APPIAH OFOSU PATRICK’s Post

🚀 I wrote a blog to discuss case studies in data visualization. 🔥 This is the 4th article in my weekly updating newsletter series, and there's more to come. Stay tuned if you're enjoying it! We've come a long way in our journey to make data charts not just informative, but intuitive. From telling compelling stories with data, using the right blend of colors, to simplifying complex visuals – our mission has been to reduce the effort it takes to grasp complex data. 👉 In this new article, I dive deeper with practical examples to keep the insight straightforward and save our audience the hassle of crunching numbers themselves. 🔗 https://lnkd.in/g-euaFQy p.s. I welcome any suggestions and comments! Please feel free to leave a message or send me a direct message. I look forward to hearing from you! #DataVisualization #BusinessIntelligence #DataScience #Analytics

𝐓𝐚𝐩𝐩𝐢𝐧𝐠 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗮𝘁𝗮 𝘄𝗶𝘁𝗵 𝗮 𝗦𝘁𝗿𝗼𝗻𝗴 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 📊 Great business decisions are rooted in data. But how do you ensure you’re getting the most out of your data? A well-defined business analytics lifecycle can help you transform raw data into actionable insights. High-level overview of the six key stages: 1.Data Acquisition 📥 2.Data Cleaning & Preparation 🧹 3.Exploratory Data Analysis (EDA) 🔍 4.Modeling & Forecasting 📈 5.Communication & Visualization 📊 6.Decision Making & Action 🚀 By following these stages, you can ensure your data is accurate, usable, and drives real business value. Visit Us: https://bit.ly/47qtblF #businessanalytics #dataanalytics #datascience #bigdata #datavisualization #businessintelligence #artificialintelligence #machinelearning #datascientist #dataanalyst

🤔 If you are new to analytics and data visualization, you may not be aware of "Pre-attentive Attributes".   📉 Reminder: I have a dashboard for that!   🧠 Hopefully, it makes sense. Link in comments.   📃 In the workbook, the dashboards/tabs are organized by data types: quantitative, ordinal, and categorical.   🔼 The attributes are listed with most effective at the top (which ironically is the position attribute itself).   👍 Hope that helps.   🏗→🧠 Build to Learn!   💭🚶♀️🚶♂️ Follow for more.   #data #datascience #analytics #VizoftheRay

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🔍 Ever been buried under a mountain of data with no clear insights? It’s like having a treasure map without the “X marks the spot”! 🌟 Data overload can be overwhelming and frustrating. All those numbers and stats mean nothing if you can’t extract clear, actionable insights from them. 🛠️ But don’t worry, turning data into compelling stories is my specialty. By learning how to tell stories with your data, you can uncover the hidden gems and make informed decisions that drive success. 💬 Got any examples of data stories you’ve crafted or come across? I’d love to hear them! Let’s share and learn together. 🚀 #DataAnalysis #DataStorytelling #ActionableInsights #DataDrivenDecisions #DataVisualization #BusinessIntelligence #Analytics #BigData #DataScience #DataAnalytics #DataStories #DataStrategy #Insights #BusinessAnalytics #DataMining

Uncover Insights: Types of Data Visualizations and Their Uses 📊🔍 Happy Wednesday, everyone! 👋 Continuing our Data Visualization tips, today we’re exploring different types of data visualizations and how to use them. Whether you're a newbie or a data pro, this guide will help you choose the right chart for the right insight. Let’s dive in! 🚀 1. Bar Charts 📊 Use for: Comparing categories like sales across regions or responses by age group. Bar charts clearly show differences side by side. 2. Line Charts 📈 Use for: Tracking trends over time, such as monthly sales or website traffic. They highlight patterns and future trends. 3. Pie Charts 🥧 Use for: Showing proportions within a whole, like market share or budget distribution. Great for visualizing part-to-whole relationships. 4. Scatter Plots 🌌 Use for: Exploring relationships between variables, like height vs. weight. They reveal correlations, clusters, and outliers. 5. Heatmaps 🔥 Use for: Visualizing data density and intensity, such as in geographic data or website clicks. Heatmaps highlight hotspots with color gradients. 6. Histograms 📏 Use for: Analyzing distributions of data, like income levels or test scores. They show frequency of different values and highlight outliers. 7. Tree Maps 🌳 Use for: Displaying hierarchical data and part-to-whole relationships, such as file sizes or market breakdowns, in nested rectangles. 8. Sankey Diagrams 💧 Use for: Illustrating flows and connections, like energy transfers or financial flows, showing how resources move through a system. Choosing the right visualization helps reveal insights and effectively communicate your data story. 📖💡 What’s your go-to visualization? Share your thoughts below! 👇 Stay tuned for next week’s tips on choosing the best color schemes! 🎨 #DataVisualization #DataViz #DataAnalysis #PowerBI #Analytics #DataScience #WednesdayTips

