Shri Wagh

Hi Connections, Here I would like to share my Power BI dashborad on an interesting data analysis project about UK Bank Customers. With deep dive into this dataset, I have gained the below insights; 1. England has maximum no. of male customers as compared to female. 2. Male customers have a high bank balance than female customers. 3. There are total 40 customers who have total 1.41 M bank balance amount in bank. 4. Out of 40 customers, the maximum proportion of customers is white collar & blue collar job category. Hence we observed that people who works in corporate offices & well settled jobs are more tend to have savings in their bank account. #powerbi #dataanalytics #datavisualization #businessintelligencetool #businessanalysis

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Day 4 of learning Statistics for Data Analysis Today I dived into some really important topics of stats that gives an overview of data being analysed. There explanation is given as: Guassian Distribution: Also known as normal distribution, it is symmetrical form of distribution having 50% of data on both sides of the calculated mean, forming normal bell shaped curve also known as "Bell Curve". This distribution normally follows the "Emperical rule" for its formation. So what is emperical rule? It is actually 68%-95%-99.7% rule, which means that 68% of data is distributed between mean (centre) and mean+standard deviation on the right and mean-standard deviation on the left. (It is difficult to explain in words unless we see some visualization for this concept 😉 ). 95% of data comprises between the points explaind above and one further point on both sides. Other concept in this Journey that I have learned today is; Central Limit Theorem: This theorem depicts that no matter what sort of distribution you have (i.e. Normal Distribution or Log normal distribution), if you extract sample out of with a constraint n>30 and take mean from those multiple samples it will form normal distribution. There is alot more but we will keep it for any other day. So join me in this journey as I move along. #DataAnalysis #DataScience #DataInsights #BigData #DataVisualization #DataMining #Analytics #BusinessIntelligence #DataDriven #MachineLearning #AI #PythonForDataScience #RProgramming #Statistics #PredictiveAnalytics #DataWrangling #DataCleaning #DataStorytelling #DataEngineering #DescriptiveAnalytics #DataInterpretation

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**Calling All Cricket Data Analysts!** I’d love to hear your thoughts on this: when it comes to weighing the performance of a T20 batter or bowler, what metric do you rely on the most? Is there a particular metric you believe truly captures a player’s value in this format? Have you devised any unique metrics or ideas you’d like to share? Personally, I’ve developed a few metrics that I’ve found to be over 90% accurate in evaluating the performance of both batters and bowlers in T20 cricket: 1. Runs Above Average Replacement (RAAR) – This measures how a batter’s performance compares to that of an average replacement player. 2. Runs Below Average Replacement (RBAR) – This evaluates the effectiveness of a bowler against an average replacement player. 3. Mean Stretch of Consecutive Dot Balls and Singles – Inspired by Harshad Chavan, this metric highlights how often a player strings together consecutive dot balls or singles, revealing their ability to either build or break momentum. What are your go-to metrics for evaluating players across all three formats of the game? Let’s discuss! If you’ve already worked on something in this area, feel free to share your insights or findings. Looking forward to hearing your thoughts and sparking a great discussion! #WednesdayWorks | #CricketDataAnalysis

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Day 4 of #21Vizzes21Days Socioeconomic analysis offers crucial insights into regional disparities and development opportunities. I’m excited to share my latest Tableau dashboard that provides a comparative analysis of socioeconomic metrics between England and its individual London boroughs. This dashboard is designed to offer a nuanced view of these dynamics, and it includes: 1️⃣ Measure vs Measure Values: Delve into the relationships between different socioeconomic factors to identify key trends and correlations. 2️⃣ Measure Values Across Boroughs: Examine how various indicators vary across London’s boroughs, offering a detailed perspective on local socioeconomic conditions. 3️⃣ Scatter Plot of Threshold Averages: Compare the average upper and lower thresholds for England as a whole versus London’s boroughs, shedding light on regional differences and disparities. By visualizing these metrics, the dashboard aims to enhance understanding of regional socioeconomic patterns and support data-driven decision-making. Explore the insights and let me know your thoughts! Link to my Dashboard: https://lnkd.in/eaQEtd-z #DataAnalysis #Tableau #SocioeconomicInsights #DataVisualization #England #LondonBoroughs #RegionalDevelopment #21Vizzes21Days #MOM #makeovermonday #W38

