Lindsay Betzendahl’s Post

I wrote a tutorial on how to create a [good] pie chart in #Tableau. https://lnkd.in/dPrYJYqn

This is just a practical use article about my experience between the two platforms. Which one do you guys like to use most? #tableau #dataanalytics #visualization

#1 𝗔𝗡𝗦𝗪𝗘𝗥 𝗢𝗡 𝗧𝗛𝗘 𝗕𝗢𝗔𝗥𝗗 😉 𝘉𝘛𝘞 𝘪𝘵'𝘴 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 𝘗𝘰𝘸𝘦𝘳𝘉𝘐, 𝘪𝘵'𝘴 𝘢𝘭𝘭 𝘧𝘰𝘳𝘮𝘴 𝘰𝘧 𝘉𝘐. Seriously though, this is the most often expressed symptom that we hear from our customers. And we've heard it - consistently - 𝘧𝘰𝘳 𝘺𝘦𝘢𝘳𝘴. It's not that they don't like their BI tooling -> They do. They use it for a great many different things. {That's why #PowerBI Tableau #Looker #Grafana Retool Sigma Computing etc have such large user bases.} It's just that BI tooling can't do everything they want it to do, and so they investigate #python Plotly #Dash (and Dash Enterprise) when they want more #interactivity #flexibility #customizability #scaleability #integratability for 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗱𝗮𝘁𝗮 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 such as those profiled per: plotly.com/user-stories BUT what then? BUT ... 'We need more than a "dashboard" 𝘧𝘰𝘳 𝘵𝘩𝘪𝘴 𝘱𝘢𝘳𝘵𝘪𝘤𝘶𝘭𝘢𝘳 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦' BUT ... 'We need more than a simple "data app"' Here they're needing a sophisticated analytics & viz focused application/tool/UI with capabilities (𝘸𝘩𝘪𝘤𝘩 𝘵𝘩𝘦𝘺'𝘳𝘦 𝘖𝘒 𝘵𝘰 𝘥𝘦𝘷𝘦𝘭𝘰𝘱 𝘪𝘯 𝘱𝘺𝘵𝘩𝘰𝘯) including some/all of: • editing/annotating/writeback • N-dimensional and/or dynamic cascading cross-filtering • customized branding (for externally facing discerning audiences) • integration with multiple data sources (in the same app) • interactive/scheduled/event-driven pixel-perfect reporting • RBAC, differential data access security controls, etc • working w un-structured data (e.g. imaging, scientific visualizations) • highly customized charts/maps This further extends to enabling dynamic calculations driven by the user of the application where they iteratively parameterize and run - from the UI (vs in a notebook / at code level) - multiple simulations or (ML) models on some backend compute (e.g. Databricks Snowflake #BigQuery Amazon Web Services (AWS) #Azure Google Cloud Platform etc) i.e. These are interactive, actionable, user interfaces Oh ... one more thing. They don't want to go hire a full stack dev team to do it. They want to do it themselves, and relatively quickly. So who are "they"? "They" are analytics types who know python (e.g. quants, computational biologists, data scientists, mechanical engineers, etc.) and are doing advanced analytics. ∴ Plotly COMPLEMENTS BI ... we don't replace BI.

I'm pleased to announce that I have completed #tableau work on the first part of the 'Time Series Analysis: Practical Methods for Business Dashboards' project. I recently presented on time series analysis at #02VizNaDvore RSTUG, and I want to express my gratitude to the organizers Egor Larin, Roman Bunin, Alex Barakov. The 'Time Series Analysis' project focuses on analyzing and visualizing time series data. With experience as both a BI developer and manager in various companies, as well as a scientist specializing in spectroscopy, I have gained insights from both user and developer perspectives. Continuously learning from the Data Community, I have accumulated a wealth of material on time series analysis that I am eager to share with the #datafam community, where I have grown and continue to learn. https://lnkd.in/erCADq_U In this project, I present techniques that I have utilized in business dashboards for over five years across different companies and teams. These tricks have proven effective for business users, and I have compiled them into one project, serving as a catalog of techniques to understand the technical implementation of various chart types and to demonstrate to clients what can be achieved and how it will work using examples. A total of 27 methods and features are described in the project, with computations for each method organized into folders for easier navigation. The main features: - Date Granularity - Category Granularity - Adaptive Line Thickness for Graphs - Floating Zero - Replacement of Empty Values on Graphs - Exclusion of Incomplete Cohorts - Switching Area Chart Types - Area & Dots - Variable Width Bars - Metric Switching - Metric Comparison - Displaying Values on Graphs - Displaying Measures of Central Tendency - Customizable Moving Average - Lookup Date Filter - Standard Deviation on Graphs - Confidence Interval - Period Comparison - Displaying Long-Running Events - Displaying Point Events - Highlighting Specific Dates The project is not yet complete, and in the second part, I will add regression, handling outliers, and other features. There may still be bugs or inaccuracies in the project, so feedback is warmly welcomed. I will continue to work on the project and gather methods and features for time series analysis.

