Vyjayanthi K.

Exciting developments are underway in the realm of data analytics and engineering as Microsoft integration into Tableau becomes a reality. This integration holds immense promise and is poised to revolutionize data visualization and analysis for professionals across various industries. The potential synergies between these two powerful platforms are particularly intriguing, especially considering scenarios where individuals within the same organization utilize them in disparate departments. As anticipation builds, we eagerly await further details on how this collaboration will unfold and the transformative impact it will have on data-driven decision-making processes. https://lnkd.in/dXuZutyE

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In Introduction to Snowflake, I learned fundamental concepts about Snowflake's distinct architecture and database concepts. The session covered topics such as DDL and DML, the importance of data types and their conversions, as well as hands-on exercises focusing on Snowflake's functionality. Additionally, the course delved into joins, sub-queries, and query optimization, with a special emphasis on handling semi-structured data, particularly JSON.

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Exciting News, LinkedIn Family! I just published another article to my snowflake series: "An Interactive Guide to Snowflake Stages and Storage Integration". In this article, I dive deep into: 1. The nuances of Snowflake Stages and their role in streamlining data movement 2. How to leverage COPY commands for bulk loading and the power of Snowpipe for continuous data ingestion 3. Practical examples of integrating storage solutions with Snowflake for optimal performance But wait, it’s more than just theory! I’ve included interactive examples and code snippets to help you apply these concepts in real time! Curious to enhance your Snowflake skills? Check it out and let me know what you think! #SnowflakeData #DataEngineering #CloudComputing #ETL #DataLoading #DataOptimization #RealTimeData #Snowpipe #CloudData #BigData

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Bayesian regression has answers for so many problems in the marketing mix modelling space. But it is so very slow. So. . . conjugate priors, then? Normal distributions for everyone! #marketingscience #marketinganalytics

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Had fun playing around with some of the newer Tableau features. Specifically, I was working with the dashed lines introduced a while back and learned a couple of tricks with regards to labeling the endpoints of this viz. https://lnkd.in/gXxMru38 #datafam #MakeoverMonday #dataviz

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🎉 Milestone Alert: 8 Years of Professional Growth! 🎉 Today, I’m proud to celebrate 8 years of an incredible journey that has shaped my career in ways I couldn’t have imagined. Here's a snapshot of my path: - 1.5 years in EdTech 📚 - 3.5 years in Analytics Consulting 📊 - 3 years in Supply Chain Analytics 🚚 Throughout these years, I’ve navigated through various stages of analytics maturity within organizations, contributing to and learning from each experience. Here’s what I’ve been up to: ✅ Creating impactful Data Science Proof of Concepts (POCs) 🚀 Developing Recommendation Systems 💬 Diving deep into Natural Language Processing (NLP) 🏭 Solving complex Analytical Problems 🚚 Currently shaping the future in Supply Chain Analytics This journey has been filled with immense learning, challenges, and opportunities for growth. I’m excited about what the future holds and am eager to continue making a difference in the world of analytics. Here’s to the next chapter! 🎯 #CareerMilestone #Analytics #DataScience #SupplyChain #ProfessionalGrowth

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Tableau had so much fun with Data + Movies, that they're launching Data + TV! I had fun playing with the data and created this viz about the top-rated Science Fiction shows 🖖 💫 . Viz: https://lnkd.in/e4fuWkCs Data + TV: http://tabsoft.co/3XgfB2w #DataViz #Tableau #ScienceFiction #DataPlusTV #DataVisualization

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Excited to showcase my Tableau Public profile, which I've been working on lately! 📊 ✨ Check out my Tableau Public Profile: https://lnkd.in/dQEXvkH4 I've created various interactive dashboards that demonstrate my ability to turn data into actionable insights. I invite you to explore them in my public dashboard and follow me. I have also recently completed the following certification on LinkedIn Learning: 🏅 Tableau For Data Scientists This course, taught by expert Matt Francis, has deepened my understanding of data analysis and visualization, covering everything from discrete and continuous data to creating interactive maps and engaging dashboards. Let’s connect on Tableau Public to explore my latest projects and dive into the world of data storytelling together. Whether you're in need of data-driven solutions or simply want to geek out over visualizations, I'm here to collaborate! I'm looking forward to applying these new skills in upcoming projects and contributing to data-driven decision making. hashtag #DataVisualization #Tableau #LinkedInLearning #ContinuousLearning #DataAnalytics #ProfessionalDevelopment

