Third Semester
DATA ENGINEERING
This course introduces students to data warehousing architectures, big data processing pipelines, and in-memory analytic techniques as an alternative to traditional warehouse approaches. The class will provide an overview of conventional data warehousing architectures, focusing on those processing pipeline technologies that enable the management of both SQL and NoSQL data. Students will learn how to design systems to manage large volumes of poly-structured data including temporal, spatial, spatiotemporal, and multidimensional data. The class will also provide an overview of the benefits of in-memory analytics, focusing on cloud computing and cluster computing architectures and associated modern toolsets. Students will learn how to design in-memory systems to iterative graphs, complex multistage applications, and fault tolerant solutions, and to use modern cloud based analytic platform services.
INTRODUCTION TO VISUALIZATION
This course introduces tools and methods for visualizing data and communicating information clearly through graphical means. The class covers various data visualizations and how to select the most effective one depending on the nature of the data. Students will work with modern analytic graphics packages, and will be introduced to open source libraries and best-in-class libraries and methods.
The course focuses in part on the technical methods: what actions and commands are necessary to ingest data and visualize it. The course also focuses equally upon the concepts and thought processes that make for strong visualizations. Examples include a focus on data density; as more data is provided to the user in a consumable fashion, the truth is necessarily more evident. Another example is line weights and text embedded into the visualization; these techniques help bring the user's eyes to the important parts of the visualization that the student wants to emphasize. Finally, the course revisits concepts to which the students have been exposed earlier, such as reproducible reports.
CUSTOMER ANALYTICS
This course introduces the techniques used to analyze consumer shopping and buying behavior using transactional data in industries like retail, grocery, e-commerce, and others. Students will learn how to conduct item affinity (market basket) analysis, trip classification analysis, RFM (recency, frequency, monetary) analysis, churn analysis, and others. This class will teach students how to prepare data for these types of analyses, as well as how to use machine learning and statistical methods to build the models. The class is an experiential learning opportunity that utilizes real-world data sets and scenarios.
NOSQL DATABASES
This course will examine different non-relational (NoSQL) database paradigms, such as Key-Value, Document, Column-family, and Graph databases. Students will learn about advantages and disadvantages of the different approaches. The class will include hands-on experience with a representative sample of NoSQL databases. Computing developments that spurred the existence of NoSQL databases, such as big data, distributed and cloud computing will also be discussed. .
HEALTHCARE CASE STUDIES
This course provides an experiential learning opportunity that ties together the statistical, computational analytics, and database concepts in a series of case studies in the Healthcare sector. Students will examine four separate case studies of the use of data analytics in healthcare. Students will work in teams to dissect these case studies and evaluate the business opportunity, the analysis methodology, the raw data, the feature engineering and data preparation, and the analytical outcomes. Students will present their evaluation and make recommendations for improvements in the analysis and related opportunities.
EMPHASIS CASE STUDIES
This course is a culmination of the third semester in the MSc Analytics program. It provides an experiential learning opportunity that ties together the statistical, computational analytics, and database concepts in a series of case studies in the finance, manufacturing, telecommunications, and retail sectors. Students will examine four separate case studies of the use of data analytics. Students will work in teams to dissect these case studies and evaluate the business opportunity, the analysis methodology, the raw data, the data and feature engineering and data preparation, and the analytical outcomes. Students will present their evaluation and make recommendations for improvements in the analysis and related opportunities.
This course builds upon the Introduction to Visualization course. It will dive into how visualizations should be presented differently when presenting to lay people, business executives, and a technical group. It will also consider visualizations meant for exploratory analysis versus persuasive argument versus survey, or '30,000 foot' analysis. Working alone and in teams, students will create visualizations using their own findings and using provided case studies.
WEEKLY HOT TOPICS
This course consists of a set of weekly presentations and discussions around key analytic issues and current case studies. These hot topics will be presented by a combination of guest speakers-industry luminaries in the area of analytics-and University of the Pacific faculty members, including the MS analytics program director. Many of these topics will be drawn from relevant real-world contemporary analytic stories that reinforce specific elements of the academic content being taught and can not be predicted in advance.