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.
R Programming (Basic)
Ethical application of data analytics to form data and facilitate modern knowledge of gap analysis, model building, and interpretation of results.
R Programming (Advanced)
Acquired, audited, and assembled data into modeling samples, performed basic data integrity checks, cleansed data, feature engineered and created data visualizations.
SAS (Basic)
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.
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.
SAS (Intermediate)
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.
SAS (Advanced)
Masters Student in Data Science with a 3.94 GPA.
Learned to solve real-world problems using a blend of business, computer science, and 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.
Data Mining Techniques
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.
- 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.
Python (Basic – Intermediate)