensure you have a Date table with dates ranging from the earliest to the latest in your dataset. This table should include columns for Year, Month, and Date.👉 Total Sales: Calculate the total sales using SUM(Sales[Amount]) for the current period (e.g., year or month) in your main data table.👉 Previous Year Sales: Use CALCULATE([Total Sales], SAMEPERIODLASTYEAR(DateTable[Date])) to get sales from the same period in the previous year. Ensure your main data table is linked to the Date table by the date column.👉 YoY Growth Calculation: Define YoY Growth as DIVIDE([Total Sales] - [Previous Year Sales], [Previous Year Sales], 0) to compute the percentage change year-over-year.👉 Usage: Apply these measures in Power BI or Excel to visualize and track year-over-year growth trends for sales or any other metrics.👉 Example: For June 2023, if sales were $200 and in June 2022 they were $150, YoY growth would be 33%.👉 Insight: This analysis helps businesses track performance trends over time and make informed decisions based on annual growth rates.👉 Application: Modify formulas as needed to analyze different metrics such as profits or customer acquisitions, providing a comprehensive view of business growth.What DAX challenges did you face in interviews? Share your experiences in the comments!➡️𝐑𝐞𝐟𝐞𝐫𝐫𝐚𝐥𝐬/𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬:: : https://lnkd.in/gcw-ziZmMADHU THANGELLA connect with me for more updates !#PowerBI #DataAnalyst #DAXChallenge #YearOverYear #DataVisualization #BusinessIntelligence #datacommence

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Practical interview question for an entry-level data analyst role in #Power_BI:Question :-Describe how you would use DAX to calculate year-over-year growth in a dataset.Answer:-👉 Date Table: Ensure you have a Date table with dates ranging from the earliest to the latest in your dataset. This table should include columns for Year, Month, and Date.👉 Total Sales: Calculate the total sales using SUM(Sales[Amount]) for the current period (e.g., year or month) in your main data table.👉 Previous Year Sales: Use CALCULATE([Total Sales], SAMEPERIODLASTYEAR(DateTable[Date])) to get sales from the same period in the previous year. Ensure your main data table is linked to the Date table by the date column.👉 YoY Growth Calculation: Define YoY Growth as DIVIDE([Total Sales] - [Previous Year Sales], [Previous Year Sales], 0) to compute the percentage change year-over-year.👉 Usage: Apply these measures in Power BI or Excel to visualize and track year-over-year growth trends for sales or any other metrics.👉 Example: For June 2023, if sales were $200 and in June 2022 they were $150, YoY growth would be 33%.👉 Insight: This analysis helps businesses track performance trends over time and make informed decisions based on annual growth rates.👉 Application: Modify formulas as needed to analyze different metrics such as profits or customer acquisitions, providing a comprehensive view of business growth.Hope everyone understood 🙏!Feel free to share any interview questions you've come across in the comments. Let's help each other prepare !➡️𝐑𝐞𝐟𝐞𝐫𝐫𝐚𝐥𝐬/𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬:: : https://lnkd.in/gcw-ziZmMADHU THANGELLA connect with me for more updates !#dataanalyst #powerbi

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Practical interview question for an entry-level data analyst role in hashtag#Power_BI:Question :-Describe how you would use DAX to calculate year-over-year growth in a dataset.Answer:-👉 Date Table: Ensure you have a Date table with dates ranging from the earliest to the latest in your dataset. This table should include columns for Year, Month, and Date.👉 Total Sales: Calculate the total sales using SUM(Sales[Amount]) for the current period (e.g., year or month) in your main data table.👉 Previous Year Sales: Use CALCULATE([Total Sales], SAMEPERIODLASTYEAR(DateTable[Date])) to get sales from the same period in the previous year. Ensure your main data table is linked to the Date table by the date column.👉 YoY Growth Calculation: Define YoY Growth as DIVIDE([Total Sales] - [Previous Year Sales], [Previous Year Sales], 0) to compute the percentage change year-over-year.👉 Usage: Apply these measures in Power BI or Excel to visualize and track year-over-year growth trends for sales or any other metrics.👉 Example: For June 2023, if sales were $200 and in June 2022 they were $150, YoY growth would be 33%.👉 Insight: This analysis helps businesses track performance trends over time and make informed decisions based on annual growth rates.👉 Application: Modify formulas as needed to analyze different metrics such as profits or customer acquisitions, providing a comprehensive view of business growth.Hope everyone understood 🙏!Feel free to share any interview questions you've come across in the comments. Let's help each other prepare !#powerbi #dataanalyst

