Data Science
Specialization

This course addresses needs in industry, business, and academia to improve performance and advance scientific knowledge. You will learn data mining techniques that help discover patterns, trends, anomalies, and associations that are otherwise hidden or unknown. In addition, this course introduces the fundamentals, principles, implementation techniques, and applications of data mining. Learning also includes data curation techniques, focuses on exploratory data analysis, prediction, classification, association analysis, similarity assessment and clustering, outlier, and anomaly detection. Interpreting and evaluating data analysis/data mining results is explored. Additionally, data mining experience for applications in computer vision, big data, and social networks will be provided.

This course provides an advanced study of theories and concepts about knowledge management systems (KMS) and trends to evaluate the gaps between theory and practice in knowledge management. Additionally, the course will provide an overview of a comprehensive and methodological approach to support managers in their implementations of KMS. You will also gain the concepts related to capturing, storing, managing, recalling, and reusing organizational knowledge. This course also includes the process to measure results and enable managers to improve their KMS implementations and identify key future issues.

This course includes analytics methods to understand how data is shaped in relation to how it can be analyzed. This is a foundational skill for data scientists and important to apply prior to creating confirmatory (final) models that predict and deliver end-user insights for decision making. The focal points in this course are descriptive statistics and exploratory data analysis. Specific attention is given to measures of central tendency, clustering, variability, and frequency. You will learn identification of the appropriate univariate analysis for use in applied research in a business context. You will also learn to apply clustering analysis in relation confirmatory models.

Evaluating the accuracy and effectiveness of graphical representations of data is a critical skill required of experienced data scientists. This advanced course in data visualization will help you identify the appropriate questions required to evaluate the validity of the insights provided by others and develop the skills needed to influence other decision makers. During this course, you will synthesize research on the best practices associated with communicating through data visualization. You will also study techniques and processes you can use to dynamically communicate your interpretations of effective graphic interactive representations of data.

Establishing insights concerning population estimates, while understanding and communicating knowledge about variance in likely outcomes, is a fundamental skill of a data scientist. At the doctoral level, you will apply this understanding to the delivery of documentation for an audience of stakeholders who hinge key business decision-making on understanding the likelihood of an event’s occurrence. Within the academic setting, this understanding drives the development of foundational knowledge for research in the resolution of problem settings. In this course you will learn how to understand probability functions to apply your knowledge as a decision-maker or educator.

This course focuses on modern tools and methods to develop and work with large datasets. Some course concepts include the exploration of relational databases, distributed storage software, distributed computing methods, analytics and algorithms. You will explore current topics in the area of big data and potential future problems. You will investigate appropriate architectural techniques associated with big data. You will also evaluate the constructs of ethics in data science, propose techniques for application, and design a system to produce insights.