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𝐏𝐨𝐬𝐭 𝐍𝐨.𝟖𝟔 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐀𝐫𝐭 𝐨𝐟 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 𝐢𝐧 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 Are you looking to elevate your data analysis game? 🚀 Dive into the world of interactive dashboards with Power BI! Here's a step-by-step guide to help you create compelling and effective dashboards: 1️⃣ Define your objectives: Clearly outline your goals and the key metrics you want to highlight. 2️⃣ Collect and prepare your data: Ensure your data is clean, organized, and ready for analysis. 3️⃣ Create visuals: Use Power BI's visualization options to create impactful charts and graphs. 4️⃣ Design the layout: Arrange your visuals in a logical and intuitive manner. 5️⃣ Add interactivity: Use slicers, filters, and drill-throughs to allow users to explore the data. 6️⃣ Enhance with calculations: Use DAX to create custom calculations and measures. 7️⃣ Apply themes and formatting: Use Power BI's formatting options to improve readability and aesthetics. 8️⃣ Test and iterate: Get feedback from users and make improvements based on their input. 9️⃣ Publish and share: Once your dashboard is ready, publish it to the Power BI service and share it with your audience. My Recent Power BI projects 1. IPL Dashboard: https://lnkd.in/gMpSj36a 2. Election Dashboard: https://lnkd.in/gCFPYMK9 Mastering interactive dashboards in Power BI can revolutionize your data analysis game, providing you with valuable insights and helping you make informed decisions. Start creating your own interactive dashboards today! #PowerBI #DataAnalysis #InteractiveDashboards #DataVisualization

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I was always fascinated with cheatsheet. Especially those formula cheatsheet, they helped me in quick access to perform my portfolio management calculations. How to manage your portfolio like a professional? Here's a compilation of the formulas in a 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐂𝐡𝐞𝐚𝐭𝐬𝐡𝐞𝐞𝐭. Download the high-resolution PDF here: https://lnkd.in/dgb9CnE4 5 learnings you learn: ➡️Risk Management: Use tools like asset allocation and rebalancing to manage potential losses. Understanding risk tolerance and when to cut loss is crucial. ➡️Cost Awareness: unforeseen costs can erode returns over time. Select for low-cost funds and tax-efficient strategies whenever possible. ➡️Diversification: To spreads investments across different types of assets. These include stocks, bonds, and real estate. It balances out the risk and boost returns. ➡️Review: Your portfolio often. Adjust in response to changing markets or your goals. ➡️Lastly, focus on the long term. Don't react to short-term market moves. You can download the high-resolution PDF here: https://lnkd.in/dgb9CnE4 Mike Sim

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Releasing another huge new guide today on using R & Power BI together. There are many data analysis techniques you can take advantage of when combining both of these. Including: - Custom visualizations using ggplot2 - Advanced statistical analysis and predictive modeling - Performing multivariate regression analysis for sales forecasting - Implementing time series analysis for trend prediction - Conducting cluster analysis for customer segmentation - Building interactive geospatial visualizations using R's mapping libraries - Optimizing R script performance for large datasets and much more... The goal with the new guide is to get you thinking wider around what's possible and to learn various best practice techniques. Jump into it! #PowerBI #DataAnalytics #RStats #EnterpriseDNA #DataMentor

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Power BI, Data Types and Data Formats Data type and Data format are two different concepts in Power Query, even though they both affect how your data is presented. Here's a breakdown: Data Type: Defines the underlying nature of the data in a column. Examples: Whole Number, Decimal, Date, Text, etc. Determines how Power Query interprets and performs calculations on the data. Inconsistent data types can lead to errors or unexpected results. Data Format: Controls how data is displayed. Examples: Currency symbols, number of decimal places, date format (DD/MM/YYYY vs MM/DD/YYYY). Does not change the underlying value of the data. As far as possible and as best practice, define data types as the last step within your Power Query within Power BI #LifelongLearning #OnlineEducation #DataScience #ContinuousImprovement #SkillDevelopment #PowerBI #Oman #Muscat #GCC #Training #Consulting

