Phone: (209) 946-2992
Master of Science in Analytics
Analytics Program Overview
The MS in Analytics prepares graduates for careers in data analytics and related fields. This is done by developing students’ math foundation in statistics and linear algebra, and computer programming to prepare them for coursework in topics like machine learning, fraud detection, sentiment analysis, and data visualization.
This 32-unit, 4-semester degree culminates in the Capstone Project, in which students work on an analytics problem with a sponsoring company.
Prerequisite entry requirements include:
- A Bachelors degree
- Educational qualifications and/or work experience in:
- Linear Algebra
- Computer programming (any language, although Python and R are the preferred languages)
- In addition, international students must also have:
- The US equivalent of a GPA of 3.0 or above
- TOEFL (or equivalent) English language proficiency. A minimum score of 90 or a score of at least 550 (213 on the computer-based test) is required
Analytics Program Educational Objectives
The MS in Analytics prepares graduates for careers in data analytics and related fields. This is done by developing students’ math foundation in statistics and linear algeb
The education that students receive will allow them after graduation to:
- Extract value from data to assist organization in predicting future events, understanding past performance and optimizing processes;
- Apply the methods of data mining, data wrangling, programming, quantitative methods, modeling, and machine learning to prepare very large data sets for analysis;
- Apply the scientific method to develop and test hypotheses using mathematical and statistical principles;
Deliver in skilled communication results and findings through informative visualizations to project stakeholders.
Master of Science in Analytics
Students must complete a minimum of 32 units with a Pacific cumulative grade point average of 3.0 to earn the Master of Science in Analytics degree.
|ANLT 201||Linear Algebra for Data Science I||1|
|ANLT 202||Statistics for Data Science I||1|
|ANLT 204||Linear Algebra for Data Science II||1|
|ANLT 208||Research Methods for Data Science||1|
|ANLT 212||Analytics Computing I||1|
|ANLT 224||Data Wrangling||1|
|ANLT 242||Relational Databases||1|
|ANLT 283||Weekly Hot Topics||1|
|ANLT 203||Statistics for Data Science II||1|
|ANLT 210||Professional Methods for Data Science||1|
|ANLT 213||Analytics Computing II||1|
|ANLT 222||Machine Learning I||1|
|ANLT 232||Introduction to Data Visualization||1|
|ANLT 243||NoSQL Databases||1|
|ANLT 272||Healthcare Case Studies||1|
|ANLT 283||Weekly Hot Topics||1|
|ANLT 214||Data Engineering I||1|
|ANLT 215||Data Engineering II||1|
|ANLT 223||Machine Learning II||1|
|ANLT 276||Emphasis Case Studies||1|
|ANLT 283||Weekly Hot Topics||1|
|Select three of the following:||3|
|Sentiment Analysis and Opinion Mining|
|Time Series Analysis|
|ANLT 233||Dynamic Visualization||1|
|ANLT 234||Analytics Storytelling for Data Science||1|
|ANLT 282||Capstone Project||6|
ANLT 201. Linear Algebra for Data Science I. 1 Unit.
Linear algebra is the generalized study of solutions to systems of linear equations. This course will focus on developing a conceptual understanding of computational tools from linear algebra which are frequently employed in the analysis of data. These tools include formulating linear systems as matrix-vector equations, solving systems of simultaneous equations using technology, performing basic computations involving matrix algebra, solving eigenvalue-eigenvector problems using technology, diagonalization, and orthogonal projections. The use of software to perform computations will be emphasized.
ANLT 202. Statistics for Data Science I. 1 Unit.
A survey of regression, linear models, and experimental design. Topics include simple and multiple linear regression, single- and multi-factor studies, analysis of variance, analysis of covariance, model selection, diagnostics. This class will focus more on the application of regression methods than the underlying theory through the use of modern statistical programming languages. Prerequisite: ANLT 201.
ANLT 203. Statistics for Data Science II. 1 Unit.
This course introduces Bayesian statistical methods that enable data analysts and scientists to combine information from similar experiments, account for complex spatial, temporal, and other relationships, and also incorporate prior information or expert knowledge into a statistical analysis. This course explains the theory behind Bayesian methods and their practical applications, such as social network analysis, predicting crime risk, or predicting credit fraud. The course emphasizes data analysis through the use of modern analytic programming languages. Prerequisite: ANLT 202.
ANLT 204. Linear Algebra for Data Science II. 1 Unit.
This course builds on the introductory course Linear Algebra I by exposing students to more advanced topics in linear algebra. Several very powerful numerical linear algebra techniques are available for solving problems in data analysis. This is an application-oriented course that will describe how modern matrix methods can be used to solve these problems, and provides a set of tools that can be modified for a particular application. Topics include singular value decomposition, principle component analysis, Random Walk and Markov Chains, and applications of linear algebra in data mining. The use of software that is frequently employed in the analysis of data will be emphasized.
ANLT 205. Consumer Analytics. 1 Unit.
