Noblis’ Post

Exciting News! 🎉 Our research paper titled "Efficient Task Allocation with Mentorship Mapping" has been accepted for presentation at the prestigious 7th International Conference on Computational Intelligence in Data Science (ICCIDS 2024)! We're thrilled to share our insights on optimizing task allocation and leveraging mentorship mapping to enhance productivity and knowledge sharing. Join us at ICCIDS 2024 as we delve into the latest advancements in computational intelligence and data science. Stay tuned for updates as we prepare to showcase our work at this esteemed conference! #ICCIDS2024 #ResearchPaper #TaskAllocation #MentorshipMapping #DataScience #ComputationalIntelligence

#Flashback to the #DARESymposium keynote delivered by Professor Scott Sisson, a globally recognised expert in computational and Bayesian statistics. Scott's enlightening presentation showcases some of the work by his recent PhD students in “symbolic data analysis”, which aims to perform statistical analyses for data that have been summarised into distributional form, such as random rectangles, random histograms, and other distributions. Be sure to watch the video for further insights! UNSW Data Science Hub (uDASH) | #DataScience #DataAnalysis #Statistics #FlashbackFriday

What is your opinion on the academic #digitaltransformation? 👾 Join our HIDA lecture series with Dr. Melissa Laufer on February 14 and discuss with us the impact of Large Language Models on Academics and Academic Work!

On Day 3 of 30 Days of Statistical Modelling, lets talk about a very interesting measure of dispersion. The inter quartile range. The interquartile range (IQR) is a robust statistical measure that provides insights into the spread of data by focusing on the middle 50% of a dataset. Calculated as the difference between the third quartile (Q3) and the first quartile (Q1), the IQR is less sensitive to extreme values, making it particularly useful for assessing variability in skewed distributions. Understanding the IQR enhances our ability to grasp the central tendencies and overall distribution of data, crucial for making informed decisions in various fields. #statsitics #datascience #communitylearning

🔍 Excited to share my latest blog post on t-SNE! 🌟 In my recent exploration of dimensionality reduction techniques, I've delved into the fascinating world of t-SNE (t-distributed stochastic neighbor embedding). 🚀 In this blog, I demystify t-SNE and its powerful ability to visualize high-dimensional data in lower dimensions while preserving local relationships. From its graceful handling of distance to the control of density with the variance parameter, I've covered it all! 💡 If you're curious about unlocking complex patterns hidden within your data or want to enhance your data visualization skills, this blog is a must-read. Dive in and discover how t-SNE can revolutionize your understanding of data! 📊✨ Read the full blog here https://lnkd.in/g4AyHuvH. #DataScience #MachineLearning #DataVisualization #tSNE #DimensionalityReduction #DataAnalysis

Exploring t-SNE vs. SNE in Simple Terms When it comes to visualizing complex data, t-SNE and SNE are two popular methods. Let's break down the differences: 🔹 SNE (Stochastic Neighbor Embedding): ▪ Keeps Neighbors Close: SNE focuses on keeping similar data points close together. ▪ Slower for Big Data: Takes more time and power to process large datasets. ▪ Crowding Issue: Can struggle to show data clearly when reduced to 2D or 3D. 🔹 t-SNE (t-Distributed Stochastic Neighbor Embedding): ▪ Better Visuals: Uses a special method to prevent the crowding problem, making clusters more distinct. ▪ Faster with Large Data: More efficient at handling bigger datasets. ▪ Widely Used: Preferred for creating clear and insightful visualizations of data. Why Choose t-SNE? 🔹 Speed: t-SNE is quicker for large datasets. 🔹 Clarity: Produces clearer and more meaningful visuals. 🔹 Ease of Use: Simple to implement and understand. #DataScience #MachineLearning #DataVisualization #tSNE #SNE #DataAnalysis

Join us on a journey unraveling the significance of Numbers in Science! 🌐 Let's demystify complex concepts for everyday understanding. Delve into the world of Science Communication and Data Analysis with us. Making science accessible, one number at a time! #Chisquares #ScienceCommunication #DataAnalysis

Did you miss this year's DSC conference? You won't want to make that mistake again! The talks in 2023 were a huge success, and we're excited to bring you another informative and inspiring event next year. Stay connected for upcoming details on our next conferences, and check out our insightful talks! Today we're sharing Ilija Lazarevic's talk: From Algorithms to Assets: Data Science Meets Real Estate In the evolving landscape of real estate, data science emerged as a transformative force. By combining data-driven insights with property markets, we unlocked a new era of informed decision-making. A notable application was automated valuation, where machine learning leveraged vast datasets to estimate property values accurately. This synergy empowered investors, agents, and buyers with comprehensive perspectives. As data science continued to shape the real estate domain, embracing automated valuation offered a promising gateway to precision and efficiency in property assessment and transactions. This speech by Ilija Lazarevic was held on November 24th at Data Science Conference Europe 2023 in Belgrade. This year, DSC 24 is set to become the world's largest conference on Data and AI! For the entire video just click the link below ⬇ https://lnkd.in/e29QJ6Du #ai #datascience #ml #real #estate #business #dsceurope #belgrade

Navigating the Data Preprocessing Journey! 🛠️ My dissertation's comprehensive methodologies prepare data for effective analysis and modeling. 📊 Each step—feature selection, categorical feature encoding, scaling, and resolving class imbalance—plays a significant role in refining my dataset. Chi-square tests helped me identify less useful factors. 🌐 Witness the transformation of raw data into a well-prepared dataset, establishing the groundwork for powerful prediction models! #DataPreprocessing #MachineLearning #DataScience #LinkedInAnalyticsInsights.

🚀 Day 57 of #180DaysofDataScience 🚀 🔸 Statistics: Today's Adventure: 🔍 What I Explored: Cumulative Distribution Function: --> The cumulative distribution function (CDF) is a way to describe the total probability up to a certain point 📚 Key Takeaways: --> The CDF is always non-decreasing --> As x increases, F(x) either stays the same or increases --> As x approaches −∞, F(x) approaches 0 --> As x approaches ∞, F(x) approaches 1 What's Next: 🔮 Upcoming Exploration: --> Tomorrow, I will continue with Data Distributions #DataScience #180DaysOfData #LearningJourney #TechExploration #DataScienceCommunity #StayCurious