Abhishek Gautam

DS 32 ✅ PBI 9 ✅ Here are some top YouTube channels that you can use to learn about DAX and creating interactive Power BI dashboards, and then apply what you learn through experimentation. #data #sachinsatya #powerbi #dax

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The Data Analyst's Guide to Reproducibility (Part 1): https://wix.to/3cWX9tq #newblogpost #dataanalysis #reproducibility #newdataanalysts #careergrowth #datadocumentation #dataanalysistips

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Power BI in Hindi- DAX Function: RANK - How It Differs from RANKX| How to break ties: https://lnkd.in/gkce99ti #powerbiinterview #PowerBI #dax #powerquery #AmitChandakDA #businessintelligence #PL300 #powerplatform #bi #powerbidesktop #datatodiscovery #powerbitutorial

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Pick Your Suit Color with Power BI Created a fun and interactive Power BI report that allows me to pick the right color combination for a suit using SVG image and new slicer visual. #powerbi #dataviz #svgimage #powerbidesktop

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👉 Check out the latest article on 𝐌𝐚𝐱𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞, building upon the insights shared in my previous post about the "𝐔𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧". Dive deeper into optimizing your analytics environment for peak efficiency. 🎯 Read my previous post here: https://lnkd.in/gPKVfqRs #PowerBI #DataAnalytics #PerformanceOptimization #DataVisualization #Insights #Analytics #SubscribeNow #DataVisualization #ReportMakeover #DataInsights #VisualAppeal #DataViz #report #dashboard #pbicorevisuals #powerbiupdate #visualization #salestrends #FigmaTemplates #ContentCreation #DynamicReports #CommunityThanks #PWP #DAX #DAXDairy #DataAnalyst #Vega #BusinessIntelligence #Visualization #BI #PowerBIDeveloper #Deloitte #KPMG #EY #tcs #pwc #Infosys #Developer #PowerBIwithPrasad #Lumel #VisualCustomization #Creativity #LikeShareSubscribe

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AI is reshaping analytics: Discover how the latest Gartner Report predicts the future of BI 🤖📈 As a data professional who's been in the trenches of analytics, insights, and predictive modeling, I'm thrilled to share the latest industry benchmark. Here's what you need to know: 1. Microsoft Power BI continues its reign 👑   - Leader for the 17th consecutive year   - Positioned furthest right for Completeness of Vision   - Highest in Ability to Execute for 6th year running 2. Google makes a strong showing 💪   - Named a Leader, showcasing their innovative approach   - Integrating AI to revolutionize data interactions 3. Oracle joins the Leaders quadrant 🏆   - Recognized for its robust analytics capabilities What does this mean for us data professionals? It's time to upskill and adapt! The landscape is evolving rapidly, with AI integration becoming a game-changer. Whether you're in data engineering, visualization, or predictive modeling, staying ahead of these trends is crucial. What's your take on this year's Magic Quadrant? Which platform are you betting on for the future of analytics? Share your thoughts below! 👇 #GartnerMagicQuadrant #BusinessIntelligence #DataAnalytics #AnalyticsTrends #MarketResearch #DataInsights #BIReporting #TechInnovation #DigitalTransformation #DataDrivenDecisions

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Ever wondered how to turn data into actionable insights? 💡 Let me share with you the top 3 impactful analytics practices that I've found to be game-changers in the world of Business Intelligence. 🚀 hashtag #PowerBIEdition 1️⃣ Embrace Power BI's DirectQuery for Power BI datasets and Azure Analysis Services feature. It's like having a superpower that allows you to connect multiple datasets, even from different sources. Imagine the possibilities! 🌐 2️⃣ Utilize the Power BI Goals feature. It's like having a personal trainer for your data, pushing you towards your targets. Goals can be tracked in the Power BI Mobile app too, so you can keep an eye on your progress, even on the go! 🏃‍♂️ 3️⃣ Leverage the Power BI Universal Links feature. It's like having a universal remote for your data. You can access your Power BI content anywhere, anytime, on any device. Convenience at its best! 📲 Remember, it's not about having data; it's about understanding it. These Power BI features are not just tools; they are the keys to unlocking the true potential of your organization's data. 🔑 Now, I'm curious to know, which of these practices are you excited to implement in your organization? Or perhaps you're already using them? Share your thoughts in the comments below! 👇 If you found this post helpful, feel free to share it with your network. Let's spread the knowledge! 🔄 #PowerBI #DataAnalytics #BusinessIntelligence #MrBusinessIntelligence

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Interested in learning how to implement RAG in Databricks? Check out this fantastic demo notebook on Databricks https://lnkd.in/g6e58P_A #dataengineering #databricks

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Frequently used #tableau functions #data #dataviz #datavisualization #visualization #visualisation #viz #analytics #big_data #bigdata #businessintelligence #tableausoftware #tableaudeveloper #tableaudesktop

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Following the success of yesterday's post, I'm excited to share another one today! Was it the mention of a well-known Tableau influencer or the format that caught your attention? Let’s find out. Today, I’m diving into a newer visualization in Astrato Analytics - the word cloud ☁️, introduced this year. Stay tuned for some useful tips! 🏗️ Built in Astrato Analytics 🤖 AI titles powered by OpenAI ⚡️ Data powered by Snowflake Databricks Dremio Google Cloud #WordCloud #Visualizations #Qlik #Tableau #PowerBI #SigmaComputing

