Emmanuel Olutayo Akinlabi

In the world of data, analysis is part of everyday life. With R, you can easily load datasets, clean them, run simple stats, and create clear visualisations. Check out my example here. Let's make data work for us.

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Student t-test Isn't Always the Answer! The Student t-test is a classic statistical tool, but it’s not always the right choice. ❌ In this video, I dive into the limitations of the t-test and show you how to use JMP to analyze data when the assumptions don’t hold up. 📈 Using JMP, I showcase how to identify violations of the t-test assumptions and what to do when the assumptions fail. 📈 Go to my profile and ring the 🔔 to get regular tips on statistics and data analysis! Let’s explore the power of data analysis together! 🚀 #JMP #StatsLikeJazz

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Standard Deviation (SD) and Standard Error (SE) are essential statistical concepts that statisticians use daily. Thanks to Eric Cai for explaining them in a clear and simple way.

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Thrilled to share that I've leveled up my data science game with a comprehensive Udemy course on R programming! 🔍💻 Excited to blend Python proficiency with R prowess, expanding my toolkit for insightful research and analysis. Let's unlock even more data-driven discoveries together! #DataScience #RProgramming #Python #ContinuousLearning #HealthDatascience, #PublicHealth

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Standard deviation is a statistic measuring the dispersion of a dataset relative to its mean. It is calculated as the square root of the variance. Learn how it's used.

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This is my post #3 in my R learning initiative, providing insightful reference to master the skill. Let's talk about Data Visualisation in R. Data visualization is a powerful tool in R programming, allowing you to create meaningful and insightful visual representations of your data. The ggplot2 package, part of the tidyverse, is particularly popular for creating high-quality and customizable graphics. The ggplot2 system is built around the Grammar of Graphics, which provides a coherent and flexible framework for building plots. A basic ggplot2 plot consists of three main components: Data: The dataset being visualized. Aesthetics (aes): The mappings of data variables to visual properties (e.g., x-axis, y-axis, color). Geometries (geom): The geometric shapes that represent the data points (e.g., points, lines, bars). Refer to https://r-graphics.org/ , R Graphics Cookbook by Winston Chang, a guide that provides more than 150 recipes to help you generate high-quality graphs.

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I’m so excited to dive into the new book by my former PhD supervisor - the brilliant Francis Nyamnjoh! Our understandings of emerging technologies, like AI, is already deeply influenced by our imaginations and visions of the future. I believe that allowing academic insights and creative story-telling perspectives to blend can offer a much richer understanding about the role these technologies play in our lives. Cannot wait to explore the ideas in this book!

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✴️ Day 3/75 of My Data Science Challenge: Mastering Pivot Tables for Data Summarization! 📊🚀 Today, I dove into one of the most powerful features in Excel: Pivot Tables! They are my go-to tool for summarizing, analyzing, and making sense of large datasets with just a few clicks. 💻 What I Learned: 💡 Pivot Tables allow you to: Quickly summarize data from raw datasets. Slice and dice data from multiple angles. Easily calculate metrics like totals, averages, and percentages. Spot trends and patterns in the data, all without complex formulas! Why Pivot Tables Matter in Data Science: They are great for data exploration and finding key insights quickly. A must-have skill for anyone working with data, from beginners to pros. You can create customizable reports and visualize data, making it easy to present findings. Example: I used a dataset on employee performance to create a summary of total sales by department, analyzed monthly trends, and identified top performers with just a few clicks! 🙌 💡 Pro Tip: Experiment with filters, columns, and rows to view your data from different perspectives— it can reveal insights you didn’t know were there! What’s Next: Excited to keep building my skills with more Excel tricks and data science tools over the next few days. Stay tuned! #exceltips #EntriEntri Elevate #DrDr.Jitha P Nair #75DaysOfDataAnalysis #DataScienceJourney

