This is a portfolio of my work in the MS Data Analysis (MSDA) degree program at Western Governors University. I completed my education to make a career change into data analytics following a career in Speech-Language Pathology. This portfolio showcases my work for prospective employers, while also providing useful resources for other students following behind me in those programs. I benefitted tremendously from the tips and advice of prior students throughout my educational journey, and this is a part of my ongoing issue to return the favor and ensure more people get the help they need to also complete this degree.
This degree included on 11 courses:
The Data Analytics Journey gives an overview of the entire analytics life cycle. Learners gain fluency in data analytics terminology, tools, and techniques. The course contextualizes the data analytics journey firmly with organizational metrics and requirements to position graduates to answer key questions for businesses and other employers. This course has no prerequisites.
This course covers the following competencies:
● The graduate explains the phases of the data analytics life cycle to contextualize and define the scope of each phase. ● The graduate develops a project plan to solve organizational problems. ● The graduate determines organizational requirements to improve key drivers. ● The graduate identifies appropriate data analytics tools and techniques to solve organizational problems.
Data Cleaning continues building proficiency in the data analytics life cycle with data preparation skills. This course addresses exploring, transforming, and imputing data as well as handling outliers. Learners write code to manipulate, structure, and clean data as well as to reduce features in data sets. The following courses are prerequisites: The Data Analytics Journey, and Data Acquisition.
This course covers the following competencies: ● The graduate predicts potential obstacles in data analysis based on the quality of data provided. ● The graduate prepares data for analysis to address organizational needs. ● The graduate writes reusable code to manipulate and clean data in preparation for analysis.
Exploratory Data Analysis covers statistical principles supporting the data analytics life cycle. Students in this course compute and interpret measures of central tendency, correlations, and variation. The course introduces hypothesis testing, focusing on application for parametric tests, and addresses communication skills and tools to explain an analyst’s findings to others within an organization. Data Cleaning is a required prerequisite for this course.
This course covers the following competencies:. ● The graduate interprets central tendency, correlations, and variation to inform organizational decisions. ● The graduate conducts parametric hypothesis testing.
Predictive Modeling builds on initial data preparation, cleaning, and analysis, enabling students to make assertions vital to organizational needs. In this course, students conduct logistic regression and multiple regression to model the phenomena revealed by data. The course covers normality, homoscedasticity, and significance, preparing students to communicate findings and the limitations of those findings accurately to organizational leaders. Exploratory Data Analysis is a prerequisite for this course.
This course covers the following competencies: ● The graduate employs logistic regression algorithms to describe phenomena. ● The graduate employs multiple regression algorithms with categorical and numerical predictors in describing phenomena. ● The graduate makes assertions based on regression modeling.
Advanced Data Analytics prepares students for career-long growth in steadily advancing tools and techniques and provides emerging concepts in data analysis. This course hones the mental and theoretical flexibility that will be required of analysts in the coming decades while grounding their approach firmly in ethical and organizational-need-focused practice. Topics include machine learning, neural networks, randomness, and unconventional data sources. Data Mining II is a prerequisite for this course.
This course covers the following competencies: ● The graduate builds neural networks in the context of machine-learning modeling. ● The graduate applies time series models in generating forecasts. ● The graduate extracts insights from text data using effective and appropriate natural processing (NLP) models.
Data Acquisition builds proficiency in Structured Query Language (SQL) and the initial stages of the data analytics lifecycle. The course introduces relational databases. Students gain concrete skills in data transference and database manipulation. There are no prerequisites.
This course covers the following competencies: ● The graduate examines the data available for analysis to determine their dimension, quality, relations, and limitations. ● The graduate implements physical data models. ● The graduate performs table operations and queries within the context of data acquisition for analysis.
Advanced Data Acquisition enhances theoretical and SQL skills in furthering the data analytics life cycle. This course covers advanced SQL operations, aggregating data, and acquiring data from various sources in support of core organizational needs. The prerequisite for this course is Representation and Reporting. This course covers the following competencies: ● The graduate applies advanced SQL operations to integrate multiple data sources. ● The graduate explores data acquisition
Data Mining I expands predictive modeling into nonlinear dimensions, enhancing the capabilities and effectiveness of the data analytics lifecycle. In this course, learners implement supervised models—specifically classification and prediction data mining models—to unearth relationships among variables that are not apparent with more surface-level techniques. The course provides frameworks for assessing models’ sensitivity and specificity. D208 Predictive Modeling is a prerequisite to this course
This course covers the following competencies: ● The graduate applies observations to appropriate classes and categories using classification models. ● The graduate implements prediction data mining models to find hard-to-spot relationships among variables. ● The graduate evaluates data mining model performance for precision, accuracy, and model comparison.
Representation and Reporting focuses on communicating observations and patterns to diverse stakeholders, a key aspect of the data analytics life cycle. This course helps students gain communication and storytelling skills. It also covers data visualizations, audio representations, and interactive dashboards. The prerequisite for this course is Data Mining I. This course covers the following competencies: ● The graduate communicates data insights to technical and nontechnical audiences. ● The graduate creates data representations to offer insight into an organizational problem. ● The graduate designs executive decision support with interactive tools.
The Data Analytics Graduate Capstone allows students to apply the academic and professional abilities developed as a graduate student. This capstone challenges students to integrate skills and knowledge from several program domains into one project. Advanced Data Analytics is a prerequisite for this course.
This course covers the following competencies: ● The graduate integrates and synthesizes competencies from across the degree program, thereby demonstrating the ability to participate in and contribute value to the chosen professional field.