Data Analysis Techniques in R

Many amazing presenters fall into the trap of believing their data will speak for itself. But it never does… Our brains aren't spreadsheets, they're story processors. You may understand the importance of your data, but don't assume others do too. The truth is, data alone doesn't persuade…but the impact it has on your audience's lives does. Your job is to tell that story in your presentation. Here are a few steps to help transform your data into a story: 1. Formulate your Data Point of View. Your "DataPOV" is the big idea that all your data supports. It's not a finding; it's a clear recommendation based on what the data is telling you. Instead of "Our turnover rate increased 15% this quarter," your DataPOV might be "We need to invest $200K in management training because exit interviews show poor leadership is causing $1.2M in turnover costs." This becomes the north star for every slide, chart, and talking point. 2. Turn your DataPOV into a narrative arc. Build a complete story structure that moves from "what is" to "what could be." Open with current reality (supported by your data), build tension by showing what's at stake if nothing changes, then resolve with your recommended action. Every data point should advance this narrative, not just exist as isolated information. 3. Know your audience's decision-making role. Tailor your story based on whether your audience is a decision-maker, influencer, or implementer. Executives want clear implications and next steps. Match your storytelling pattern to their role and what you need from them. 4. Humanize your data. Behind every data point is a person with hopes, challenges, and aspirations. Instead of saying "60% of users requested this feature," share how specific individuals are struggling without it. The difference between being heard and being remembered comes down to this simple shift from stats to stories. Next time you're preparing to present data, ask yourself: "Is this just a data dump, or am I guiding my audience toward a new way of thinking?" #DataStorytelling #LeadershipCommunication #CommunicationSkills

Analytical results go unused way too often! Here is how you can ensure that they don't settle dust: 1. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘆𝗼𝘂𝗿 𝘀𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗻𝗲𝗲𝗱𝘀 by asking them about their goals, challenges, and what decisions they hope to make with your data.     2. 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝘆𝗼𝘂𝗿 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 to avoid overwhelming your stakeholders with technical jargon and complex statistics.     3. 𝗣𝗿𝗼𝘃𝗶𝗱𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 by showing how your analysis or models impact the business and support decision-making.     4. 𝗖𝗿𝗲𝗮𝘁𝗲 𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 by clearly outlining the steps stakeholders can take based on your findings.     5. 𝗘𝗻𝗴𝗮𝗴𝗲 𝗮𝗳𝘁𝗲𝗿 𝘆𝗼𝘂𝗿 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 by scheduling follow-up meetings to discuss implementation and address any questions or concerns.     6. 𝗕𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝘀𝘁 𝗮𝗻𝗱 𝗰𝗿𝗲𝗱𝗶𝗯𝗶𝗹𝗶𝘁𝘆 by continuously delivering reliable and robust results to make stakeholders more likely to use your insights. What are your tips to ensure the results get used by your stakeholders? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field #dataanalytics #datascience #stakeholdermanagement #datadriven #careergrowth

Communicating complex data insights to stakeholders who may not have a technical background is crucial for the success of any data science project. Here are some personal tips that I've learned over the years while working in consulting: 1. Know Your Audience: Understand who your audience is and what they care about. Tailor your presentation to address their specific concerns and interests. Use language and examples that are relevant and easily understandable to them. 2. Simplify the Message: Distill your findings into clear, concise messages. Avoid jargon and technical terms that may confuse your audience. Focus on the key insights and their implications rather than the intricate details of your analysis. 3. Use Visuals Wisely: Leverage charts, graphs, and infographics to convey your data visually. Visuals can help illustrate trends and patterns more effectively than numbers alone. Ensure your visuals are simple, clean, and directly support your key points. 4. Tell a Story: Frame your data within a narrative that guides your audience through the insights. Start with the problem, present your analysis, and conclude with actionable recommendations. Storytelling helps make the data more relatable and memorable. 5. Highlight the Impact: Explain the real-world impact of your findings. How do they affect the business or the problem at hand? Stakeholders are more likely to engage with your presentation if they understand the tangible benefits of your insights. 6. Practice Active Listening: Encourage questions and feedback from your audience. Listen actively and be prepared to explain or reframe your points as needed. This shows respect for their perspective and helps ensure they fully grasp your message. Share your tips or experiences in presenting data science projects in the comments below! Let’s learn from each other. 🌟 #DataScience #PresentationSkills #EffectiveCommunication #TechToNonTech #StakeholderEngagement #DataVisualization

