Arthur Redpath’s Post

🚀 It's Official: SC Analytics is Now Live!🚀 We're thrilled to announce the launch of SC Analytics, an independent company within the Stonehaven Cozmix Group (SC Group) , focused on generating deep Data & Analytics insights in the life science space, particularly in Animal Health. At SC Analytics, we believe in the power of data to transform industries and improve lives. Our mission is to democratize data & insights in Animal Health and unlock substantial value creation opportunities. 🔍 Why SC Analytics? - Focused Expertise: Specializing Animal Health for many years, being backed up by industry veterans, experienced consultants, and data scientists. - Deep insights: Tapping into a large network of industry experts and building on the Animal Health curated insights & intelligence for over a decade. - Cutting edge Analytics: Building on data science algorithms and Advanced Analytics tools to create comprehensive data & intelligence products in Animal Health. Please visit us on www.scgroup-analytics.com to find out more about us and stay tuned for updates as we embark on this exciting journey! #SCAnalyticsLive #LifeSciences #AnimalHealth #Insights

🚀 It's Official: SC Analytics is Now Live!🚀 We're thrilled to announce the launch of SC Analytics, an independent company within the Stonehaven Cozmix Group (SC Group), focused on generating deep Data & Analytics insights in the life science space, particularly in Animal Health. At SC Analytics, we believe in the power of data to transform industries and improve lives. Our mission is to democratize data & insights in Animal Health and unlock substantial value creation opportunities. 🔍 Why SC Analytics?   - Focused Expertise: Specializing Animal Health for many years, being backed up by industry veterans, experienced consultants, and data scientists. - Deep insights: Tapping into a large network of industry experts and building on the Animal Health curated insights & intelligence for over a decade. - Cutting edge Analytics: Building on data science algorithms and Advanced Analytics tools to create comprehensive data & intelligence products in Animal Health. Please visit us on www.scgroup-analytics.com to find out more about us and stay tuned for updates as we embark on this exciting journey! #SCAnalyticsLive #LifeSciences #AnimalHealth #Insights

This is my first dashboard showing analysis made on Crop Yield ( the Avg Harvest Day, Avg Yield,Avg Rainfall). The data is a public data from kaggle.com Challenges faced: 1. Null values/Empty rows 2. Trim the white spaces 3. Wrong Spellings Solution: 1.There were some empty rows and because it's a public dataset, there's no way i could ask about the empty rows, so i had to drop them. 2. I had to make use of the Excel function Trim to remove the White Spaces. 3. I highlighted the whole sheet and click on spellings to correct some wrongly spelt words. Tools used: Excel and Power BI Skills: Data Cleaning, Data Exploration and Data Visualization. #agriculture #dataanalyst #farmer #recruiter #science #technology #data

🌟 Hello, everyone! Exciting news - I've just completed my second task at MeriSKILL, where I delved into diabetes prediction through data analysis! 💼📊 This project was a fascinating journey into the world of healthcare analytics, where I applied advanced techniques to predict diabetes risks and outcomes. 📈💉 Through this experience, I gained hands-on expertise in data preprocessing, feature engineering, and predictive modeling. 🛠️💡 Grateful for the opportunity to work on impactful projects and learn from seasoned professionals at MeriSKILL! Can't wait to tackle more challenges and continue growing in this dynamic field. 🚀✨ #DataAnalysis #DiabetesPrediction #MeriSkillExperience #ProfessionalGrowth #DataScience #HealthcareAnalytics #FutureAnalyst #LearningJourney #HandsOnExperience #BigDataInsights #ExcitingChallenges

🌍 Exploring Global Land Animal Slaughter Trends (1961-2022): A Data-Driven Insight 📊 I am thrilled to share that I have recently successfully completed an exploratory data analysis (EDA) project on global land animal slaughter trends from 1961 to 2022. 🐄🐖🐓 🔍 Key Insights: Global Growth: Significant increase in the slaughter of animals, with some regions experiencing rapid growth due to changing dietary habits and population growth. Species Dominance: Poultry emerged as the most slaughtered species in recent decades, highlighting a shift in global consumption patterns. Regional Differences: Notable contrasts between developed and developing countries, with different trends based on economic growth, policies, and cultural practices. Impact of Global Events: Historical events like economic crises and pandemics had a measurable impact on global slaughter numbers. Environmental and Ethical Considerations: As environmental awareness and ethical concerns grow, there's potential for significant changes in these trends in the coming years. A special thanks to Rushikesh Dane sir & Shambo Sen sir for their invaluable guidance and support throughout this project. 🙏 Your mentorship helped me dive deeper into the data and understand the broader impact of global land animal slaughter data analysing. I’m excited to continue exploring the intersection of data more and more!!! #DataScience #EDA #DataAnalytics #Sustainability #GlobalTrends #AnimalAgriculture https://lnkd.in/g2ttZqnK