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. Prerequisite: ANLT 222.
ANLT 206. Sentiment Analysis and Opinion Mining. 1 Unit.
This course introduces the algorithms and methods used to analyze the subjective opinions and sentiments of the author of a free text document such as a tweet, blog post, or article. The class will examine the applications of this type of analysis as well as its benefits and limitations. Sentiment analysis is closely tied to text mining and uses techniques such as natural language processing, text analysis, and computational linguistics for feature extraction and preprocessing of the data. Students will explore the current state of usage of sentiment analysis, as well as future implications and opportunities. Prerequisite: ANLT 222.
ANLT 207. Time Series Analysis. 1 Unit.
This course introduces the theory and application of statistical methods for the analysis of data that have been observed over time. Students will learn techniques for working with time series data and how to account for the correlation that may exist between measurements that are separated by time. The class will concentrate on both univariate and multivariate time series analysis, with a balance between theory and applications. Students will complete a time series analysis project using real-world scenario and data set. Prerequisite: ANLT 222.
ANLT 208. Research Methods for Data Science. 1 Unit.
Students learn about research design, qualitative and quantitative research, and sources of data. Topics will include a variety of research topics, including such things as data collection procedures, measurement strategies questionnaire design and content analysis, interviewing techniques, literature surveys; information databases, probability testing, and inferential statistics. Students will prepare and present a research proposal (with emphasis on technical writing/presentation principles) as part of the course.
ANLT 210. Professional Methods for Data Science. 1 Unit.
Students learn the tools, methodology, and etiquette in developing data science applications, tools, and analytical workflows in collaborative environments. Data scientists are at the nexus of software engineering, science, and business. In order to thrive in this world, they must work collaboratively across these fields and skill sets, while ensuring that work is accessible and digestible to everyone involved. Moreover, they must ensure their work is production-worthy and extensible. This course teaches all of the elements, both technical and conceptual, to create productive, helpful, and professional data scientists.
ANLT 212. Analytics Computing I. 1 Unit.
This course introduces computational data analysis using multi-paradigm programming languages. This course emphasizes the use of these languages for statistical and machine learning data analysis and predictive modeling. This course also emphasizes using analytics specific libraries, and will introduce the use of graphical analytics tools. Prerequisites: Introduction to Programming Boot camp module or equivalent; ANLT 203.
ANLT 213. Analytics Computing II. 1 Unit.
This course introduces computational data analysis using multi-paradigm programming languages to tackle complex data analysis problems. This course will cover the essentials of programming in these various languages and their various packages for accessing data from various sources, manipulating and preparing data for analysis, conducting statistical and machine learning analyses, and graphically plotting and visualizing data and analytical results. The course emphasizes hands-on data and analysis using a variety of real-world data sets and analytical objectives. Prerequisite: ANLT 212.
ANLT 214. Data Engineering I. 1 Unit.
This course introduces students to data warehousing architectures and big data processing pipelines. These data management approaches often contain the source data needed for analytics. The class will provide an overview of conventional data warehousing architectures, but will primarily focus on introducing students to “big data” processing pipelines 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, spatio-temporal, and multidimensional data. Prerequisite: Successful Completion of First Semester of Master of Science in Analytics (Fall).
ANLT 215. Data Engineering II. 1 Unit.
This course introduces students to in-memory analytic techniques as an alternative to traditional warehouse approaches. With the declining cost of memory, fast, in-memory analytics is becoming feasible for many businesses. The class will provide an overview of benefits of in-memory analytics with a focus on cloud computing and cluster computing architectures and associated modern toolsets. Students will also be introduced to cloud based architectures and modern cloud based analytic platforms and services. Students will learn how to design in-memory systems to iterative graphs, complex multi-stage applications, and fault tolerant solutions and to use modern cloud based analytic platform services. Prerequisite: ANLT 214.
ANLT 222. Machine Learning I. 1 Unit.
This course introduces the artificial intelligence discipline of machine learning for uncovering patterns and relationships contained in large data sets. Machine learning algorithms offer a complimentary set of analytical techniques to statistical methods. Students will be exposed to the theory underlying supervised learning methods such as neural networks and decision trees. Practical application of these techniques will be introduced in various tools like R, Python, and MATLAB. Additionally, students will learn proper techniques for developing, training, and cross validating predictive models; bias versus variance; and will explore the practical usage of these techniques in business and scientific environments. Prerequisite: Successful Completion of First Semester of Master of Science in Analytics (Fall).
ANLT 223. Machine Learning II. 1 Unit.
This course builds on the introductory course ANLT 222 by exposing students to more supervised learning techniques such as affinity analysis, and ensemble methods for combining techniques, and introducing unsupervised learning methods. Unsupervised learning is a class of machine learning for uncovering patterns and relationships in data without labeling the data, or establishing a preconceived set of classes or results. Students will learn through hands-on programming projects. This class will examine the benefits and drawbacks of unsupervised learning methods. Prerequisite: ANLT 222.
