Talha Khan

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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Learning and understanding the concept and functions of Power BI it won't take much time.May be less than a month you can understand. But If you want to get rich experience or get pro on that and get real time experience it will take a few years. minimum 3 to 5 years. here is the small example... Watching a movie or a series will take less than a week.But if you want to take or produce a movie or series you need a year of experience and knowledge. think and act wisely 😉

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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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Some non data food of thoughts for data professionals which are equally important as hard skills to thrive in longer run. 1.Develop a Strategic Muscle Examine the problem critically before solving it. Quantify value proposition of the problem using well defined and tangible metrics which are aligned with long term goals. 2.Get the Basics Right Understand core fundamentals of the business and it’s consumers. Have thorough understanding of the tools, its functionalities and limitations before applying them. Keep problem at the center and work towards a solution,rather aligning problem to the solution. 3.Remain Abreast Be proactive and stay updated on long-term vision and key initiatives of the business. Cultivate good listening skills, stay informed about the latest trends in your field, ask pertinent questions and reflect on these insight by engaging with your peers & leaders. 4.Be a Good Storyteller Storytelling is a tough art to nail, if not done well your insights might go unnoticed.Harness the power of visualization, focus on summarizing insights emphasizing the impact. Make your storytelling interactive by encouraging audience to participate. More in details here 👇🏼

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One of the best features in power bi is that data modelling #powerbi #Datamodel #data

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🚀 **Data Science & AI: Where Are We Heading?** 🚀 The world of tech is buzzing with exciting updates in Data Science, AI, and Machine Learning! From AutoML and MLOps simplifying model building to Data Fabric revolutionizing data management, the horizon is expanding. 🌐 Besides, who can ignore the leap in Data Visualization tools like Tableau and Power BI making insights more accessible? AI's future shines bright with advancements like Transformer models (think BERT & GPT-3) transforming NLP, and Autoencoders pushing the boundaries of what machines can "imagine." But it's not just theory; these innovations are reshaping industries. In healthcare, AI is powering predictive analytics for better patient outcomes. Finance is seeing a revolution with smarter fraud detection, and the automotive world is edging closer to fully autonomous vehicles. 🔍 NeurIPS & KDD conferences are showcasing groundbreaking research while market reports predict the data analytics market to reach $274.3 billion by 2027! This only hints at the limitless potential and ever-growing importance of these fields. So, here's a question for all the dreamers, innovators, and skeptics alike - **What do you think is the next big breakthrough in AI or Data Science?** Let's discuss! 🔥 #DataScience #ArtificialIntelligence #MachineLearning #Innovation

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Data Storytelling: Turning Numbers into Narratives Data storytelling is the art of transforming complex data into engaging narratives that resonate with audiences. It's about finding the story hidden within numbers and presenting it in a way that's both informative and inspiring. Here are 10 ways to master this skill: * Know Your Audience: Understand their needs, interests, and level of data literacy. * Define Your Story: Clearly articulate the main message you want to convey. * Choose the Right Data: Select relevant data that supports your narrative. * Visualize Effectively: Use charts, graphs, and infographics to enhance understanding. * Create a Narrative: Structure your story with a beginning, middle, and end. * Use Storytelling Techniques: Incorporate elements like conflict, characters, and a climax. * Practice Empathy: Put yourself in your audience's shoes. * Iterate and Refine: Continuously improve your storytelling based on feedback. * Leverage Technology: Explore interactive storytelling tools and platforms. * Practice Regularly: The more you tell data stories, the better you'll become. By mastering data storytelling, you can unlock the full potential of your data and drive meaningful impact. #datastorytelling #datascience #storytelling #visualization #analytics #businessintelligence #data #bigdata #dataanalyst #datascientist #techtalks

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3 Data Engineering Myths to Leave Behind in 2025: 1- More Data Means Better Insights It’s easy to think that the more data you have, the better your insights will be. But quality always beats quantity. Too much irrelevant data can actually slow down processing and clutter up valuable insights. Focusing on targeted, high-quality data is where the real value lies. 2- Manual Monitoring is Sufficient for System Reliability Relying solely on manual checks or basic monitoring tools is risky, especially as systems get more complex. Manual processes leave room for error and slow down response times. Today’s best systems use AI-driven monitoring to catch issues in real time and keep operations stable. 3- One Data Pipeline Fits All Data engineering needs are as unique as the companies behind them. A one-size-fits-all pipeline rarely meets the specific demands of large-scale, dynamic environments. Customizing pipelines for specific workflows, data types, and performance needs can make all the difference in speed and reliability. Here’s what I’d do instead: 1- Focus on Data Quality over Volume. Make sure data is relevant, clean, and ready to use. High-quality data gets you to insights faster without the excess noise. 2- Adopt Real-Time, AI-Powered Monitoring. Systems that monitor and adjust on their own save time and reduce risk, especially as complexity grows. 3- Customize Pipelines Based on Specific Needs. Tailored pipelines mean faster, more reliable data processing that’s aligned with the company’s unique requirements. As data engineering continues to evolve, leaving these myths behind can help teams stay efficient, scalable, and ready for growth. #DataEngineering #TechMyths #RealTimeMonitoring #DataQuality #ScalableSolutions