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📉 Low GPA but Dreaming of a PhD? Here’s How You Can Make It Happen!🎓✨ If you’re wondering, “Can I still get into a PhD program with a low GPA?” the answer is a resounding YES! While your GPA does matter, it’s not the whole story. Here’s how you can strengthen your application and stand out: 1️⃣ Highlight Your Strengths 🌟 Showcase your research experience, completed projects, or publications. Emphasize how your skills and expertise align with the professor’s or program’s goals. 💡 Example: Worked on an innovative project or gained lab experience? Mention it in your application—it shows you’re ready to contribute! 2️⃣ Nail Your Statement of Purpose (SoP) 📝 - Your SoP is your chance to make your case. Use it to demonstrate your passion and resilience. - If your GPA improved over time, explain how you grew and overcame challenges. 💡 Pro Tip: Don’t just talk about your struggles—show how you’ve used them as a stepping stone to success! 3️⃣ Build Genuine Connections 🤝 - Reach out to professors with research interests that align with yours. A meaningful connection can open doors that GPA alone cannot. - Networking could uncover hidden opportunities or unadvertised PhD positions. 💡 Pro Tip: A well-crafted email expressing genuine interest in their work can make a huge difference! 4️⃣ Leverage Strong Recommendations 📬 - A glowing recommendation from someone who knows your work ethic and potential can offset a lower GPA. - Ask professors or mentors who can highlight your unique strengths and problem-solving skills. 5️⃣ Excel in Standardized Tests 🧠 - GRE/GMAT scores can sometimes carry more weight than your GPA. A strong performance can tip the scales in your favor. - Focus on the sections most relevant to your field to showcase your academic aptitude. 6️⃣ Choose the Right Programs 🎯 - Look for universities or programs that value research potential, real-world experience, and skill alignment over GPA. - Many institutions in Europe and Canada prioritize interviews, proposals, and fit with their research culture. 💡 Final Words: A low GPA is not a dealbreaker! Focus on your strengths, tell a compelling story in your application, and demonstrate your readiness to excel in a PhD program. It’s about the whole package—skills, passion, resilience, and potential. 🚀 Your GPA doesn’t define your future. Keep pushing forward and chasing your academic dreams! Have any specific concerns or tips to share? Let’s discuss in the comments below! 👇 For continuous guidance, join my LinkedIn community https://lnkd.in/drgFj7rN and our Telegram group https://lnkd.in/dvPhwsme or search for: NextGenResearch 💡 Remember: Think, Believe, and Achieve! #PhDApplications #LowGPA #DreamBig #ResearchPotential #AcademicSuccess #StudyAbroad #GraduateStudies #NextGenResearcher

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Thanks for sharing your thoughts on this, well detailed and straight to the point.

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I created a #DataVisualization tutorial using tidygraph and ggraph libraries in R to create and customize network visualizations. Social connections, communication networks, and biological pathways can intuitively be represented as networks. And network visualization is a very useful exploratory tool for summarizing and presenting data before quantitative models are applied. Utilizing Twitter data, I mapped ~160000 follower-followee connections among 4,000+ US state legislators, uncovering the structure of relationships, communities in it, and other interesting properties of this digital network of policymakers! Check it out here: https://lnkd.in/eu5Vv7T9 #DataVisualization #NetworkAnalysis #NetworkScience #RProgramming