𝗖𝗮𝗻 𝗮 𝗦𝗶𝗺𝗽𝗹𝗲 𝗖𝗵𝗮𝗿𝘁 𝗠𝗮𝗸𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗠𝗼𝗿𝗲 𝗜𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹? 𝗛𝗲𝗿𝗲’𝘀 𝗛𝗼𝘄! 𝗧𝗵𝗶𝗻𝗸 𝗰𝗵𝗮𝗿𝘁𝘀 𝗮𝗿𝗲 𝗷𝘂𝘀𝘁 𝗳𝗼𝗿 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝗱𝗮𝘁𝗮? 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 𝗵𝗼𝘄 𝗰𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗰𝗵𝗮𝗿𝘁 𝗰𝗮𝗻 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝘆𝗼𝘂𝗿 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀! Hi everyone! When it comes to data analysis, choosing the right chart can make a huge difference in how your data is understood. I used to think any chart would do, but then I realized that the right choice could make my insights clearer and more compelling. 𝙃𝙚𝙧𝙚’𝙨 𝙝𝙤𝙬 𝙨𝙚𝙡𝙚𝙘𝙩𝙞𝙣𝙜 𝙩𝙝𝙚 𝙧𝙞𝙜𝙝𝙩 𝙘𝙝𝙖𝙧𝙩 𝙘𝙖𝙣 𝙚𝙣𝙝𝙖𝙣𝙘𝙚 𝙮𝙤𝙪𝙧 𝙖𝙣𝙖𝙡𝙮𝙨𝙞𝙨: - 𝗟𝗶𝗻𝗲 𝗖𝗵𝗮𝗿𝘁𝘀 𝗳𝗼𝗿 𝗧𝗿𝗲𝗻𝗱𝘀: Use line charts to show trends over time, which helps in visualizing how data changes. - 𝗕𝗮𝗿 𝗖𝗵𝗮𝗿𝘁𝘀 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻𝘀: Bar charts are great for comparing different categories or groups, making it easy to see which is larger or smaller. - 𝗣𝗶𝗲 𝗖𝗵𝗮𝗿𝘁𝘀 𝗳𝗼𝗿 𝗣𝗿𝗼𝗽𝗼𝗿𝘁𝗶𝗼𝗻𝘀: Pie charts can help show how different parts contribute to a whole, useful for percentage-based data. - 𝗛𝗲𝗮𝘁 𝗠𝗮𝗽𝘀 𝗳𝗼𝗿 𝗗𝗲𝗻𝘀𝗶𝘁𝘆: Heat maps are excellent for showing the density of data points and identifying patterns quickly. Picking the right chart not only makes your data more understandable but also more engaging for your audience. What’s your favorite chart type and why? Do you have any charting tips to share? 🚀 Follow Simala Om Prakash for more practical tips and tricks on data analytics! #DataVisualization #Charts #DataAnalytics #EffectiveAnalysis

What’s Your Biggest Data Visualization Challenge? Data visualization is key to unlocking insights from complex data, but it comes with its own set of challenges. What’s the biggest hurdle you face when working with intricate data sets? Is it choosing the right visualizations, integrating diverse data sources, or something else entirely? 🔍 Share Your Insights: I’d love to hear your thoughts and experiences. Drop a comment below and let’s learn from each other’s challenges and solutions! 🔄 Spread the Knowledge: Tag a colleague or peer who might have valuable insights to share. 👉 For more discussions and updates on data analytics, follow me Padmalochan Sahu #DataAnalytics #DataVisualization #DataScience #BigData #Analytics #BusinessIntelligence #MachineLearning #DataDriven #DataInsights #Visualization #DataScienceCommunity

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📊 Just wrapped up a fantastic data visualization task! 🚀 I had the opportunity to create a bar chart/histogram to showcase the distribution of a variable - whether it's ages or genders in a population. 📈 Visualizing data has never been this exciting! Transforming raw numbers into a clear, easy-to-understand chart brings a whole new level of insights. 🧠💡 It's amazing how a simple graph can tell a compelling story about the patterns and trends in the data. Thrilled to have honed my data visualization skills and added another valuable tool to my analytics toolkit. #DataVisualization #Analytics #GraphsAndCharts #DataAnalysis #NewSkills #CareerDevelopment