There's nothing more painful than watching a data scientist stumble through a presentation without a framework. They dump data, show too many charts, forget to make a recommendation - and wonder why nothing happens. What they're missing is a proven structure that actually persuades. Here's the battle-tested structure that data scientist Russell E. Walker, PhD taught me from his experiences in competitive debate, that transforms technical presentations into persuasive business cases: 1. 𝗛𝗔𝗥𝗠 - 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺? ✴️ Don't just state facts - frame the problem in terms your audience cares about For example: ✴️ For a medical audience: "Patient hospitalizations increased 20%"   ✴️ For a finance audience: "Hospitalization costs increased 20%" Same data, different framing 2. 𝗦𝗜𝗚𝗡𝗜𝗙𝗜𝗖𝗔𝗡𝗖𝗘 - 𝗛𝗼𝘄 𝗯𝗶𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗶𝗺𝗽𝗮𝗰𝘁? ✴️ Quantify the harm in dollars, time, or other metrics that matter  ✴️ Put it in context (e.g. "This represents 15% of our annual profit") ✴️ Make it material to business goals 3. 𝗜𝗡𝗛𝗘𝗥𝗘𝗡𝗖𝗬 - 𝗪𝗵𝘆 𝘄𝗼𝗻'𝘁 𝘁𝗵𝗶𝘀 𝗳𝗶𝘅 𝗶𝘁𝘀𝗲𝗹𝗳? ✴️ Identify the root cause  ✴️ Show the problem is systemic, not temporary   ✴️ Prove intervention is necessary (e.g. "This trend has continued for 18 months despite normal business cycles"). 4. 𝗦𝗢𝗟𝗩𝗘𝗡𝗖𝗬 - 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘀𝗼𝗹𝘃𝗲 𝗶𝘁? ✴️ Present your plan or recommendation  ✴️ Connect the dots: show exactly how your solution addresses the root cause  ✴️ Loop back to the original harm (e.g. "This will reduce hospitalizations by X%, saving $Y annually") This works because you're taking your audience on a logical journey from problem to solution - each step builds on the previous one. And it works for any data science presentation - whether you're presenting a model, recommending process changes, or requesting resources. Try this structure in your next presentation. Start with the business problem your audience cares about, not with your methodology. Stop watching your brilliant insights get ignored because of poor presentation structure. How do you currently structure your data science presentations? #datascience #business #career --- 👋 If you enjoyed this, you'll enjoy my newsletter. Twice weekly, I share insights to help data scientists get noticed, promoted and valued. Click "Visit my website" under my name to join.

📊 𝗗𝗮𝘁𝗮! 𝗗𝗮𝘁𝗮! 𝗗𝗮𝘁𝗮! 𝗗𝗮𝘁𝗮! We all know data is king in this industry. It is the judge, jury and executioner of any company. And although all #data generated are important, #toxicology data is, well, particularly sensitive. Or a more accurate term might be scary. When we get to any tox studies in the pipeline of #drugdevelopment, without a doubt there is always confusion and fear accompanying the data generated by those studies. At the first sign of a tox finding, choas ensues because many think the #drug is dead. But in reality, tox studies require #toxicity - otherwise they are useless. 👉 So how do we make sense of these data without our fears going haywire? We sort them. We simplify them. We arrange and present. We create a story to tell from them. 🐁 For example, say we are running a 28d #GLP toxicology study in the rat. We dose the rats by #oralgavage everyday for 28 days and then take them down. Within those 28 days, we collect a ton of data including #bodyweights, #toxicokinetics and #histopathology (to name a few). Now, we have all these data with no home. Let’s make them one. 1️⃣ First, we sort the data. We sort the bodyweight data by week, the toxicokinetic data by timepoint and the histopath data by dose group. 2️⃣ Then, we arrange them. We arrange the bodyweight data in chronological order to reveal bodyweight loss, we arrange the TK data to reveal AUC/Cmax, and we arrange the histopath data to reveal incidence of findings in each dose group. 3️⃣ Next, we present them visually. We present the bodyweight data to see a trend over time from Day 1 to Day 28, we present the TK data to compare AUC on Day 1 versus Day 28, and we present the histopath data to show correlations to other pathology findings (eg, clinical pathology). 4️⃣ Finally, we explain all these data. Our interpretations and analysis of the data allow us to determine adverse effects and establish a NOAEL for the study, summarized in the study report and told as a story through regulatory submissions. 🏡 Our data now has a home, it has a place in our story as a whole and, if done right, can relay to the audience the important takeaways of the molecule through understanding rather than confusion or fear. 🌎 In the end, data is the wheel that turns our world. The smoother we can make that wheel, the more efficient we can be in our missions. #pharmaindustry #data #pathology #NOAEL #regulatory

