Himanshu Bansal

Possibly One of the Most Underrated Features of Tableau Cloud: ↳Tableau Pulse It won't: - Provide the full interactive experience of dashboards - Replace custom reporting for complex analysis - Offer in-depth visual exploration for every dataset But it does: - Speed insights with rapid access to critical metrics - Empower users by focusing on key, actionable information - Boost decision-making with easily consumable data at a glance Tableau Pulse is perfect for those moments when fast, impactful insights are the priority.

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In your Analytics career you can choose between 1. working within a single industry or domain, or 2. jumping around between different industries There are advantages to both! Sticking to one industry: - You probably get more replies when applying - You hit the ground running faster in a new job - Your domain knowledge compounds over time Jumping around: - Wider pool of job openings available to you - Get exposed to a wider variety of tools & methods - On average, it helps you grow more as a generalist Based on your experience, what else would you add? And what do you prefer? #data #analytics #career

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Analytics is simple in theory. Analytics in practice is another matter 🙃 Why is it simple? It's only 3 steps: 1. Understand business problems 2. Select the right technical solutions 3. Analyze, build and influence to drive outcomes What makes it so hard in practice? - Data itself is messy and something is always breaking - Implementing solutions is never straightforward - Some people are data-driven, others are not - Different people have different incentives - Stakeholders don't understand our work The list goes on...what else would you add? #data #analytics

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📊 How Exit Polls Work: A Data Science and Statistical Perspective 🗳️ Exit polls are a fascinating blend of data science and statistics, offering a preliminary glimpse into election outcomes. Here's a breakdown of how they work and why they're so significant, especially in the context of the 2024 Indian General Elections: 🔍 Design and Sampling: Uses random and stratified sampling to ensure diverse voter representation. Polling stations are chosen to capture urban-rural divides and various demographic groups. 📋 Data Collection: Trained surveyors collect data from voters exiting polling stations. Standardized questionnaires gather voting choices and demographic details. 📈 Data Analysis: Responses are weighted to correct sampling biases. Statistical models like regression analysis and machine learning identify trends and predict outcomes. 🔮 Predictive Analytics: Historical data and machine learning enhance prediction accuracy. Trends such as urban vs. rural voting patterns, youth voter preferences, and economic issue impacts are analyzed. 📰 Interpretation and Reporting: Results are reported with confidence intervals and visualized through graphs and heat maps for clarity. Why Exit Polls Matter: Real-time Insight: Provides immediate insights into voter behavior. Strategic Decisions: Helps political parties understand voter sentiment. Public Engagement: Engages the public and media, fostering participation and anticipation. Conclusion Exit polls, backed by robust data science and statistical methods, are vital for understanding voter behavior and predicting election trends. Let's dive into the data and uncover the stories behind the numbers! 📊🗳️ #DataScience #Statistics #ExitPolls #Elections2024 #India #PredictiveAnalytics #VoterBehavior #MachineLearning #PoliticalAnalysis #SurveyMethodology #ElectoralStudies

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One of the most common question asked in Data Engineering Interview: Difference between ETL, ELT, and Reverse ETL. 1️⃣ ETL: The Traditional Powerhouse Extract, Transform, Load (ETL) has been the go-to process for decades. It involves: 1. Extracting data from various sources 2. Transforming it to fit operational needs 3. Loading it into the target system (usually a data warehouse) 2️⃣ ELT: Flipping the Script Extract, Load, Transform (ELT) emerged with the rise of cloud computing and big data. The key difference: - Data is loaded into the target system before transformation - Leverages the power of modern data warehouses for transformation - Offers more flexibility and scalability 3️⃣ Reverse ETL: Closing the Loop A newer player in the field, Reverse ETL: - Moves processed data from warehouses back into operational systems - Enables data activation, turning insights into action - Bridges the gap between analytics and operations For career guidance in Data Analytics, job search strategies, resume review, interview preparation, etc., you can book a session with me here: https://topmate.io/itshb

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Here is a very interesting question asked in Probo for a senior data analyst position. We have a table which has 5 columns. a. User_id, poll_id, poll_option_id, amount, created_dt b. Users invest money on these different poll options - these entries are there in the table c. One of the options is the outcome of the event. Example : Event: How many matches will the Indian cricket team win in 2022? A) Less than 50 B) 50-60 C) 61-65 D) Greater than 65 India wins 63 matches in the end. Option C is the winner. Money invested in option A, B and D should be proportionately distributed amongst users who invested money in option C. For example - If the total money invested in option A, B and D is Rs. 1500 and there are 3 users who invested 500 in option C {250, 200 and 50} These users would receive (750, 600 and 150 respectively) - sum is 1500. Write down a query for the above settlement process. Check out my detailed explanation in the comments section. #sql #analytics

