Data Solutions

Transforming data into insights with expertise in health data science and tailored solutions.

Study Design

• Study Design & Protocols: Develop robust observational or experimental study structures, including sampling plans and methodology.

• Survey Development & Questionnaires: Craft clear, targeted survey instruments; oversee distribution and collection.

• Disease Classification Expertise: Skilled in ICD-9, ICD-10, and clinical coding for research insights.

• Evaluation & Reporting: Conduct monitoring and evaluation (M&E), generate comprehensive study reports.

Statistical Analysis

• Hypothesis Testing & EDA: Perform parametric/non-parametric tests, ANOVAs, power analyses, and exploratory data analysis (EDA).

• Advanced Statistical Analysis: Regression modeling, time-to-event analyses, mixed methods, time series to answer complex research questions.

• Bioinformatics Expertise: Develop and execute workflows for genomic and transcriptomic data analysis, including differential expression, and functional enrichment to uncover biological pathways.

• Electronic Health Record: Proficiency in EHRs data extraction and processing for research purposes.

• Cloud Solutions: Expertise in Google Cloud Platform (BigQuery, Dataflow) and other leading cloud services for secure, scalable data processing.

• Data Manipulation: Proficiency in Python (NumPy, Pandas), R (tidyverse), SQL, SAS, Stata—ensuring clean, reliable data pipelines.

• Maintenance & Monitoring: Maintain databases, perform ongoing study surveillance, and ensure quality control.

Data Management

Data Visualization

• Interactive Dashboards: Develop dynamic R Shiny or Python Dash applications that showcase real-time analytics and custom user interfaces.

• Business Intelligence Tools: Create and manage dashboards in Power BI or Tableau.

• Geospatial Analysis: Harness ArcGIS, QGIS, and SaTScan to uncover spatial patterns, identify hotspots, and optimize resource allocation.

• Insightful Storytelling: Combine advanced visuals with compelling narratives.

Machine Learning

• Core Data Analysis & Modeling: Skilled in Python, R, and SQL for data wrangling and statistical modeling.

• Machine Learning Techniques: Classification (logistic regression, random forests), clustering, time series, and recommendation systems.

• Algorithm Optimization: Hyperparameter tuning, feature selection, and ensemble methods (XGBoost, LightGBM).

• End-to-End ML Workflows: From exploratory data analysis (EDA) through productionizing models.

• Neural Networks & Transfer Learning: Build and fine-tune ANNs, CNNs, and advanced architectures in TensorFlow, PyTorch, or Keras.

• Computer Vision & Image Recognition: Detect objects, classify images, and leverage deep models for real-time image processing.

• Natural Language Processing: Apply NLP techniques for text classification, sentiment analysis.

• Complex Projects: Integrate web scraping and large-scale data ingestion for high-volume AI tasks.

Deep Learning & AI