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𝕋𝕙𝕖 𝕀𝕞𝕡𝕠𝕣𝕥𝕒𝕟𝕔𝕖 𝕠𝕗 𝕊𝕥𝕒𝕥𝕚𝕤𝕥𝕚𝕔𝕤 𝕗𝕠𝕣 𝔻𝕒𝕥𝕒 𝕊𝕔𝕚𝕖𝕟𝕥𝕚𝕤𝕥𝕤 In the field of data science, statistics emerge as a cornerstone, providing the framework and tools necessary for extracting meaningful insights from data. Statistics is not just a subset of mathematics; for data scientists, it is a key enabler of their craft. 𝕌𝕟𝕕𝕖𝕣𝕤𝕥𝕒𝕟𝕕𝕚𝕟𝕘 𝕥𝕙𝕖 𝔻𝕒𝕥𝕒 𝕋𝕙𝕣𝕠𝕦𝕘𝕙 𝔻𝕖𝕤𝕔𝕣𝕚𝕡𝕥𝕚𝕧𝕖 𝕊𝕥𝕒𝕥𝕚𝕤𝕥𝕚𝕔𝕤 Descriptive statistics offer a starting point for data scientists. They provide a way to summarize and describe the vast amounts of data they encounter. Measures of central tendency, such as the mean, median, and mode, along with measures of variability, like the standard deviation and variance, paint a picture of the data's distribution. This initial analysis is crucial for understanding the data's structure before delving into more complex analysis. 𝕀𝕟𝕗𝕖𝕣𝕖𝕟𝕥𝕚𝕒𝕝 𝕊𝕥𝕒𝕥𝕚𝕤𝕥𝕚𝕔𝕤: 𝕋𝕙𝕖 𝔾𝕒𝕥𝕖𝕨𝕒𝕪 𝕥𝕠 𝔾𝕖𝕟𝕖𝕣𝕒𝕝𝕚𝕫𝕒𝕥𝕚𝕠𝕟 Where descriptive statistics describe, inferential statistics infer. Data scientists often work with samples—a subset of a larger population. Inferential statistics empower them to make predictions and generalizations about this larger population from which the sample is drawn. Techniques such as hypothesis testing, confidence intervals, and regression analysis are fundamental for making informed decisions and predictions. 𝕄𝕠𝕕𝕖𝕝 𝕍𝕒𝕝𝕚𝕕𝕒𝕥𝕚𝕠𝕟 𝕒𝕟𝕕 ℝ𝕖𝕤𝕦𝕝𝕥 ℝ𝕖𝕝𝕚𝕒𝕓𝕚𝕝𝕚𝕥𝕪 The true test of any data-driven insight is its validity and accuracy. Statistical knowledge is vital for validating models and results. It ensures that the conclusions drawn are not just by chance. P-values, R-squared values, and other statistical tests provide a measure of reliability and help data scientists to quantify the certainty of their findings. ℂ𝕠𝕟𝕔𝕝𝕦𝕤𝕚𝕠𝕟 Statistics is the language of data. For data scientists, proficiency in this language is non-negotiable. It is through statistical methods that data reveals its stories, and these stories inform decisions, drive innovation, and create value. As data continues to grow in volume, variety, and velocity, the role of statistics in data science becomes even more pronounced, solidifying its place as an indispensable tool in the data scientist's arsenal. #DataScience #Statistics #DataAnalysis #InferentialStatistics #DescriptiveStatistics #DataDrivenDecisions #ModelValidation #PredictiveAnalytics #DataInsights

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For those of you new to quantitative research, here is a quick introduction: https://lnkd.in/egsyGHss

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🎓 PhD Aspirants, Listen Up! 🎓 Are you reaching out to PhD supervisors before applying? 🤔 You might be doing it WRONG! #PhDAspirants #PhDMentor #PhDAdmissionTips #ResearchJourney #AcademicGuidance #PhDSupervisor #DrRitikaGauba #PhDJourney #PhDGoals #PostdocTips #PhDApplicationProcess

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Microsoft PowerPoint is more crucial than a Python or R in the realm of Data science & Machine learning A correlation score of 0.7 or a model accuracy of 75% or that beautiful elbow curve in scree plot, which appear like jewels on your output window can be lost on a Power point slide if your insight fails to land on the stakeholder. Powerpoint and narration are extremely crucial to Data science and PowerPoint is here to stay :)

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Time-varying covariates can be tricky for survival data. Why? They could violate the PH assumption! (if you're using a Cox PH model) One solution is to extend the Cox PH model to include time-varying covariates. An article I recently came across discussed just this! Zhang et al. (2018). The authors give tangible examples and R code! Building off this code, I've been able to explore a few scenarios myself. There are several options when the PH assumption is violated. It's been interesting comparing them. Recommend reading this one if you're curious about time-varying covariates and Cox regression! #CausallyCurious #RealWorldEvidence #CausalInference #RealWorldData [Link: https://lnkd.in/eu2W-S5r]

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