Babak Davani

🔍 As a dedicated and insightful Data Scientist, I bring a unique blend of healthcare expertise and advanced data science skills to the table. My journey began in the medical field, During my time at Tehran University of Medical Sciences, I achieved my MD and subsequently became a board-certified Anesthesiologist. This pivotal phase in my career was characterized by active participation in various clinical research teams, leading to significant contributions to peer-reviewed publications in the medical field.

📊 Transitioning into the realm of data science, I have effectively utilized my skills in R, Python, and machine learning to drive data-driven solutions in healthcare and education sectors. My work as a Data Analyst in Canada has seen me develop robust data pipelines, engage in sophisticated social network analysis, and create dynamic visualizations to aid decision-making processes.

💡 My passion lies in harnessing the power of data to uncover insights and guide informed decisions. With my foundation in research methodology and a keen eye for data-driven approaches, I am committed to leveraging my expertise to revolutionize patient care and tackle new challenges across various industries.

🌐 Feel free to connect with me here on LinkedIn or check out my GitHub repository for a glimpse into my work and projects.

                                     

• Programming Languages: Python, R, SQL: (MySQL , PostgreSQL)
• R Libraries: Tidyverse, ggplot2, igraph, statnet, rsiena
• Data Analysis Libraries: NumPy, Pandas, SciPy
• Visualization Tools: Tableau, Matplotlib, Plotly, Seaborn
• Statistical Analysis: Power analysis, Effect sizes, Multivariate analysis, Predictive modeling techniques (e.g., ANOVA, regression), Social network analysis, Meta-analysis
• Machine Learning Frameworks: TensorFlow, Scikit-learn, XGBoost, PyTorch
• Big Data Technologies: AWS, Hadoop, Spark, Hive, Docker

Nov 2023

• Developed a Convolutional Neural Network (CNN) using PyTorch for detecting and classifying traumatic abdominal injuries in CT scans, addressing a critical challenge in emergency medicine for prompt diagnosis.
• Applied to a diverse dataset from the RSNA Abdominal Trauma Detection Challenge, encompassing various cases of blunt force abdominal trauma.
• Achieved high model accuracy in classifying injuries in abdominal organs, with notable results including 98.57% for bowel injury and over 90% for various states of liver and kidney damage.

Feel free to reach out to me via LinkedIn or email.

“Data!data!data!" he cried impatiently. "I can't make bricks without clay.” ― Arthur Conan Doyle, The Adventure of the Copper Beeches - a Sherlock short story