Imagine you've performed an in-depth analysis and uncovered an incredible insight. You’re now excited to share your findings with an influential group of stakeholders. You’ve been meticulous, eliminating biases, double-checking your logic, and ensuring your conclusions are sound. But even with all this diligence, there’s one common pitfall that could diminish the impact of your insights: information overload. In our excitement, we sometimes flood stakeholders with excessive details, dense reports, cluttered dashboards, and long presentations filled with too much information. The result is confusion, disengagement, and inaction. Insights are not our children, we don’t have to love them equally. To truly drive action, we must isolate and emphasize the insights that matter most—those that directly address the problem statement and have the highest impact. Here’s how to present insights effectively to ensure clarity, engagement, and action: ✅ Start with the Problem – Frame your insights around the problem statement. If stakeholders don’t see the relevance, they won’t care about the data. ✅ Prioritize Key Insights – Not all insights are created equal. Share only the most impactful findings that directly influence decision-making. ✅ Tell a Story, Not Just Show Data– Structure your presentation as a narrative: What was the challenge? What did the data reveal? What should be done next? A well-crafted story is more memorable than a raw data dump. ✅ Use Clean, Intuitive Visuals – Data-heavy slides and cluttered dashboards overwhelm stakeholders. Use simple, insightful charts that highlight key takeaways at a glance. ✅ Make Your Recommendations Clear– Insights without action are meaningless. End with specific, actionable recommendations to guide decision-making. ✅ Encourage Dialogue, Not Just Presentation – Effective communication is a two-way street. Invite questions and discussions to ensure buy-in from stakeholders. ✅ Less is More– Sometimes, one well-presented insight can be more powerful than ten slides of analysis. Keep it concise, impactful, and decision-focused. Before presenting, ask yourself: Am I providing clarity or creating confusion? The best insights don’t just inform—they inspire action. What strategies do you use to make your insights more actionable? Let’s discuss! P.S: I've shared a dashboard I reviewed recently, and thought it was overloaded and not actionably created

📊💡 Mastering Data Visualization: Tips for Clear and Compelling Presentation In today's data-driven world, effective data visualization is key to conveying insights and driving decision-making. As data analysts, we understand the power of information. But presenting that data in a way that is not only clear but also compelling is an art form in itself. Here are some tips and best practices for mastering data visualization: 1. **Know Your Audience**: Before diving into visualization, understand who you're presenting to and what they care about. Tailor your visualizations to their level of expertise and interests. 2. **Simplify Complex Data**: Complexity can overwhelm and obscure your message. Simplify your visualizations by focusing on the most important insights. 3. **Choose the Right Visualization Type**: Different types of data lend themselves to different visualization formats. Choose the visualization type that best conveys your message and makes it easy for your audience to understand. 4. **Emphasize Key Insights**: Use visual cues to draw attention to the most important insights in your data. 5. **Tell a Story with Your Data**: Structure your visualizations in a logical sequence that leads your audience from problem to insight to action. 6. **Iterate and Solicit Feedback**: Data visualization is an iterative process. Continuous refinement based on feedback will help you create more effective and impactful visualizations over time. Tools such as Tableau, Power BI, and Python libraries like Matplotlib and Seaborn can be incredibly useful in creating visually stunning and informative visualizations. The real magic happens when you combine technical expertise with a keen eye for design and storytelling. Let's continue to harness the power of data visualization to unlock insights, tell compelling stories, and drive decision-making in our organizations. 🚀💻 #datavisualization #analytics

Without fail, every one of my science and industry presentations includes these 5 components. They aren’t rocket science, but they make a big difference. 1️⃣ An outline. People need to know what direction you’re taking with the presentation. The outline should be built using no more than 5 key topics and maximum 2 to 3 sub points. Continue to refer to the outline throughout the presentation so people know where they are. No one ever walked away from a presentation and said “Dang, that flowed too well.” 2️⃣ Great graphics. If there’s a concept or summary point that can be put into a graphic instead of words, you better believe I’m building it. Having this graphic draws the eye, improving engagement while also allowing me to be flexible in how I summarize the concept. 3️⃣ Bullet points. You’re probably like “Ok, no duh Bethany.” But hear me out - we need to be more critical about what a bullet point actually is. It should be a blunt highlight of key content, easy to read and digest. The bullet point shouldn’t be a complete paragraph or even a full sentence. As Kevin from The Office says “Why waste time say lot word when few word do trick?" 4️⃣ Data - but limited! I think we can overuse figures and tables from research papers, especially if they don’t stand alone well on a slide. Any data that is being shared needs to be quickly described for reference points (“This is what the x and y axis represent.”) and what the audience should take from it (“I want to draw your attention to these values here because they make my point.”). If explaining these two portions is challenging or takes too long while you practice, they shouldn’t be in the presentation. 5️⃣ Summary slide. This will looks slightly like the outline but will cover more “big picture” final thoughts. When my audience walks away from my presentation, I want them thinking about the 3 to 5 main concepts I shared in the summary. What did I miss, my fellow communicators? Share your thoughts below! #presentations #communication #science #sciencecommunication #agcommunication #speaker #speaking #publicspeaking