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Recently, I was inspired by Iris Mejuto Crego and took the FP20 Analytics Challenge 16 data to create a viz in tableau. It was great fun and I learnt a lot. Some of the lessons include: 1. Creating an advanced table with text, shapes, sparklines etc. following Samuel Parsons technique. 2. Using dynamic zone visibility to select between sheets (I'm late to the party). 3. Using blank shapes for navigation between dashboards using Lindsay Betzendahl technique. The viz was created in Tableau, designed using Figma with creative input from Yu Chen Chen. Let me know if you have any feedback Link to interactive viz: https://lnkd.in/euSAAf6a Lindsay's blog post: https://lnkd.in/eV5AXwUV. Samuel's video: https://lnkd.in/ebmhf8BD Iris's work: https://lnkd.in/ea9xDn23 hashtag #dataviz

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Timeline Slopes for Cricket in Tableau 🏏 Inspired by Krisztina Szűcs award winning work on 'Timeline Triangles'. 🔗 Inspiration Link: https://lnkd.in/diUNcEuN This week, I made an attempt at an adaptation to design for the use case of the aforementioned chart in #cricket using Tableau, highlighting the ball-by-ball difference in scoring between the two teams. The annotations provide context into match phase scoring variation and how one of the two teams despite a sluggish start, ended up blowing their middle overs consolidation by inability to accelerate the run scoring in the death over phase of the match. Please let me know if you believe the chart has the potential to highlight relative phase wise match progress and difference in run scoring between the two sides along with the impact of the fall of wickets on the scoring. 🔗 Tableau Public: https://lnkd.in/dEKrqF2P I'm always open to any form of feedback and I'll soon blog on how to create this easily using Tableau.✍️ #cricket #data #visualisation #analytics #dataviz #team #cricketanalytics #cricketviz #timelineplots #timelinetriangles #timelineslopes #cricketdata #sportsvizsunday #datafam

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Thrilled to share my latest case study IPL Season Analysis using tableau, where I've developed a comprehensive dashboard analyzing the IPL season! 📊 Key insights featured in the dashboard: Season Winner Orange Cap Winner Purple Cap Winner Total Sixes Hit Total Fours Hit Matches Won by Toss This dashboard provides a detailed overview of the season's highlights, offering valuable insights for cricket enthusiasts and analysts alike. Explore the data to see the standout performances and trends of this IPL season! Check out the dashboard to dive into the exciting world of IPL statistics and analytics! 🌟 #IPL #Cricket #Tableau #DataAnalytics #Dashboard #SportsAnalytics #CricketLovers #IPL #DataVisualization #CaseStudy

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🚀 Excited to Share My Latest Dashboard for Essies Ltd.! 🚀 I’ve been working on a new dashboard that provides insightful analytics for Essies Ltd., and I’m thrilled to showcase it. Here’s a snapshot of what I’ve created: 🔍 Insights: 1. Data Foundation: Developed a comprehensive reference table for dates and products to ensure reliable data. 2. Category Focus: Organized Product Category and Subcategory tables to maintain data clarity. 3. Retail Prioritization: Implemented three priority levels for retail sales: High, Mid, and Low. 4. Interactive Reporting: Added an interactive slicer for countries to easily filter and analyze data. 5. Key Metrics: The dashboard highlights crucial metrics, including: • Units Sold by Classification • Number of Units Sold • Sales of Retail Units • Sold Sales Units 6. Top Performers: Identified top 10 retailers based on unit sales and performance metrics. 🔎 Key Investigations: • Unit sales insights for resellers and scenarios without discounts. • Analysis of sales during promotional events. • Breakdown of unit sales by categories like bicycles, clothing, accessories, and parts. I’d love to hear your feedback and thoughts on this project! 🔗 Check out the dashboard on GitHub #DataAnalytics #DataDrivenSuccess #DataVisualization #PowerBIDesktop