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Principal Component Analysis (PCA) reduces data complexity by transforming variables into key components that capture the most variance. Choosing the Optimal Number of Components for PCA is crucial for effective data analysis. Here are some key methods to determine the optimal number of components: ✔️ Setting Threshold for Explained Variance: Select components that together explain a predetermined percentage of the total variance (e.g., 90%). This ensures that most of the data's variability is retained. ✔️ Using Kaiser’s Rule: Retain components with eigenvalues greater than 1. This rule suggests that components with variance greater than the variance of an individual original variable should be kept. ✔️ Plotting Scree Plot: Create a plot of the eigenvalues in descending order. Look for the "elbow point" where the explained variance starts to level off, indicating diminishing returns for additional components. ✔️ Using Permutation Test: Perform a permutation test to assess the significance of each component. This involves randomly shuffling the data and comparing the variance explained by the real data to the variance explained by the shuffled data. For a detailed explanation, check out my tutorial created in collaboration with Paula Villasante Soriano and Cansu Kebabci: https://lnkd.in/eNatWgmH Additionally, I have created an extensive online course on PCA, which explains the theoretical concepts as well as how to apply them in R programming. Further details: https://lnkd.in/eUnAqErz #statistics #research #visualanalytics

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Quick Guide to Grouped Bar Charts in Excel for Data Pros Top 5 Key Takeaways from this blog tutorial: 1. Grouped bar charts excel in visualizing data distribution, detailed comparisons, and temporal and multi-categorical analysis, facilitating insightful decision-making. 2. Excel offers a straightforward process for creating grouped bar charts, from selecting data to customizing for visual clarity and impact. 3. Customization options, such as adjusting bar width, choosing colors, and formatting labels and fonts, are crucial for readability and audience engagement. 4. Overcoming common issues like managing overlapping labels and tackling axis scaling ensures a smooth creation process and accurate data representation. 5. Effective presentation of grouped bar charts involves storytelling, clear design, and considering accessibility, elevating the chart from a mere visual to a compelling narrative. Read our Free Step-By-Step Blog tutorial which has a downloadable practice workbook and video. Click the link below 👇👇👇 https://lnkd.in/e2dMbZ2D 👍 Give us LIKE if you learned from this article ✍️ COMMENT below if you need any questions answered or enjoyed this article 🔄 Share the love with your friends/colleagues by REPOSTING this article with them 👉 CONNECT WITH ME https://lnkd.in/dzSe3_5a for more Free Excel & Office productivity tips #excel #msexcel #microsoftexcel #careerdevelopment #exceltips #exceltipsandtricks #excel101 #worksmarter #followformore #MyExcelOnline

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Enhance your categorical data visualizations with statistical insights! The ggpiestats() function from the ggstatsplot package creates pie charts for categorical or nominal variables, adding results from contingency table analysis directly into the plot’s subtitle. ✔️ Effective Categorical Visualization: Displays proportions of categories clearly, helping you visualize distribution and compare groups. ✔️ Built-In Statistical Testing: Includes results from Pearson’s chi-squared test for between-subjects designs or McNemar’s chi-squared test for within-subjects designs, with key insights shown in the subtitle. For single categorical variables, it also displays results from a one-sample proportion test (goodness of fit). ✔️ Customizable Design: Adjust colors, labels, and layout to fit your data presentation style, ensuring a polished and informative chart. ✔️ Seamless Integration with ggplot2: Works effortlessly with ggplot2, letting you customize and refine your pie charts using familiar syntax. The visualization shown here is from the package website, illustrating how ggpiestats() effectively combines visual presentation with statistical testing: https://lnkd.in/e2upCduU Want to master ggplot2 and its powerful extensions for impactful visualizations? Join my online course, “Data Visualization in R Using ggplot2 & Friends,” starting on November 25, 2024! Learn more: https://lnkd.in/ehRrzdJ7 #tidyverse #dataviz #datavisualization #bigdata #analysis

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SUCCESSFUL Power BI Report + delivers exact insights your audience needs + provides clear action points + has optimized user experience No, it's not only about colors. #powerbi #report #analytics

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In PowerBI, dynamic filtering of data based on user selected date https://lnkd.in/gDFpitQs #powerbidesktop #powerbi #datamodelling #dax #powerpivot #microsoftexcel #msexcel #excel

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This is so exciting. There are opportunities everywhere to improve what you’re doing. There are very limited barriers to most coding languages now when using the latest AI models like o1. It already was this way with prior models, but o1 is just more accurate, more often and produces more output each query. It’s really a big game changer for any data, app or automation work.