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The best analytics outcomes happen when we understand decision tradeoffs. It’s not enough to analyze data and tell our stakeholder what the best option is. We should also consider and communicate things like - what are the costs of A vs B? - how likely is A to succeed vs B? - how reversible is A as opposed to B? - what happens if we choose A instead of B? - is there an option C? How else do you like to frame a decision? #data #analytics

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Sometimes the biggest wins with Data are powered in the most unexciting ways: - reconciling two data sources - a lot of CASE WHEN in a SQL statement - a few bar charts & line charts on a new dashboard - asking "why?" several times until you reach the bottom So remember, you don't have to be using ML or AI or the latest trendy tool to make an impact. A good spreadsheet, a simple query and some common-sense visualizations will often get you where you need to go. #data #analytics #simplicity

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Hey Power BI enthusiasts, I'm a senior developer at a well renowned company in Dubai. I face multiple challenges and roadblocks from time to time developing the reports which sometimes doesn't have an answer available directly on web. I'm looking forwards for someone who I can discuss these advanced issues faced to gain experience and share possibilities benefitting both parties. Let me know if someone experienced is available to discuss these advanced issues and also require any opinions on other related issues. Note: No monetary transactions involved, purely learning based. 😂 #powerBI #LearningAndDevelopment #Developer #DAX #DataModeling

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Today marks 40 years since Microsoft Excel revolutionized the way we work, let's appreciate how it has shaped our work lives and continues to evolve with features like Power BI integration and AI-powered insights ! 🎉📊 Here's to Excel - the unsung hero of the business world! 🚀💼 #Excel #ProductivityTool #BusinessInnovation #DataAnalysis

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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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One of the key take aways after completing the project planning and management is that "Multitasking is Bad" for 2 main reasons: 1. We lose focus on the task at hand 2. We will end up delaying rather than increasing productivity But I see when we are in managerial level we might have to multitask. ("Expectation"). How do we manage this? What tasks can be multitasked and what cannot. Please do know your thoughts in comments

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The best data visualization tool is not: - Power BI - Tableau - Looker - Sigma - Excel The best data visualization tool is: - The one that helps the business connect, understand, and make use of the data effectively. #EGDataGuy

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Presenting another Power BI dashboard where I have shown the total amount of Sales,Cost and Profit with Countrywise, Sub-Categorywise. 😊 Also have shown the mode of shipping and sum of quantity sold in Regionwise. Along with this you can find the count of order in year and quarterwise. ⏳ ✅ Here I have used Slicer, Clustered column chart, pie chart, Map, cards and linechart to prepare the dashboard. #Power_BI_dashboard #MSBI #Visualization

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View my verified achievement from Tableau.

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90% of success in Analytics comes from simply not giving up. Being good at Analytics requires such a wide variety of skills - it can seem daunting. But the upside is that you don't need to be a master in any of them to be successful. You just need to keep going. If you - keep learning new skills - work through obstacles that come up - use each failure as an opportunity to grow then you're on the right path and you'll keep getting better. So if you're feeling stuck or down today: - try writing that difficult query in a new way - set aside time to finally learn that new skill this week - try a new communication approach with that one stakeholder And just don't give up! #data #analytics 

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Why is A/b testing one of the requirements on almost all data analyst and scientist job applications? Its because correlation does not mean causation. Think about the ways of analyzing how users engage with a product/service: KPIs NPS Surveys Customer Support Tickets  Reviews  Segmentation  Funnels  LTV models  AI/ML predictive models  Clustering The list could go on and on. BUT all of the above methods only show a correlation with a users action and cannot be directly linked to causation (i.e. why a user did something). Experimentation is the only way to concretely answer why a user did x, y, and z. That’s what being a data driven company means: rigorously A/B testing your product to improve it and why its so important for data professional to understand how and when to use it. This post was inspired by a brilliant lecture done by Timothy Chan If you want to learn more about ab testing follow: Timothy Chan Statsig VWO #dataanalytics #datascience #experimentation 

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