Ant2arc’s Post

Causal inference is crucial as it helps in inferring causal relationships, enabling better decision-making and problem-solving in various fields, from healthcare to business and beyond. Through rigorous data analysis and experimentation, Exafluence has built robust frameworks to identify causal relationships within complex datasets, enabling clients to make informed decisions and drive meaningful outcomes. #datascience, #machinelearning, #statistics #research #causalmodeling #Researchmethods #counterfactuals

Medical manuscript is not just a matter of data analytics alone. it is about meaningful interpretation and factual reporting with clear conclusions on the pros and cons of the study.

Unlocking the Power of Visual Data with Image Analysis Data science has revolutionized how we extract insights from images, driving advancements in industries like healthcare, environmental monitoring, and beyond. Want to explore how this cutting-edge field can impact your career or business? Let’s innovate together! +254 748366631 www.africadataschool.com #ImageAnalysis #DataScience #ArtificialIntelligence 

Multivariate OR Multivariable? Understand the key differences between multivariate and multivariable analysis! 🌟 These distinct statistical techniques are essential for analyzing complex data in medical research. Learn more in our latest blog post: https://lnkd.in/dzS6KdaF #StatisticalAnalysis #StatisMed #MedicalResearch #Statistics #Biostatistics

The Positive Impact of Personal Data for Training Models: A Net Positive Opinion Discover the positive effects of using personal data to train models in this in-depth analysis. Join us as we explore the benefits and address misconceptions surrounding the topic. #DataDrivenInsights #AIinAction #ModelTraining #DataBenefits #PositiveOpinion #DataAnalysis #AIProgress #TechAdvancements #NetPositive #DataScience

Act now to stay ahead! With the right data strategy and partnership, companies can prepare for the changing paradigm in data management. Read this article to explore the development of new tailored solutions leading the transformation of data in life sciences: https://bit.ly/4aaO72c #DataManagement #GenAI #InformationManagement #LifeSciences

The providers most likely to clearly become strategic partners for the life sciences industry in the space of AI / Gen AI will be those who can deliver high-quality data at an unparalleled scale, possess industry expertise , and are capable of providing connected end-to-end ecosystems with specific applications for clients, as well as technology integrated with large language models (LLMs) featuring explainability and traceability. All this "connected intelligence" requires fit-for-purpose, globally diverse, specialized healthcare-grade data sets for high accuracy and no hallucinations. Read this article to explore new solutions leading the transformation of data in life sciences. #genai #ai #iqvia

Good data stories simplify without dumbing down. The best data storytellers know how to boil complex insights down to their essence without losing the important nuances. They use plain language and relatable examples to make the data accessible, but they don't shy away from the details that matter. Imagine you're explaining a complicated medical study. You might use an analogy to help people grasp the basic concept. But you wouldn't gloss over the key findings or limitations that add important context. Aim for simplicity and clarity, but don't sacrifice substance.

New paper review! Researchers at the Ai2 and the University of Washington have developed a new approach to synthetic data generation that employs open models. This innovative method called MAGPIE tackles two major pain points in current open data creation methods: 1. Making synthetic data generation scalable: MAGPIE does not require human intervention at any stage. No seed data needed! Instead, it leverages the instruction fine-tuning capabilities of models like Llama 3.1 Instruct to generate data prompting it with a pre-query template, making it possible to generate vast amounts of data without the need for manual input. 2. Preserving data diversity at scale: One of the biggest challenges in synthetic data generation is maintaining the diversity of the text data as the size of the dataset grows. MAGPIE can produce large diverse datasets. Want to dive deeper into the paper and understand how you could apply this method to your work? I've recorded a paper walkthrough where I break down the key findings and implications of this research. Check it out and let me know what you think! https://lnkd.in/eMPY5DwE

See how Qinsights can help you analysing your data: https://lnkd.in/eTVf7bKY #researcher  #DataAnalysis #qualitative analysis  #QualitativeResearch #TextAnalysis