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5 SIGNS your may have SUBPAR Power BI Reports 1. Slow loading 🙅‍♂️ 2. Low user engagement ⬇️ 3. Most pages are cluttered with visuals 😱 4. Frequent user workarounds such as exporting files 🫣 5. Frequent users’ requests for additional view/filter/page😵‍💫 #powerbi #report #design

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Power BI and Data Modeling What is and why does one need Data Modeling... Data modeling is the process of defining how data is structured, organized, and accessed within a system. Here's a breakdown of key aspects: Core Concepts: Entities: These represent the core data subjects within your system. Examples in an e-commerce system could be "Product," "Customer," or "Order." Attributes: These define the specific characteristics of an entity. For a "Product" entity, attributes might be "product name," "price," or "color." Relationships: These connections show how entities are linked. An "Order" might be linked to a "Customer" (who placed it) and contain multiple "Product" entities. The Process of Data Modeling: Data Requirements Gathering: This involves understanding the business needs and what information the data model should support. Conceptual Design: Here, you define the high-level entities, attributes, and relationships without going into technical details. Logical Design: This translates the conceptual model into a more technical representation, specifying data types, constraints, and keys (unique identifiers for entities). Physical Design: This stage focuses on how the data will be physically stored within a database management system, considering factors like storage optimization and performance. Benefits of Data Modeling: Improved Data Quality: A well-defined data model reduces data redundancy and inconsistencies, leading to cleaner and more reliable data. Enhanced Data Analysis: A clear structure makes it easier to understand how data relates, enabling efficient querying and analysis. Simplified Application Development: A solid data model acts as a foundation for building applications that leverage the data effectively. Better Data Governance: Data models help establish data ownership, access control, and standards for data management within an organization. By following data modeling best practices, you can create a robust and flexible data structure that empowers data-driven decision making and simplifies data management within your organization. #LifelongLearning #OnlineEducation #DataScience #ContinuousImprovement #SkillDevelopment #PowerBI #Oman #Muscat #GCC #Training #Consulting

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10 Power BI Interview Questions: Mastering Data Warehousing, OLTP vs. OLAP, and Leveraging a Data Catalogue: 📍 What is a data warehouse and how does it differ from a database? 📍 Can you explain the concept of dimensional modeling? 📍 What are fact tables and dimension tables in the context of a data warehouse? 📍 What are the different types of data warehouse architectures? 📍 How do you ensure data quality in a data warehouse? 📍 Can you describe the main differences between OLTP and OLAP systems? 📍 What are some examples of OLTP and OLAP systems you have worked with? 📍 How do OLTP systems handle data modifications compared to OLAP systems? 📍 What is a data catalogue and how is it used in Power BI? 📍 Can you explain how Power BI integrates with Azure Data Catalog? If you also want to learn Power BI, join me in my 1.5 months live bootcamp, where I will teach everything from scratch to advance analytics 🔗 Enroll Here - https://lnkd.in/dhwQDCkg 📅 Live classes kick off on 27-Apr-2024 Happy Learning!! #success #job #failures #growth #placements #microsoft #storytelling  #bigdata #teaching #python #sql #powerbi #job #success #growth #datascientist #edtech #interviewpreparation #dataanalytics Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Shubhankit Sirvaiya Sahil Choudhary