Day 2 working on a crop-yield prediction model! One of the invaluable tools that made this possible is Kaggle, a fantastic resource for datasets used for learning and research purposes. If you're interested in exploring more, I highly recommend checking out their website. For a deeper understanding, take a look at the yield dataset I used: Yield Dataset on Kaggle. (https://lnkd.in/dPPQ2qgi) #DataScience #MachineLearning #Agriculture #Kaggle #CropYieldPrediction

Geeks vs Dinosaurs: Evaluating Data Management Platforms: How do you engage with audiences? That's a question to ask in evaluating the relevance of existing data management platforms (DMPs) in the context of modern data management needs. Pose critical questions to yourself about the capabilities of traditional DMP solutions and consider the shortcomings that lead data managers to explore alternatives like #CMDM solutions, consider the need for specialized point solutions. #DataManagementPlatforms #DMP #SpecializedSolutions #CMDM https://buff.ly/3Su5eGd

🌟 Embracing Imperfection: My First Dataset 😂 💕 Flashback to 2023: At the age of 19 👦 , I initiated "Mission Earth: Awareness Project towards Animal Cruelty" fueled by passion and a drive to make a difference. The data I gathered through a simple survey, filled with yes/no questions, would become my initiation into the world of data science. 📈 Today, as I share a screenshot of that very dataset, I want to acknowledge its imperfections. Yes, it might be considered clunky and not a shining example of pristine data science methodology. But, here lies its beauty — it's a snapshot of genuine curiosity, untainted by the complexities of a structured survey when I even don't know about data science and data itself. Yet, this imperfect dataset served as a raw canvas, capturing the essence of people's opinions and the groundwork for my final year project. 🚀 So, here's to imperfection and acknowledging the journey of growth. While I know this dataset may not be flawless, it symbolizes the humble beginnings of my foray into the data science realm. 🌐 This post isn't about showcasing perfection; it's a nod to embracing the learning curve and celebrating the authenticity of the data that started it all. 🌍🐾 #MissionEarth #DataScienceJourney #ImperfectData #PassionProject #LearningAndGrowing

#Qualitative data includes categorical data, audio/visual data, text data, and other non-measurable data. Categorical #data can be further split into nominal (without order; blood type, marital status, etc.) or ordinal (with order; grades, satisfaction rating, etc.). #Quantitative data is numeric and can be measured. It can be discrete (countable; number of students, count of sheep, etc.) or continuous (measured; height, weight, temperature, etc.). It really comes down to this: what question are you asking? Qualitative: What is your favourite animal? Bunny. What satisfaction rating do you give this? Very Good.  Quantitative: How many sheep are there? 346. How tall are you? 1.7m. We can make the quantitative questions qualitative in nature by giving groups: Are there more than 300 sheep? Yes. How tall are you? Above average. But, we can’t always make an individual’s response to a qualitative question quantitative in nature: What is your favourite animal? 2… What satisfaction rating do you give this? 5.  We change qualitative data into numbers (quantify!) to help us analyse the data. So, you might see numbers for both quantitative and qualitative data! #statistics #consultant #educate #smallbiz #postgraduate #business #NZ #startup

Let me share a powerful lens that has helped me interpret things for a long time. If you find it helpful, please refer to it as Drew's Law of Opposites. When I take my kids to the zoo, I see many posters about the great work the zoo is doing to help wild animals. The zoo might be doing good work for wild animals, but first and foremost the zoo is about captive animals. That's why we go. But animal captivity is a sore subject, so I can see why the zoo would want to appear as a great wilderness advocate and conservationist for the sake of its visitors and employees. We all do this. I'm not singling out the zoo. As descendants of predators and prey, we use disguise. Many of our own disguises are unnoticeable even to ourselves. I've found it helpful, as I encounter people and organizations, to expect that much of what is projected is opposite from what's happening. It's not 100% accurate, but there's a correlation. Now, many organizations refer to themselves as being "Data Driven." I've worked with many companies and can confidently say that promoting one's Data Driven-ness is a bad sign for company intellect. Again, not 100%. Just correlated. What we're all aiming for is smart decisions. Who cares about data? You could make smart decisions without any data and terrible decisions with data. It depends upon the data and the people doing the deciding. Another good example would be those companies proclaiming that they are "Non-Political." Watch out! #DataDriven #churn #analytics #data