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Here I’m starting out a data storytelling series ! Lesson 1: The Power of Clear Language in Data Storytelling Let’s assume you have analysed a e-commerce data, you notice that sales peak between 6 PM and 9 PM and that most purchases are made using mobile devices. Clear Language Communication ! - Finding Sales are highest between 6 PM and 9 PM, especially on mobile devices. - Explanation Instead of saying, "There's a significant positive correlation between mobile usage and purchase time during peak hours," you simplify it to, "Customers prefer shopping on their phones in the evening." - Technical Concept If you need to mention terms like "correlation," you can add, "This means there's a relationship between when people are on their phones and when they make purchases." Based on this, you suggest that the marketing team should focus their advertising efforts during these peak hours and ensure the mobile site is optimized for speed and usability. #dataanalytics #datascience #datainsights #datastorytelling #presentingthedata

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🚀 Excited to share some of my recent portfolio projects as a Data Analyst! 💼 🔍 Explore my projects in: Data Exploration & Cleaning with SQL Queries Data Analysis using Excel, Tableau, Power BI Prediction & Forecasting with Machine Learning Models 🚀 Let's exchange ideas and insights! Check out my portfolio website created on GitHub for free: https://lnkd.in/ganBinbk #DataAnalyst #PortfolioWebsite #DataAnalysis #GitHub #DataProjects #DataAnalytics Feel free to customize this post to reflect your unique projects and experiences!

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Hi #datafam, In my previous post, I had created a poll asking which metric is best to use to determine the best selling product. "Quantity" or "Sales"?? As a Data Analyst, asking the right questions is a very much needed skill. You can't assume or use gut feelings. You must ask right. I'm sure that if you are able to get answers to the following questions, you will certainly know what metric to use. 1. What is the primary goal of the analysis? 2. What is the primary goal of identifying the best selling product? 3. Are we more interested in the total revenue generated by the products or the number of units sold? 4. How important is it for us to understand product popularity versus financial performance? 5. How critical is profit margin in this analysis? Should we focus on products that generate higher profits, even if they sell fewer units? The reason you must ask stakeholders/clients these questions, is so that you can gain a clearer understanding of their priorities and the context of the analysis, this will then enable you to choose the most appropriate metric (quantity or sales) for determining the best-selling product. Ask questions Ask questions Don't just get data and jump into analysis. Ask the right questions. Your analysis/findings/insights/recommendations can never go wrong when you ask the right questions. #data #dataanalysis #dataanalyst #ask #questions

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📊 Excited to share my latest project on Tableau! 🚀 I've created a comprehensive dashboard showcasing the voting percentages across various states and constituencies in India. Here are some key highlights: 🗳️ **Insightful Visuals**: Utilized pie charts to illustrate voting percentages in each state, providing a clear overview at a glance. 📊 **Data Organization**: Implemented sets and groups to categorize constituencies based on voting trends, enhancing data clarity and analysis. 🔍 **Detailed Information**: Incorporated tables with candidate names and vote counts, offering a detailed breakdown for deeper insights. ⚙️ **Advanced Functions**: Leveraged advanced functions to streamline data processing and ensure accuracy in representation. This dashboard not only simplifies complex electoral data but also aids in understanding voting patterns across different regions of India. Excited to hear your thoughts and discuss how data visualization can drive meaningful insights! 🌟 #DataAnalytics #Tableau #DataVisualization #Elections #Visualisation #InteractiveActionFilters #TableauPublic #NothingLikeVoting #IVoteForSure Take a Look Feel free to explore the dashboard and share your thoughts! https://lnkd.in/dX-QZtrN