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📊 𝐄𝐱𝐜𝐢𝐭𝐞𝐝 𝐭𝐨 𝐒𝐡𝐚𝐫𝐞 𝐌𝐲 𝐋𝐚𝐭𝐞𝐬𝐭 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 𝐃𝐞𝐬𝐢𝐠𝐧! 🚀 I'm thrilled to unveil my newest Power BI project, a comprehensive dashboard analyzing the 𝟐𝟎𝟏𝟗 𝐈𝐧𝐝𝐢𝐚𝐧 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐄𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 𝐟𝐨𝐫 𝐀𝐬𝐬𝐚𝐦. This dashboard provides a detailed breakdown of key metrics, including seat share, vote share, constituency-wise results, and party-wise outcomes. --------------------------------------------------------- 🔍 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: 1️⃣ Interactive Visualizations: Dive deep into the data with interactive charts, graphs, and maps that provide a clear understanding of the election results. 2️⃣ Constituency-wise Analysis: Explore individual constituencies to see voter turnout, candidate details, party affiliations, and margin of victory. 3️⃣ Party-wise Results: Understand the performance of each party with a breakdown of seats won and popular vote share. 4️⃣ Assembly Segments Analysis: See how parties fared in different assembly segments to gain insights into regional trends. --------------------------------------------------------- 📈 𝐈𝐦𝐩𝐚𝐜𝐭𝐟𝐮𝐥 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: By leveraging the power of Power BI, this dashboard not only presents data but also provides valuable insights into the political landscape of Assam during the 2019 elections. It's a testament to the effectiveness of data visualization in making complex information accessible and actionable. --------------------------------------------------------- 👀 𝐓𝐚𝐤𝐞 𝐚 𝐋𝐨𝐨𝐤: I invite you to take a closer look at this dashboard and explore the election data in an engaging and informative way. 𝐂𝐡𝐞𝐜𝐤 𝐢𝐭 𝐨𝐮𝐭 : https://lnkd.in/gazF2puE --------------------------------------------------------- 🤝 𝐋𝐞𝐭'𝐬 𝐂𝐨𝐧𝐧𝐞𝐜𝐭: Interested in collaborating on a data visualization project or looking for insights into your own data? Feel free to connect with me. I'm always excited to discuss how data analytics can drive informed decision-making. 📌 Thanks Sanjay Chandra Sikakollu #PowerBI #DataVisualization #ElectionAnalysis #AssamElections #LinkedInPost #powerbi

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Excel proficiency is a valuable asset in today's data-driven world, and practice is the compass that guides you towards excellence. Follow Lotfi GARZOUN ▫ MCT ▫ MOS Expert®🇹🇳 for more tips and tricks. BIIC | Certiport Authorized Testing Center #excel #dataanlytics #BusinessInsights #biic

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Power BI, Data Types and Data Formats Data type and data format are two different concepts in Power Query, even though they both affect how your data is represented. Here's a breakdown: Data Type: Defines the underlying nature of the data in a column. Examples: Whole Number, Decimal, Date, Text, etc. Determines how Power Query interprets and performs calculations on the data. Inconsistent data types can lead to errors or unexpected results. Data Format: Controls how data is displayed. Examples: Currency symbols, number of decimal places, date format (DD/MM/YYYY vs MM/DD/YYYY). Does not change the underlying value of the data. As far as possible and as best practice, define data types as the last step within your Power Query within Power BI #LifelongLearning #OnlineEducation #DataScience #ContinuousImprovement #SkillDevelopment #PowerBI #Oman #Muscat #GCC #Training #Consulting

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In PowerBI, search for a keyword in a description and bring over the category from another table https://lnkd.in/dtr4Gntc #powerbidesktop #powerbi #datamodelling #dax #powerpivot #microsoftexcel #msexcel #excel

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Calculating MAX of more than 2 Measures in DAX A situation I once found myself in while building reports in Power BI was to calculate the Maximum value from 3 measures (Actual Sales, PY Sales, Target) and tag whichever is the highest of the 3. The idea was if Actual Sales was tagged, it meant we were on the right track as a business which implies that our Actual Sales surpassed the Previous period's sales as well as the target set for the period. However, the issue is that the MAX function in DAX can only take 2 items - which means we can only compare 2 values to get the maximum and not more than per time. A workaround I've seen from people is to calculate the maximum in 2 rounds. First do something like "MAX( ActualSales, PYSales)" then MAX( Result1, Target). The problem with this approach is it is not flexible and can become complicated easily. Imagine you are now comparing 6 or 8 measures, it means you would be nesting more than 5 MAX functions. This is not performant as well. A better approach is to use the Iterator function MAXX and put all the measures in a virtual table within it. This calculation iterates over the measures in the virtual table as though they are rows of the table and then calculates the maximum value. This approach allows for the possibility of calculating the maximum value at once regardless of the number of measures in context. MAXX( { (ActualSales), (PYSales), (Target) }, [Value] ) Id_Analyst

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Well...this took a long time to create. But well worth it. 🙂 Here's our latest detailed guide to Mastering DAX in Power BI. 41 key topics to focus on when looking to unleash the real power behind Power BI. I'll let the resource pack do the rest of the talking! 🤜 🤛

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