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Crunching numbers by day 📊 and microwaving dinner by night 🌜, my life oscillates between data dashboards and recipes. Recently, my inner data geek decided to spice up a typical Friday evening with a little price comparison experiment—because nothing says 'weekend' like a good old showdown between Swiggy and Zomato over a Sicilian Stuffed Grilled Chicken . Here's the scoop: armed with memberships to both, I popped the same dish into my cart on each app. Initial score? Swiggy at ₹490, undercutting Zomato’s ₹520—a clean ₹30 win for Swiggy 💰. But hold onto your hats (and forks 🍴): the plot thickened at checkout. After the sneaky add-ons like GST and 'because-we-can' charges, the gap shrunk faster than my wallet, down to ₹547 for Swiggy and ₹560 for Zomato, just ₹15 more. And who did I crown the chicken champ for the night? Drumroll... Zomato! Why? Because when hunger strikes, time is money, folks . Zomato promised a 45-minute delivery versus Swiggy's leisurely 60. True to their word, Zomato had me devouring deliciousness in under 45 minutes⏱️. Moral of the story? While Swiggy played the cheaper date, Zomato got me to the good part quicker. And when it’s just me, my couch, and my growling stomach, those extra 15 minutes are golden 🌟. Stay tuned for more epic showdowns in the saga of ‘Where to Waste My Wages This Week’—because in this competitive market, it's all about savvy choices 🤔. May the best app win! 🏆 Zomato Swiggy

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Why should you use Dynamic Zone Visibility in Tableau? The reason is quite simple. Too much data makes dashboards hard to read. DZV helps fix that. Here's what it does: 1. Makes things clear 2. Shows just what you need 3. Helps make better choices 4. Lets users pick what to see 5. Gets more people using data And here's what it means 1. Simple views help people learn more. 2. Clear data means smart choices. 3. You choose what data you want. 4. Easy to read, easy to use. 5. No more messy screens. Ready to make better dashboards? Struggling with Dynamic Zone Visibility? Tomorrow’s workshop is guaranteed to help (or your money back) 🗓️ Tomorrow (Nov 14) ⏰ 7:30pm GMT / 2:30pm ET Link to sign up in comments 👇 __________ P.S. When you sign up for the workshop, you get one month FREE access to Next-Level Tableau

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The ability to understand correlations & relationships between multiple factors is the most important skill to look for when hiring an analytics person. In your next round of hiring for an analyst, - Give them a problem statement - Ask them to build an exhaustive flow chart listing the hypothesis/ reasons behind the problem - Let them talk you through how each hypothesis/ reason is related to the problem statement This alone can help you easily differentiate great analysts from bad ones - because product analytics is less about data, building reports, knowing tools, etc., and more about making sense of data & drawing correlations. #anshdoesanalytics #analytics #hiring

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🚨The greatest drop-off is from Product Details Page To Cart Page, so we must improve our Product Details Page! Not so fast ✋ In today's age of data obsession, almost every company has an analytics infrastructure that pumps out a tonne of numbers. But rarely do teams invest time, discipline & curiosity to interpret numbers meaningfully. I will illustrate with an example. Let's take a simple e-commerce funnel. Home Page ~ 100 users List Page ~ 90 users Product Display Page ~ 70 users Cart Page ~ 20 users Address Page ~ 15 users Payments Page ~12 users Order Confirmation Page ~ 9 users A team that just "looks" at data will immediately conclude that the drop-off is most steep between Product Details Page & Cart Page. As a consequence they will start putting in a lot of fire power into solving user problems on Product Display Page. But if the team were data "curious", would frame hypothesis such as "do certain types of users reach cart page more effectively than others?" and go on to look at users by purchase buckets, geography, category etc and look at the entire funnel end to end to observe patterns. In the above scenario, it's likely that the 20 cart users were power users whilst new & early purchasers don't make it to this stage. The reason could be poor recommendations on the list page or customers are only visiting the product display page to see a larger close up of the product. So how should one go about looking at data ? Do ✅ Start with an open & curious mind ✅ Start with hypothesis ✅ Identify metrics & counter metrics that will help prove/disprove hypothesis ✅ Identify the various dimensions that could influence behaviours - user type, geography, category, device type, gender, price point, day, time etc. The dimensions will be specific to your line of business. ✅ Check for data quality and consistency ✅ Look at upstream and downstream behaviour to see how the behaviour is influenced upstream and what happens to the behaviour downstream. ✅ Check for historical evidence of causality Dont ❌ Look at data to satisfy your bias ❌ Rush to conclude your interpretation ❌ Look at data in isolation - - - TLDR - Be curious. Not confirmed. #metrics #analytics #productmanagement #productmanager #productcraft #deepdiveswithdsk

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A data analyst and a social media nut case both tell stories, but what’s the difference? Data analyst digs up the data and tells a story based on relevant and concrete facts. A social media nut case tells a story first and then tries to find edge cases data points to support it. Don’t you agree ? #data #analysis #friday #thoughts