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🚀 Excited to share my latest project with you all! 📊✨ I've just wrapped up creating an interactive and visually stunning dashboard on global superstore sales using Power BI. 🌍💼 🔍 This dashboard not only dives deep into our sales data but also leverages Power BI's advanced AI features like smart narrative, key influencer analysis, and QA capabilities. 🤖📈 📌 Key Highlights: Smart Narrative: Transforming raw data into actionable insights with automated narratives. Key Influencer Analysis: Identifying factors that significantly impact our sales performance. QA Feature: Ensuring data accuracy and reliability with automated quality checks. 🎯 By harnessing these powerful tools, I've been able to uncover valuable insights and trends that are crucial for strategic decision-making. 📈💡 🌟 I'm thrilled about the potential of Power BI to empower businesses through data-driven decisions. 🚀 Let's continue to explore and innovate with analytics! 💪 Thanks to Aditya Goenka and Sharad A. for these learnings. #PowerBI #DataAnalytics #BusinessIntelligence #DataDrivenDecisions #DashboardDesign #AI #MachineLearning #InteractiveAnalytics #LinkedInLearning

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Here’s a list of questions asked in my interview at Tiger Analytics for the role of Data Engineer→ Hope it helps! 🚀 So, to start with the whole interview had 3 rounds... 📍ROUND 1: Big data, coding The round started with general introduction, questions around my project experience & tech stack, and then: 1.⁠ ⁠Write a SQL query to find duplicate records in a table. 2.⁠ ⁠How would you perform a join on very large datasets? 3.⁠ ⁠What types of joins would you avoid for performance reasons? 4.⁠ ⁠How do you process large CSV file without running to memory issues? 5.⁠ ⁠How do you optimize Spark jobs? 6.⁠ ⁠Explain partitioning and bucketing in Hive and their use cases. 7.⁠ ⁠What was your experience with desinging scalable and efficient data pipelines? 8.⁠ ⁠Calculate the total sales per customer and per product category on sales data using pyspark 9.⁠ ⁠CI/CD experience, couple of python questions as well 📍ROUND 2: Advanced big data, real time This round too started with a discussion around my Project experience, and then→ 1.⁠ ⁠How would you ensure data consistency and reliability in a distributed system? 2.⁠ ⁠How would you improve the performance of a slow-running ETL job? 3.⁠ ⁠Explain your experience with Cloud? explain the services used in Azure? 4.⁠ ⁠Explain your experience with ADF, activities used? how did you optimize the pipeline? 5.⁠ ⁠Explain your experience with Star schema, data warehousing expereince? 6.⁠ ⁠Explain the spark streaming experience? 7.⁠ ⁠In depth questions on CI/CD pipelines, what was your tools used to deploy code? 8.⁠ ⁠More questions on scenario based questions, around azure cloud. Even though I wasn’t able to clear the interviews, it was a great experience for me. I realised, that the reason for my rejection was that- i could have better answered the Scenario Based Questions. ⭐ I would suggest every Data Engineer to practice more on Scenario based questions in depth about and also practice extensive Mock Interviews with your mentors. However, I get it it’s really hard for working professionals to get the right guidance throughout their interview prep. So, if you’re also looking for all-around mentorship, I’d suggest taking help from Bosscoder Academy and getting guidance from industry veterans. #dataengineer #collab #interviewexperience #tigeranalytics

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Upon exploring Tableau Pulse ( an awesome tool for AI driven insights), I found some amazing features that seem like a gamechanger in data insights creation. 1. It is superfast in filtering data as compared to a typical dashboard ( guess Pulse doesnt work on VizQL) 2. Reduces hassle of permissions as all the permission sets are run through the data itself. 3. Through one simple metric definition, you can create n number of metrics. All you need is 1 measure and 1 Time dimension. 4. Automation of important and most valuable data cuts right at your doorstep through mail or slack. Monthly, weekly or daily. Tableau Tim Ngwena #tableau #ai

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