When you are designing your reports. No matter what tool you use, Please read this article by Kurt Buhler This is probably the best article I have read in a long time. Also, the conclusion from the report which helps "organize your report in a useful way, so that users can find what they need" is a must for every developer so that users get what they need and answer the business problems that they want in the very first place !! https://lnkd.in/dq5WBChC #reportdesign #powerbi #qlik #tableau #sisense #looker #datastudio

#Unveiling the difference #Tableau: Server vs. Public - Finding Your Perfect #Data. Imagine you've meticulously crafted a stunning #visualization, a #masterpiece of #data #insights. Now you want to share it with the world! But where? #Tableau, the king of #datavisualization, offers two exciting platforms: #TableauServer and #TableauPublic. But which one is right for you? Fear not, fellow #data enthusiasts, because we're about to unveil the key differences to help you make an informed #choice. #TableauPublic: The Free Gallery for #Data #Storytellers Think of Tableau Public as your online #art exhibit. It's completely free and perfect for showcasing your #data masterpieces to the world. You can easily connect to #public #datasets, create #interactive #visualizations, and even embed them on #websites or #socialmedia. It's a fantastic platform to: 1. Build your #datavisualization #portfolio: Showcase your skills and creativity to potential #employers or #collaborators. 2. Spread #data awareness: Share #insights on important topics and spark conversations with a #global #audience. 3. #Test the #Tableau waters: Get a feel for Tableau's functionality before diving deeper. #TableauServer: The Secure Hub for #Collaborative #Insights Tableau Server is your private #data fortress. Imagine a #secure space where your team can #collaborate and access vital #data #visualizations. Here are some of its strengths: 1. #Enhanced #Security: Keep your sensitive #data behind a secure #firewall, perfect for internal #reports and #confidential #information. 2. #Seamless #Collaboration: Share #dashboards and #workbooks with colleagues, allowing for #realtime #data #discussions and #informed #decisionmaking. 3. #Scalability and #Governance: #Accommodate a large number of #users and control access levels, ensuring everyone sees the right #data. The Design Datascience Advantage Here's how Design Datascience swoops in! We understand the power of both #Tableau Public and Server. We can help you: 1. #Craft #compelling #narratives: Design data scientists can transform raw data into captivating stories using Tableau's powerful features. 2. #Optimize for your audience: Whether it's the general public on Tableau Public or internal stakeholders on Tableau Server, we ensure your #visualizations resonate with the right people. 3. #Navigate the technicalities: From #datawrangling to #server configuration, design #datascientists bridge the gap between design and technical expertise. Choosing the right #Tableau platform is like choosing the right outfit for the occasion. #Tableau Public lets you shine on the world stage, while #Tableau Server fosters secure collaboration within your team. With the guidance of a design data scientist, you can leverage the strengths of both platforms to unlock the true power of your data story! #dataanalysis #datavisualization #businessintelligence #tableau #datascience #datadriven #dataanalyst ##datavisuals #startup #odisha #india #students

🚀 #Streamlit vs. #PowerBI vs. #Superset vs. #Dash vs. #Metabase vs. #LookerStudio vs. #QlikView vs. #Tableau: Which tool is best for enterprise projects? Choosing the right tool for data analytics dashboards and machine learning integration is challenging. Our recent project raised the question: Which tool delivers the best performance for large-scale projects? Here’s a quick breakdown of strengths and weaknesses: 1️⃣ Streamlit: 💡 Strengths: Fast prototyping, ideal for small projects, easy ML model deployment. 🔻 Weaknesses: Struggles with large datasets, high memory usage, limited UI flexibility. 2️⃣ Power BI: 💡 Strengths: Powerful BI tool, easy to learn, integrates well with multiple data sources. 🔻 Weaknesses: Limited interactivity, not ideal for ML integrations. 3️⃣ Apache Superset: 💡 Strengths: Flexible, scalable for enterprise projects, great for SQL databases and complex queries. 🔻 Weaknesses: Limited NoSQL support, complex configuration. 4️⃣ Dash (Plotly): 💡 Strengths: Excellent for interactive web apps, great for ML and data visualizations. 🔻 Weaknesses: Requires strong programming skills. 5️⃣ Metabase: 💡 Strengths: User-friendly, easy setup, SQL support. 🔻 Weaknesses: Limited scalability for complex queries. 6️⃣ Google Looker Studio: 💡 Strengths: Integrates well with Google services, good for quick visualizations. 🔻 Weaknesses: Limited for large-scale or complex projects. 7️⃣ QlikView: 💡 Strengths: Fast in-memory processing, great for associative data analysis. 🔻 Weaknesses: Steep learning curve, complex management for large setups. 8️⃣ Tableau: 💡 Strengths: Excellent visualizations, handles large datasets with ease. 🔻 Weaknesses: Expensive for larger teams, advanced analytics often require external tools. 🔍 Question: Which tool would you choose for an enterprise dashboard with machine learning capabilities? Any other recommendations? Let’s discuss! 👇 #BI #MachineLearning #DataScience #Streamlit #PowerBI #Superset #Dash #Metabase #LookerStudio #QlikView #Tableau #DataVisualization #Python

Which tool delivers the best performance for large-scale projects? #BI #MachineLearning #DataScience #Streamlit #PowerBI #Superset #Dash #Metabase #LookerStudio #QlikView #Tableau #DataVisualization #Python

Here are some use cases where the ASCII function can be effectively utilized in #Tableau. 1. Data Cleaning and Validation 2. Categorizing Data 3. Sorting and Ordering 4. Creating Unique Keys 5. Frequency Analysis

#Tableau #Server2023: A Closer Look at Latest Advancements! #Tableau #TableauConsultants #TableauConsultantsCompany #TableauDevelopment #TableauDevelopmentCompany #TableauConsultantsCompanyIndia