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Need to explore relationships between variables while showing statistical insights? The ggscatterstats() function from the ggstatsplot package is your go-to tool. It combines scatterplots with marginal distributions, giving a comprehensive view of your data along with key statistical results. ✔️ Detailed Visuals: Displays scatterplots with overlaid marginal histograms, making it easy to see patterns and distributions of each variable. ✔️ Built-In Statistical Analysis: Automatically includes statistical test results, correlation coefficients, confidence intervals, and more in the subtitle, providing essential insights without extra work. ✔️ Flexible Design: Customize plot elements and choose from various types of marginal distributions to best suit your data. ✔️ Seamless Integration: Works directly with ggplot2, ensuring you can modify and style your plots using familiar syntax. The visualization shown here is from the package website, demonstrating how ggscatterstats() makes it simple to analyze relationships with added statistical details: https://lnkd.in/e2upCduU Ready to master ggplot2 and its powerful extensions to create impactful visualizations? Enroll in my online course, “Data Visualization in R Using ggplot2 & Friends,” starting on November 25, 2024! More information: https://lnkd.in/ehRrzdJ7 #datavisualization #package #tidyverse #datastructure #statisticsclass #rstats #datavisualization

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Jasprit Bumrah: A Statistical Analysis of T20I Excellence. Dive into Bumrah's T20I dominance with key stats and insights (recent 2 matches stats not included). #Cricket #JaspritBumrah #T20I #SportsAnalytics #CricketStats #FastBowler #IndianCricket #CricketAnalysis

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In the age of social media, it’s easy to fall into the trap of comparing ourselves to others. We see posts about promotions, new ventures, and successes, and it can make us feel like we’re falling behind. But here’s the thing... People rarely post their struggles, setbacks, or failures online. What we often see is just the highlight reel, not the full story. Each of us is on our unique path with our own challenges and timelines. Comparing your journey to someone else’s curated content can undermine your confidence and distract you from your progress. (And research has show that it can also greatly impact our life satisfaction and self esteem.) Remember, success isn’t just about the destination; it’s about the lessons learned and the growth experienced along the way. So, keep going, stay focused, and be kind to yourself. Your journey is uniquely yours, and that’s something to be proud of and to celebrate. . . . . . #PersonalGrowth #Authenticity #MindsetMatters #ProgressNotPerfection #GrowthMindset

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New Data Analysts: Your audience will not remember everything about your presentation. They will not retain all the information and data you delivered. But they will remember the stories that you tell. The best analysts are the best storytellers.

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🏏 How Data Analysis is Revolutionizing Cricket! 🏏 Ever wondered how your favorite cricket teams make strategic decisions on the field? It’s all about **data analysis**! From predicting player performance to deciding field placements, data analysis plays a crucial role in modern cricket. But it doesn't stop there—data-driven decisions are transforming industries far beyond the pitch! 📊 In Cricket: - Performance Metrics: Analyzing player stats to determine the best lineup. - Game Strategy: Using historical data to predict opponents' moves. - Injury Prevention: Monitoring players’ fitness levels to reduce injury risks. 🎯 Beyond Cricket: - Healthcare: Data analysis helps in patient diagnosis, treatment plans, and improving healthcare outcomes. - Marketing: Understanding consumer behavior through data to drive sales and tailor marketing strategies. - Finance: Analyzing market trends for smarter investment decisions. 💡 The Takeaway: Whether you're on the field, in a hospital, or in a boardroom, data analysis and research methodology are essential skills that open doors across industries. Mastering these tools can set you apart, no matter what field you’re in! 🌟 Save this post if you’re looking to explore how data analysis can boost your career in sports, healthcare, marketing, and beyond! #DataAnalysis #CricketStrategy #ResearchMethodology #HealthcareAnalytics #MarketingInsights #CareerSkills #SportsAnalytics #SaveThisPost #DataDrivenDecisionMaking #StudentLife

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If you’re struggling with setting up your analytics stack and are confused about how it should function, read below. A good analytics stack must be scalable and answer all teams' analytical needs (product, marketing, engineering). So, an analytics environment should cover four major areas: - User Behavior Analysis: Understand how users interact with your app, such as what features they use, where they drop off, what paths they take through it, etc. - System Performance Analysis: Track backend processes, API call successes and failures, load times, and other system-related metrics that affect user experience. - Integrated User and System Analytics: Combine data from user behaviors and system performance to get a holistic view of your product's health and user engagement. - Communication Based on User Actions: Trigger communications like emails, SMS, and push notifications based on specific user interactions or milestones achieved within the app. You need three categories of tools, to achieve this: - Product analytics tools (Mixpanel, Posthog, Amplitude): They’ll help you track and analyze how users interact with your product. You can gain insights into user journeys, retention, conversion funnels, etc. using these tools. - Visualization and Data Warehousing Tools (Metabase, Databricks, BigQuery): They’ll help you perform SQL queries on your data, integrate various data sources (including data from product analytics tools), and visualize data in a user-friendly manner. They are crucial for both system performance metrics and deeper analytics that combine multiple data sources. - Communication tools (Klaviyo, Intercom, OneSignal, MoEngage): They’ll enable you to engage with your users through targeted communication based on their interactions with your product. You’ll probably have to use a 3rd party tool like Zapier, Appypie to connect the tool with the product analytics tool, to automate workflows & communications (in case you do not want to send data to these tools directly). I hope this was helpful. Feel free to shoot questions, if any. I’d love to help you make the right choice with setting up your analytics infrastructure. #product #analytics #founders

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