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There’s too much noise about landing Data Analytics jobs. (Read this if you want to break into data analytics.) Over the past year, my DMs have been filled with questions like: “What skills do I actually need?” “How do I start?” “What are recruiters looking for?” I’ve been in your shoes—confused, overwhelmed, and unsure of where to begin. But through my journey to becoming a Senior Data scientist. ↳ I figured out what works, (more importantly, what doesn’t.) Now, I want to make it easier for you. That’s why Rishabh Dhingra, Sahil Gogna and I are hosting a Data Analytics  Workshop. Here’s what we’ll cover: - What recruiters care about most (insider industry insights) - How to get started (the exact roadmap you can follow) - How to build a resume that lands interviews - What is a data mindset/analytical mindset Make sure you don’t miss it. Join us at YWCA Toronto, on November 24th (Time - 10:30 AM to 01:00 PM.) Sign up here: https://lnkd.in/gXTgjZwC P.S. Full event details are in the pinned comment!

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Generally agree with the sentiment of this. I assume the unpopular opinion for some is the revenue generation. It’s a North Star metric that only significantly mature data teams likely can realize. But value generation is still a spectrum, with revenue on the far end. If data teams can’t identify / articulate how operational efficiencies, cost avoidance, data literacy improvement, etc. are realized, then they are 1 Jupyter notebook model away from becoming a cost center. Don’t make consumer and product an afterthought

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📊 Navigating the Challenges of Qualitative Forecasting 📊 While qualitative forecasting brings invaluable insights into demand forecasting, it comes with its own set of challenges. Understanding these can help organizations better navigate and leverage qualitative data for more accurate forecasts. Here are some key challenges and how to address them: 🔍 Information Limitations: Qualitative forecasting often relies on input from the salesforce and external reports. However, forecasters frequently face issues with: Timeliness: Getting sales forecasts on time can be challenging. Relevance: External reports might not be up-to-date. Format: Data might not be in a usable format, requiring extra time for conversion. 💡 Solution: Streamline the process by setting clear deadlines and providing tools that ease data submission and integration. Regularly update and standardize data formats for consistency. 💰 Cost Issues: Involving senior executives, product managers, and salespeople in forecasting can be costly in terms of both time and money. These professionals are highly paid and have numerous responsibilities, making it crucial to use their time efficiently. 💡 Solution: Optimize the forecasting process to make it as efficient as possible. Use technology to automate parts of the process and only involve high-level personnel when necessary for strategic decisions. 🔍 Failure to Recognize Patterns: Humans can struggle to identify patterns in historical data as accurately as statistical tools. This can lead to missing existing patterns or seeing patterns that don’t exist. 💡 Solution: Combine qualitative insights with quantitative analysis. Use statistical tools to identify patterns and then validate or adjust these insights with qualitative data. 🎭 Personal Agendas: Qualitative forecasting can be influenced by personal or organizational biases, such as: Salespeople adjusting forecasts to affect their quotas. Overestimating demand to ensure product availability. Inflating forecasts to push new products through development processes. 💡 Solution: Foster a culture of transparency and accountability. Implement metrics to evaluate forecast accuracy and hold contributors accountable for biases. Encourage a collaborative approach where the goal is accurate and unbiased forecasting. 🔧 Key Takeaway: Qualitative forecasting is a powerful tool when used correctly. By addressing these challenges, organizations can better harness the insights from qualitative data to create more accurate and actionable forecasts. #Forecasting #BusinessStrategy #DataScience #DemandPlanning #Analytics #MarketResearch