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📊 Drowning in data? Learn to choose the RIGHT visualization and make your insights pop! 💡 Ever stared at a spreadsheet, wondering how to bring those numbers to life? 🤔 "How to Choose the Right Data Visualization" is your ultimate guide to turning raw data into compelling stories. This comprehensive resource covers everything from basic bar charts to advanced Sankey diagrams. Whether you're tracking changes over time, comparing values, or exploring relationships between variables, you'll find the perfect chart type for your needs. Don't let your data stay hidden – learn to visualize it effectively and watch your insights make waves! 🌊 Hope this helps 😀 Keep learning 😊 CA Sweety Chaudhary Feel free to share, save this post and tag your connections whoever needs it. #DataVisualization #DataStory #BusinessIntelligence #linkedin #linkedinconnections #linkedinfamily #data

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https://lnkd.in/dcC-kGpu - Dimensional Modelling vs. One Big Table in Databricks Lakehouse - a very interesting article on data engineering and analytics architecture

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A kitchen without wires is incomplete. A workroom without instruments is ineffective. Similarly, a business without data is unproductive. Moreover, data alone isn't enough—insights and information only become valuable through data products and products using data! #DataDriven #BusinessIntelligence #DataProducts #DataValue #DataInsights 

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Explore how to import custom shapes and icons into #tableau #dashboards. Learn More: https://lnkd.in/drYjPqu5 #data #dataviz #viz #visualization #visualisation #bigdata #big_data #businessintelligence #analytics #dashboarding #tableaudesktop #tableaudeveloper #learningtableau

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DS 26 ✅ PBI - 4 How Important is ETL in Power BI? ✅ Extract: ✔️ Pull data from different sources like databases, Excel files, or online services. ✔️ Think of it as collecting raw materials from various places. ✅ Transform: ✔️ Clean, organize, and format the data to make it useful. ✔️ Imagine refining those raw materials into something usable. ✅ Load: ✔️ Save the prepared data into Power BI for analysis and reporting. ✔️ This is like storing the finished product in a place where it can be easily accessed and used. #data #powerbi #sachinsatya

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🤔 Did you know that in Tableau you can put a Dimension in Size?   ⌛ For a long time, I never thought of that.   💭 But one day, I was testing an idea and I thought, "Man, it would be great if I didn't need to make another variable for a measure to put on size."   💣 So I dragged the dimension that was on color to size as well, and BAM! It worked.   Wait, it gets even better! You can drag the Dimension values into the order you want (wouldn't make much sense otherwise, would it?)   There are some use cases out there for this!   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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Leveraging Hypothesis Testing for Business Growth: A Data Analytics Case Study In today's data-driven business environment, making informed decisions is paramount to success. Data analytics techniques, such as hypothesis testing, play a crucial role in extracting actionable insights from raw data. In this article, we'll explore how hypothesis testing can empower businesses to make strategic decisions and drive growth, using a sample case study involving a sales dataset. Hypothesis testing is a statistical method used to evaluate the validity of a hypothesis about a population parameter based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (H1), collecting data, and performing statistical tests to assess the evidence against the null hypothesis. Case Study: Improving Sales Performance Let's consider a hypothetical scenario where a retail company aims to increase sales by optimizing its pricing strategy. The company collects sales data from multiple stores over a period of six months and decides to conduct a hypothesis test to determine if reducing prices leads to a significant increase in sales. Hypotheses Formulation: Null Hypothesis (H0): There is no significant difference in sales between the current pricing strategy and the reduced pricing strategy. Alternative Hypothesis (H1): Sales will increase significantly with the reduced pricing strategy. Data Analysis: Using the collected sales data, the company calculates key metrics such as average daily sales revenue for both the current and reduced pricing strategies. Next, the company performs a hypothesis test, such as a two-sample t-test, to compare the means of the two samples and determine if there is a statistically significant difference in sales. Results Interpretation: After conducting the hypothesis test, the company obtains the test statistic and p-value. If the p-value is less than a predetermined significance level (e.g., 0.05), the company rejects the null hypothesis in favor of the alternative hypothesis, indicating that the reduced pricing strategy leads to a significant increase in sales. Insights and Recommendations: Based on the results of the hypothesis test, the company gains valuable insights into the effectiveness of the reduced pricing strategy in driving sales growth. Armed with this information, the company can confidently implement the optimized pricing strategy across its stores, leading to increased revenue and business growth. In conclusion, hypothesis testing is a powerful tool in the data analyst's toolkit, enabling businesses to make evidence-based decisions and achieve their growth objectives. By applying hypothesis testing techniques to real-world datasets, businesses can unlock valuable insights, drive innovation, and stay ahead in today's competitive market landscape. #DataAnalytics #HypothesisTesting #BusinessGrowth #DataDrivenDecisions #CaseStudy #SalesAnalysis PS: Data can be fun if you understand it.

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#WeightageDistribution in Statistics and Data Analytics Weightage distribution is key in statistics and data analytics. It helps in assigning importance to different data points or categories, enhancing the accuracy of results. Here’s how it works and how to implement it. 🔍 Understanding Weightage Distribution Weightage distribution involves assigning different weights to data points based on their importance or relevance. This is crucial for accurate analysis, especially when some data points are more significant than others. 🔧 How It Works - Identify the criteria for weightage. - Assign weights to each data point or category. - Use these weights in calculations to influence the results proportionately. 🚀 Implementation Steps 1. Data Collection: Gather all relevant data. 2. Define Criteria: Decide the importance of each data point. 3. Assign Weights: Allocate weights based on the defined criteria. 4. Apply Weights: Integrate these weights into your statistical models or calculations. 5. Analyze Results: Observe how the weighted data influences the outcomes. 🛠️ Tools for Implementation - Excel: Use weighted average functions. - Python: Utilize libraries like NumPy or pandas for complex calculations. - R: Employ weighted functions for statistical analysis. Weightage distribution enhances the precision of your data analysis by focusing on the most critical data points. #DataAnalytics #Statistics #WeightageDistribution #DataScience #DataAnalysis #Python #R #Excel

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