D-Lab, UC Berkeley’s Post

Our next PROGRESS webinar is next Thursday, June 6th from 4-5pm! 💡 Join Eugene Barsky and Jeremy Buhler for a workshop on using data repositories and accessing open data resources. Read more 👉 https://lnkd.in/gDNBDZ7v #ProfessionalDevelopment #DataManagement #OpenScience

It’s finally here! francesca dominici and I have launched a new columnist in the HDSR on causal inference. The goal of the column is to provide an accessible primer on the most pressing topics in causal inference! Be sure to tune in to every issue. #experiment #causalinference #causality #causalAI #abtesting

🔬 Data Science Project: Exploratory Data Analysis on Heart Disease Dataset 📊 I recently completed an insightful project where I utilized my data science skills to explore and derive meaningful insights from a comprehensive dataset related to heart disease. Techma Zone Under the mentorship of Muhammad Danial Gauhar , this project aimed to understand the factors associated with heart disease and identify significant trends. 📈 Key Highlights: Dataset Exploration: Analyzed the heart disease dataset using Pandas, focusing on its structure and key attributes, such as age, gender, cholesterol levels, and other health indicators. 📊 Data Visualization: Used Matplotlib and Seaborn to create a range of visualizations, including scatter plots, histograms, and box plots, to illustrate relationships between risk factors and heart disease outcomes. 🔎 Insights through Analytics: Applied various analytical methods to uncover actionable insights, such as correlations between cholesterol levels and heart disease, age distributions, and gender-based trends. 🛠 Technologies Used: Python: Pandas for data manipulation. Matplotlib and Seaborn for data visualization. 🏆 Outcome: Identified key risk factors contributing to heart disease, offering insights for medical professionals and researchers. Enhanced understanding of demographic trends and health indicators related to heart disease. Provided actionable recommendations for targeted healthcare interventions and preventive measures. This project not only enhanced my technical skills but also demonstrated my ability to translate complex data into meaningful insights for healthcare. I'm passionate about leveraging data science to improve health outcomes and look forward to applying these skills to future projects in the medical field. 📊 #DataScience #DataAnalysis #Python #HeartDisease #HealthcareAnalytics #DataVisualization

🎆 Excited to share a milestone in my data science journey! 🚀 Just completed a practice project on "Diabetes Prediction" using XGBoosting. 💡 Throughout this project, I delved into the fascinating realm of predictive modeling, leveraging the power of XGBoosting to analyze and predict the onset of diabetes based on various health indicators. 📊💉 👩🏫 Key takeaways from this project: 1️⃣ Model Training: Utilized XGBoosting, a powerful gradient boosting algorithm, to train the predictive model. 2️⃣ Evaluation: Employed rigorous evaluation metrics to assess the model's performance and fine-tuned parameters for optimal results. 3️⃣ Insights: Extracted valuable insights into the factors influencing diabetes onset, paving the way for proactive healthcare strategies. I'm thrilled about the results achieved and the insights gained through this project. It's amazing to witness how data science can contribute to proactive healthcare and decision-making. 🩺💡 🙇♀️ Special thanks to Nagaraju Ekkirala , Lakshmi Teja Illuri , Innomatics Research Labs who supported me throughout this journey. 🙌 Excited to continue learning and exploring new avenues in the dynamic field of data science! 📈🔍 #DataScience #XGBoosting #HealthcareAnalytics #PredictiveModeling #DataDrivenDecisionMaking #InnomaticsResearchLabs

Excited to share a milestone in my data science journey! 🚀 Just completed a practice project on "Diabetes Prediction" using XGBoosting. 💡 Throughout this project, I delved into the fascinating realm of predictive modeling, leveraging the power of XGBoosting to analyze and predict the onset of diabetes based on various health indicators. 📊💉 Key takeaways from this project: 1️⃣ Model Training: Utilized XGBoosting, a powerful gradient boosting algorithm, to train the predictive model. 2️⃣ Evaluation: Employed rigorous evaluation metrics to assess the model's performance and fine-tuned parameters for optimal results. 3️⃣ Insights: Extracted valuable insights into the factors influencing diabetes onset, paving the way for proactive healthcare strategies. I'm thrilled about the results achieved and the insights gained through this project. It's amazing to witness how data science can contribute to proactive healthcare and decision-making. 🩺💡 Special thanks to Nagaraju Ekkirala , Lakshmi Teja Illuri , Innomatics Research Labs who supported me throughout this journey. 🙌 Excited to continue learning and exploring new avenues in the dynamic field of data science! 📈🔍 #DataScience #XGBoosting #HealthcareAnalytics #PredictiveModeling #DataDrivenDecisionMaking #InnomaticsResearchLabs

In her column this month, Madhumita Ghosh-Dastidar writes, "Our work is often complex, and and it can be hard for nonstatisticians to understand or appreciate. We need to communicate our impact in ways nonstatisticians can appreciate—to tell the stories of how statistics affects the lives of real people every day." She then shares three examples of individuals using statistics and data science to make the world a better and more equitable place: https://lnkd.in/eepvcA3T. Do you know of other individuals who are using statistics and data science to make the world better? Give them a shout out!

Start your inferential statistics journey using R with our latest blogpost published on R bloggers! This blogpost talks about the crucial relationship between sample size and standard error, revealing how they impact the accuracy of your conclusions.

Please see the most recent version of the Health Data Science Newsletter below. It includes #dataanalyst and #datascientist jobs at PennMedicine, Availity, and UPMC. There is much more on news in #artificialintelligence and current research as well. Check it out. https://lnkd.in/euthKEru If you are interested in subscribing, you can subscribe here. ---> https://lnkd.in/eBS4GSMC

Hi Everyone! I'm excited to share my latest project on heart disease classification! I have developed a machine learning model designed to classify the presence of heart disease based on various health metrics. The project utilizes advanced techniques like Logistic Regression, Random Forest, and SVM, with hyperparameter tuning through Randomized and Grid Search CV to find the best parameters. Key features of the project include: ✴Comprehensive data preprocessing and feature engineering ✴Multiple classification algorithms ✴Hyperparameter optimization ✴Model evaluation using classification metrics and ROC curves ✴Visualization of results for better understanding and insights You can explore the project on GitHub and try it out for yourself: 👉 https://lnkd.in/danMpDTy  Check out my Kaggle profile for more interesting data science projects: 👉 https://lnkd.in/d3BcXMTT I am eager to receive your feedback and suggestions. Let's collaborate to make healthcare better through data science and machine learning! #DataScience #MachineLearning #HeartDisease

We're excited to announce our latest paper on estimating parameters and form of regression models by searching in the data. No need for minimizing sum of squared residuals. Case based reasoning that enables the full specification of both parameters and form of a feature-based regression model. We admit there is still much to be discovered and invite you to join us in completing this work. Check out our paper here: https://lnkd.in/eWR5ieyS #DataScience #RegressionModels #NonlinearModels #CaseBasedReasoning