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One of the interview questions asked by one of my friends for the position of data analyst.There are 2 columns in a table, Product name and Product sales. You want % of sales for each product in Microsoft Power BiHow will you approach this question?First method:First, we want to calculate the total sales for each product.We will use sum function.Total_Sales = sum(Product_Sales[Sales])Then we will use calculate and all function:Calculate function is used to change the context.The first argument is the expression and the next arguments are the filter All function Returns all the rows in a table, or all the values in a column, ignoring any filters that might have been applied.This function is useful for clearing filters and creating calculations on all the rows in a table.Total_sales whole=All_Total_Sales = CALCULATE([Total_Sales],all(Product_Sales[Product]))This calculates the total sales ([Total_Sales]) without considering any filters applied to the 'Product' column in the 'Product_Sales' table.Thirdly, we will use Divide function to calculate the total percentage sales of each product.Sales % = divide([Total_Sales],[All_Total_Sales],0)Second method,If you want to use a single measure we can define that using variables to get the sales percentageUsing variables:Sales % by variables =VAR Total_Sales = [Total_Sales]VAR All_Sales = CALCULATE([Total_Sales], ALL('Product_Sales'[Product]))RETURN divide(Total_Sales,All_Sales,0)I will be covering the interview questions that will help you to make your understanding better.Want to start your data analysis journey Dm to know more.Follow Avneet Saluja Wadhawan for more updates!#sql #powerbi #openforwork #openforpowerbi#openforsql#hiring

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Practical interview question for an entry-level data analyst role in #Power_BI:Complex DAX Challenge: Describe how you would use DAX to calculate year-over-year growth in a dataset.👉 Date Table: Ensure you have a Date table with dates ranging from the earliest to the latest in your dataset. This table should include columns for Year, Month, and Date.👉 Total Sales: Calculate the total sales using SUM(Sales[Amount]) for the current period (e.g., year or month) in your main data table.👉 Previous Year Sales: Use CALCULATE([Total Sales], SAMEPERIODLASTYEAR(DateTable[Date])) to get sales from the same period in the previous year. Ensure your main data table is linked to the Date table by the date column.👉 YoY Growth Calculation: Define YoY Growth as DIVIDE([Total Sales] - [Previous Year Sales], [Previous Year Sales], 0) to compute the percentage change year-over-year.👉 Usage: Apply these measures in Power BI or Excel to visualize and track year-over-year growth trends for sales or any other metrics.👉 Example: For June 2023, if sales were $200 and in June 2022 they were $150, YoY growth would be 33%.👉 Insight: This analysis helps businesses track performance trends over time and make informed decisions based on annual growth rates.👉 Application: Modify formulas as needed to analyze different metrics such as profits or customer acquisitions, providing a comprehensive view of business growth.What DAX challenges did you face in interviews? Share your experiences in the comments!➡️𝐑𝐞𝐟𝐞𝐫𝐫𝐚𝐥𝐬/𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬:: : https://lnkd.in/gcw-ziZmMADHU THANGELLA connect with me for more updates !

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Are you preparing for a Data Analyst interview?Data Analyst Series #Day17In this PDF, you will find a list of 4 commonly asked Data Analyst interview questions in Power Bi with comprehensive answers to help you succeed.1.Your report is taking a long time to load. How do you optimize its performance?2.You have a dataset with inconsistent data. How do you clean and transform it in Power BI?3.You need to combine data from multiple sources. What options does Power BI offer for data integration?4.A stakeholder wants a geographical representation of sales. How would you create a map visualization in Power BI?I hope these questions and answers help you prepare for your Data Analyst interview. Best of luck!Happy Learning! 😊I spill secrets of Data Analysis dailyP.S. Repost this info with your network ♻️Do Follow Prem Mandal for more contents.#Data #Analyst #InterviewPrep #DataAnalysis #Analytics #CareerGrowth #DataAnalystInterviewTips #PowerBi

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As a data analyst our daily tasks --80% of the time on impactful tasks:Data Cleaning (40%): Remove duplicates, handle missing values, correct data types, standardize data.Finding Insights (20%): Perform EDA, descriptive statistics, and hypothesis testing.Creating Relationships (10%): Join datasets, establish relationships, and model data.Solving Business Problems (10%): Define problems, apply analytical methods, and develop solutions.20% of time on aesthetics:Dashboard Design (20%): Use consistent design principles, ensure user-friendly interfaces, add interactivity, and enhance visual appeal.#dataanalyst #powerbi

