Solved real-world problems using a blend of business, computer science, statistics, machine learning, neural networks and artificial intelligence.
- Integrated components of data science to produce knowledge based solutions for real-world challenges using public and private data sources.
- Evaluated data management methods and technologies used to improve integrated use of data.
- Constructed data files using advanced statistical and data programming techniques to solve practical problems in data analytics.
- Designed an analytic strategy to frame a potential issue and solution relevant to the community and stakeholders.
- Developed team skills to ethical research, developed, and evaluated analytic solutions to improve organizational performance.
Used statistical methods to solve health and life science analytics problems (for health analytics, clinical and pharmaceutical industries). Learned how create analytical and predictive modeling, data acquisition, data mining, healthcare information management systems, epidemiology, health management, clinical research, clinical trials, health outcomes research, teamwork, and communication.
- Analyzed real data from organizations and publicly available data. Participated in team projects.
- Analyzed the planning, organization, administration, and policies of healthcare organizations using health analytics method.
- Evaluated healthcare information system technologies through immigration and interoperability of health data.
- Integrated data and analytics techniques to establish financial priorities of a healthcare organization in line with the needs and values of community and stakeholders it serves.
- Analyzed the distribution and determinants of disease and health outcomes in human populations.
Masters Student in Data Science with a 3.94 GPA.
“One of the hardest working and bright students, you have a lot to offer the field of decision science”.
R Programming (Basic)
Statistical modeling and data analysis using R programming to explore data variation, model the data, and evaluate models. Analyze and evaluate different types of regression models and error analysis methods.
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R Programming (Advanced)
Ethical application of data analytics to form data and facilitate modern knowledge of gap analysis, model building, and interpretation of results.
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SAS (Basic)
Acquired, audited, and assembled data into modeling samples, performed basic data integrity checks, cleansed data, feature engineered and created data visualizations.
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Data mining methods and predictive modeling. Designed data selection and preparation, then analyzed method selections using classification rules, decision trees, association rules, instance-based learning to semi-supervised learning, unsupervised learning, multi-instance learning and predictive modeling for various case studies and industry applications.
Python (Basic – Intermediate)
- Learned python programming language in order to apply it to data science applications such as deep learning and natural language processing.
- Deployed machine learning models in the cloud. Optimized ML and artificial intelligence models.
- Identified suitable optimization algorithms for different applications in industry.
- Applied neural network analytical methods to a variety of applications in artificial intelligence using python. Analyzed deep learning predictive models in industrial applications.
- Investigated advanced topics in Artificial Intelligence and used different Python packages and libraries for data mining such as NumPy, Pandas, SciPy, scikit-learn, nltk, re, matplotlib, Seaborn and more with Jupyter Notebooks and various IDEs.
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SAS (Intermediate)
Used different methods for analyzing continuous data from real world case studies using real life data sources. Analyzed continuous data concepts and methods like exploratory data analysis, descriptive statistics, goodness-of-fit tests, correlation measures, single and multiple linear regression, and variance and covariance. Became proficient in statistical assessments and interpreting results.
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SAS (Intermediate)
Applied different methods for analyzing categorical data from real world case studies using real life data sources. Analyzed categorical data concepts and methods to develop practical skills in exploratory data analysis, descriptive statistics of discrete data, contingency tables, and generalized linear models. Also became proficient in statistical assessments and interpretation of results.
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SAS (Advanced)
Learned advanced application and methods such as, longitudinal data, factor and principal components analysis, multivariate logistic regression, and multivariate analysis of variance (ANOVA). These methods were performed on real world case studies using real life data sources in order to leverage statistical assessments and interpretation of results.
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Data and information technology utilized to improve healthcare organizations such as health information systems (e.g., EHR), databases and analytical tools to structure, analyze and present information; legal and ethical issues affecting management of healthcare information. Other topics to include: Interoperability, FHIR spark, Hadoop, Amazon Care, health lake, health mobile applications and more.
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SAS (Advanced)
Epidemiological study of determinants and distribution of disease and disability in human populations. Empirical analysis of population data related to morbidity and mortality. Investigated disease outbreaks, risk factors, health outcomes and causal relationships. Critical evaluation of public health literature and study design.
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SAS (Advanced) and SQL
Health data analytics to improve health results in clinical care. Data integration and analysis from the perspective of patient care, decision support, and quality control for evidence-based solutions. Data manipulation using Structured Query Language (SQL): views, triggers, sequences, reporting, sub-queries, query optimization and how to use SQL for database technologies, data structures and data warehouse manipulation (spark, Hadoop).
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SAS (Advanced)
Health data analytics to guide decisions about the health of populations and individuals. Population and individual level data integration and analysis on evidenced-based solutions in clinical trials and assessment of recovery time, patient stays, survival analysis, risk of complications, morbidity, and mortality.
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Proposals & Presentation (Advance)
Master’s level research using complex analytical concepts and techniques. Project design, problem framing, and technical presentation. Team building, team collaboration, and conflict resolution are implemented in the proposal of a data science project. Strategic and technical aspects of data acquisition, data cleaning, and analytic methodology proposed and presented to project advisors and other stakeholders in various fields and industries.
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Presentations & Implementation
Continue Master’s level research and implementation of analytics project, using technical writing, and project presentations. Strategic and technical aspects of data acquisition, data cleaning, and analytic methodology to presented to project advisors and stakeholders.
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Written Thesis (Advanced Analytics)
Complete Master’s level research analytics project, implementation, technical writing, and project presentation using strategic and technical aspects of data analysis and visualization and writing a thesis.
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