Ever watched eyes glaze over during your data presentation? All that beautiful math—missed the real question: "What should we do next?" I learned the hard way: translating analysis into action is everything. Curious how? Read on. I used to spend most of my meeting time explaining statistical methods, only to realize my audience just wanted a clear recommendation. Once, after 45 minutes on hierarchical models, a scientist asked, "So, should we move this compound forward or not?" I hadn't even calculated that probability. Lab scientists are experts in their own right—they need actionable insights, not a stats seminar. Now, I always start with the decision and probability, then offer details if asked. Trust and engagement have skyrocketed. Your communication budget is finite; spend it on what matters. Lead with the decision, not the methods. Use BLUF (Bottom-Line Up-Front): start with your recommendation and the probability behind it. Lab scientists operate in different modes: decision, learning, or validation. Tailor your approach to their needs—don't default to teaching when they're in decision mode. Translate your analysis through three layers: statistical reality (for you), scientific meaning (the bridge), and decision layer (for them). Only collapse to a single probability when it's time to make a decision. Build trust by being clear and actionable, not by over-explaining. Keep technical details in an appendix—share them only if asked. Anticipate the questions your collaborators always ask. Proactively address their concerns to build credibility and save time. If you've ever struggled to get your analysis heard, check out my latest post for practical frameworks and real-world examples. Would love your thoughts, likes, or shares! Full post here: https://lnkd.in/ex6-q4Hd What strategies have helped you bridge the gap between data and decision-making in your collaborations? #biotech #datascience #communication #leadership #decisionmaking

Figures help communicate your research findings better. But they must be designed with clarity and integrity to avoid misinterpretation. Here are some key principles: ✅ 1. Figures aren't just for duplicating what's in tables or text—they're a powerful tool for highlighting visually compelling insights. In any manuscript, results can be presented in four places: the main text, tables, figures, or online supplemental materials. 👁️🗨️ 2. Figures Should Stand Alone With many journals now displaying figures independently online, it's important that a reader can understand the figure without having to consult the full manuscript. Include a descriptive title with key elements: person, place, and time. Add clear footnotes to define terms, measures, or abbreviations used. 📏 3. Use Scales Appropriately For percentages, your Y-axis should run from 0 to 100. If the data points are small and you need to truncate the axis, indicate this with two slashes (//) to show that the full range is not depicted. 🎨 4. Design for Black and White Assume your figure may be printed in grayscale. Use color AND patterns (e.g., hatching, stripes, dots) to differentiate data points clearly—ensuring your visualization is effective in both color and monochrome formats. 📉 5. Less Is More Avoid squeezing too much into one figure. If you need to show results for multiple demographic breakdowns, it’s better suited for a table, not a figure. Use figures, for example, when you’re presenting: Overall estimates for multiple outcomes , or Stratified estimates for one or two outcomes by a key demographic (e.g., education). 🧾 6. Always Include a Legend If your figure includes multiple outcomes or variables, include a legend. If it shows just one single outcome, make sure that outcome is clearly stated in the title. 🧭 7. Label Your Axes Clearly Both X and Y axes must be labeled with units, where applicable. This helps orient your audience. 📌 Pro tip: When presenting a figure live, begin by walking your audience through the axes: “This figure shows X. The horizontal axis represents [variable], and the vertical axis represents [variable]...” Give them a moment to get oriented before diving into the interpretation. 🧹 8. Minimize Clutter Avoid gridlines—they make your figure look messy. Only label bars or data points when essential, especially if space is tight. 🖼️ 9. Submit High-Resolution Figures Minimum resolution: 300 DPI (dots per inch). If using Excel: paste your chart into PowerPoint, save the slide as a PDF, then convert that PDF to an image at 300 DPI using tools like IrfanView (https://www.irfanview.com/). ✍️ 10. Use Consistent Footnote Symbols Use a recurring set of symbols in this order: *, †, ‡, § Then repeat with double marks: **, ††, etc. Alternatively, use superscript letters (a–z) or numbers. Keep it clean and consistent. By following these principles, you ensure your results are clear, credible, and impactful—getting the attention they deserve.