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Practical interview questions for an entry-level data analyst role in #Power_BI: along with answers ! Question): If you're working with a very large dataset that is slowing down Power BI, what strategies would you use to improve performance?👉 Use Import Mode: Opt for Import mode instead of DirectQuery for better performance, as Import mode caches data in memory.👉 Reduce Data Volume: Load only the necessary rows and columns, and ensure the dataset is free of duplicate records.👉 Aggregate Data: Summarize and group data before importing it to reduce dataset size and make calculations faster.👉 Utilize Indexes: Ensure your source databases have appropriate indexing for faster data retrieval.👉 Implement Incremental Refresh: Set up incremental refresh to update only new or modified data instead of reloading the entire dataset.👉 Optimize Data Model: Simplify relationships and use a star schema for more efficient queries.👉 Leverage Data Compression: Use Power BI’s built-in data compression features to reduce dataset size and improve speed.👉 Enable Query Folding: Perform data transformations at the source level by leveraging query folding to push transformations back to the source.👉 Partition Data: Divide large tables into smaller, manageable partitions for better performance.Hope everyone understood 🙏!Feel free to share any interview questions you've come across in the comments. Let's help each other prepare !👉𝐑𝐞𝐟𝐞𝐫𝐫𝐚𝐥𝐬/𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬:: : https://lnkd.in/gcw-ziZmMADHU THANGELLA connect with me for more updates !I'll post real interview Q&A daily. If you like my content, connect with me, and check out another Q&A in the comments👇!----------------------------------------------------------------------Maximize your data's potential with expert support in SQL, Power BI, and more. We offer personalized project help, skill training, and consulting. For inquiries, email madhuthangella123@gmail.com or message us on LinkedIn.#dataanalyst #powerbi

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Hello 👋, Are you struggling to become a professional Data Analyst.....?*Boost Your Data Analysis Skills!*As a data analyst, mastering key formulas is crucial for success. Here are the top formulas to elevate your game:*Basic Functions*- SUMIF/SUMIFS (conditional sum)- COUNTIF/COUNTIFS (conditional count)- AVERAGEIF/AVERAGEIFS (conditional average)- MAX/MIN (maximum/minimum values)- IF/IFS (logical tests)- VLOOKUP (vertical lookup)- INDEX/MATCH (flexible lookup)- PivotTables (data summarization)*Data Manipulation*- FILTER (filter data)- SORT/SORTBY (sort data)- UNIQUE (remove duplicates)- TRANSPOSE (rotate data)- GROUPBY (group data)*Statistical Functions*- AVERAGE (mean)- MEDIAN (middle value)- MODE (most frequent value)- STDEV/STDEVP (standard deviation)- VAR/VARP (variance)- CORREL (correlation coefficient)- REGRESS (linear regression)*Date and Time Functions*- TODAY (current date)- NOW (current date and time)- DATEDIF (date difference)- EOMONTH (end of month)- WORKDAY (workday calculation)*Text Functions*- LEN (text length)- LOWER/UPPER (text case)- TRIM (remove spaces)- SUBSTITUTE (replace text)- CONCATENATE (combine text)*Logical Functions*- IF/IFS (logical tests)- AND/OR/NOT (logical operators)- IFERROR/IFBLANK (error handling)- SWITCH (switch values)*Advanced Functions*- XLOOKUP (flexible lookup)- INDEX/MATCH (flexible lookup)Share your favorite formulas or tips in the comments!#DataAnalysis #Solution Provider #Story teller #Analytics #Productivity

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Last weekend, I took part in an engaging data analytics resume challenge hosted by Dhaval Patel and Hemanand Vadivel from Codebasics. This project, focused on the human resources domain, was a refreshing change from my usual areas of expertise. The insights I gained were not only intriguing but also imparted valuable lessons, especially in the use of Power BI. Here are some key takeaways I'd like to share:(A). General Insights for Data Analysts:1). As a data analyst, it's crucial to listen more than you speak, particularly when determining what your employer or stakeholder needs from the dataset. Understanding their requirements thoroughly is essential.2). While listening, ask the right questions. Doing so positions you as a strategic partner rather than just a solution provider. Moreover, asking insightful questions helps you understand the stakeholders' long-term and short-term plans, allowing you to align your data analytics solutions accordingly.3). Always aim to provide comprehensive insight reports that encourage stakeholders to ask more questions. Your report should act as a minimum viable product (MVP) for effective decision-making.(B). Power BI Specifics:1). When excluding or transforming data using tools like Power BI or Tableau, think of dynamic solutions that will adapt even when new data is added. This practice distinguishes exceptional data professionals. Learn to work with parameters, filters, and calculated fields to achieve this flexibility.2). When working with DAX, keep your measures in a separate table. This approach helps maintain an organized data model, making it easier to manage and locate your measures. It enhances readability, centralizes all measures, reduces the risk of errors, and boosts productivity.3). Mastering the CALCULATE function in DAX is crucial when working with Power BI. It’s fundamental for creating complex calculations and filters.There are many more lessons, but I'll stop here. I hope you found these insights valuable, whether they're new to you or a helpful refresher.I'd love to hear your thoughts in the comments section!#selftaught_data_lab #PowerBI #selftaught_data_analyst

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