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If I had to start from 0 with Tableau here's what I'd do: (how I actually got noticed) → Put the majority of your learning time into your Tableau Public portfolio Here’s why: 1. **Instant Credibility**: Your Tableau Public portfolio acts as a live resume. Employers, clients, and peers can see your work, skills, and creativity in real time. No need to just talk about your expertise—you can **show** it.     2. **Community Recognition**: Tableau Public isn’t just a tool, it’s a community. Having high-quality visualizations can get you noticed, featured, and shared. This exposure can lead to new opportunities and connections that may never have come your way otherwise.     3. **Constant Learning & Improvement**: A public profile keeps you motivated to innovate. Each new visualization is an opportunity to experiment, learn new techniques, and raise the bar. The feedback and inspiration from the community only push you further.     4. **Visibility to Opportunities**: Recruiters, companies, and collaborators use Tableau Public to scout talent. A standout profile could open doors to projects, roles, or collaborations that others simply won’t get access to. The trick is to find a topic from interesting data sources that you enjoy.  You'll stick to it longer. 1. makeovermonday 2. data.world 3. kaggle This is exactly how I have landed interest in my Tableau expertise. And how I've been recruited for multiple positions. Pick a data source, viz it and sell your skills on Tableau Public.

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Life’s Update: Professional: I am extremely happy to share that I have been promoted to Lead Data Scientist. My learnings: ✍️ Be responsible for deliverables and QA. Communicate well. ✍️ Take ownership of your work. This builds trust with your manager. ✍️ Expand your horizons. This way, seniors will become familiar with you and your work. Personal: I have completed my Umrah this EID 🌙. Stayed 11 days in Mecca and 6 days in Madina. Life’s most pure days. My learnings: ✍️ If you intend to perform Umrah/Hajj, do it at a younger age. ✍️ Avoid purchasing an internet plan unless necessary. Most hotels provide complimentary WiFi, which is usually sufficient. ✍️ Connect yourself totally to Allah and His mercy. Completely submit to His might. Special thanks to my current organization for their support ❤️. Comment your experience and advice.

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Did you know Mixpanel has a built-in feature to easily visualize your DAU, WAU, and MAU metrics? Here’s how to find it (Attached screenshot too): - Go to the Insights report. - Select an event of your choice. - Choose “Unique Users" as measurement, & click on the Settings button on the right - Choose your desired interval - DAU, WAU, MAU, or Cumulative Mixpanel calculates these metrics as follows: - DAU (Daily Active Users): When viewing a daily frequency chart, Mixpanel calculates the unique users from the last 24 hours for each day and displays that number for the day. - WAU (Weekly Active Users): On a daily frequency chart, it captures the unique users who performed the event within a 7-day window, starting from the current date shown on the chart, and displays that number for the specific day. - MAU (Monthly Active Users): Similarly, MAU is calculated by considering unique users who performed the event within a 30-day window from the current date on the chart. I hope this makes it easier to track your user engagement metrics! #product #analytics #founders

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Sometime over the past 15 years, your company has invested significant $$$ in data. Maybe the investment was in people, hiring folks with titles like data scientist, data engineer, machine learning engineer, or something equally as exotic. Maybe the investment was in tools like Databricks, Snowflake, Sagemaker, Fabric, or something else that promised to solve all your data problems. Lots of companies have done those things and seen varied success. Now we have GenAI and folks are foaming at the mouth to "implement AI" somewhere in their organization. Some companies are going to attack this opportunity in the same way they attacked data, and they will see the same results. Scenario 1: Hire some experts (new staff or consultants) with the goal of "implementing AI." Don't change anything else about the way you organize or function. Scenario 2: Assign your internal technical experts to "implement AI" without additional budget, training, or taking anything off their plates. Scenario 3: figure out how "implementing AI" integrates with your corporate goals and your existing processes and employee or customer experience. Assign the right people to solve what needs to be solved and hire as necessary (contractors or employees). Which scenario do you think will be most successful? Which scenario is how your company solves problems? Can you influence change?

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TGIF, folks! Fridays are hands down my favorite day—a perfect mix of weekend anticipation and a well-deserved breather from the weekly hustle (which I also love, haha). So here's the latest, I've finally caved and set up a Topmate profile! 😅 Why? Because so many of you folks have been reaching out for 1-1 help with: 1. Navigating the wild world of data science and analytics careers 2. Prepping for those nerve-wracking interviews I’ve decided to dedicate few hours of my weekend to offering personalized support for these requests. I've opened up a few 1-on-1 slots on my Topmate profile for those interested. If you're looking for guidance or interview prep, feel free to check it out and book a session! Looking forward to helping you on your data science journey. 😊 https://lnkd.in/gy-fMC6Q #CareerGuidance #DataScience #Analytics #InterviewPrep #TopmateProfile

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