ANLT 224. Data Wrangling. 1 Unit.
This course will teach you how to retrieve data from disparate sources, combine it into a unified format, and prepare it for effective analysis. This aspect of data science is often estimated to be upwards of 80% of the effort in a typical analytics process. Students will learn how to read data from a variety of common storage formats, evaluate its quality, and learn various techniques for data cleansing. Students will also learn how to select appropriate features for analysis, transform them into more usable formats, and engineer new features into more powerful predictors. This class will also teach students how to split the data set into training and validation data for more effective analytical modeling.
ANLT 232. Introduction to Data Visualization. 1 Unit.
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 practice using the data visualization methodology by walking through a case study with the instructor and then practicing the steps on their own. Students will work with modern analytic graphics packages, and will be introduced to open source libraries, and to commercial visualization products. Prerequisite: ANLT 213.
ANLT 233. Dynamic Visualization. 1 Unit.
This course introduces advanced visualization techniques for developing dynamic, interactive, and animated data visualization. Students will learn a variety of techniques for the visualization of complicated data sets. These techniques are valuable for visualizing genomic data, social or other complex networks, healthcare data, business dynamics changing over time, weather and scientific data, and others. Often the visual presentation of data is enhanced when it is made interactive and dynamic, allowing users to “move through” the data and manipulate the data graphically for exploratory analysis. This presentation often involves web application development, and students will be exposed to these rudiments as well as tools that enable faster development of data visualization. Prerequisite: ANLT 234.
ANLT 234. Analytics Storytelling for Data Science. 1 Unit.
This course builds upon ANLT 232. 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. Prerequisite: ANLT 232.
ANLT 242. Relational Databases. 1 Unit.
This course introduces relational database management systems (RDBMS) and the structured query language (SQL) for manipulating data stored therein. The class is focused on the applied use of SQL by data scientists to extract, manipulate and prepare data for analysis. Although this class is not a database design class, students will be exposed to entity-relationship (ER) models and the benefits of third normal form (3NF) data modeling. The class employs hands-on experiential learning utilizing the modern relational database querying languages and graphical development environments.
ANLT 243. NoSQL Databases. 1 Unit.
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. Prerequisite: ANLT 242.
ANLT 272. Healthcare Case Studies. 1 Unit.
This course is a culmination of the first semester of the MS 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 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. Prerequisites: ANLT 203, ANLT 212, ANLT 243.
ANLT 273. Fraud Detection. 1 Unit.
This course introduces the use of analytics to detect fraud in a variety of contexts. This class shows how to use machine learning techniques to detect fraudulent patterns in historical data, and how to predict future occurrences of fraud. Students will learn how to use supervised learning, unsupervised learning, and social network learning for these types of analyses. Students will be introduced to these techniques in the domains of credit card fraud, healthcare fraud, insurance fraud, employee fraud, telecommunications fraud, web click fraud, and others. The course is experiential and will apply concepts taught in prior data wrangling and machine learning courses using real-world data sets and fraud scenarios. Perquisite: ANLT 222.
ANLT 274. Recommender Systems. 1 Unit.
This course introduces the techniques, algorithms, and uses for recommender systems – systems that recommend products, information, and actions based on an analysis of the personalized behavior of the user. Recommender systems are used by merchants like Amazon.com, Netflix, eBay, and many others. Students in this class will learn how to design and develop effective recommender systems, and how to recognize when a recommender system offers a suitable solution. Prerequisite: ANLT 222.
ANLT 275. Text Mining. 1 Unit.
This course introduces the essential elements of text mining, or the extension of standard predictive methods to unstructured text. The class will explore the use of text mining in domains such as digital security, bioinformatics, law, marketing, and social media. Students will be exposed to information retrieval, lexical analysis, pattern recognition, meta-data tagging, and natural language processing (NLP). A large portion of this class will be devoted to the data preparation and wrangling methods needed to transform unstructured text into a suitable structure for analysis. Prerequisite: ANLT 222.
ANLT 276. Emphasis Case Studies. 1 Unit.
This course is a culmination of the second semester in the Master of Science in 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. Prerequisite: Successful Completion of First Semester of Master of Science in Analytics (Fall).
ANLT 277. Visualization Case Studies. 1 Unit.
This course is a culmination of the third semester in the Master of Science in Analytics program. It provides an experiential learning opportunity that ties together the analytic visualization concepts in a series of case studies in a discipline of choice, for example Healthcare or Econometrics. Students will examine four separate case studies of the use of data analytic visualizations in the selected domain. Students will work in teams to dissect these case studies and evaluate the business opportunity, the analysis methodology, the analytical insights and outcomes, and the visualization techniques employed. Students will present their evaluation and make recommendations for improvements in the analysis and presentation related opportunities. Prerequisite: ANLT 233.
ANLT 282. Capstone Project. 6 Units.
This course is a culmination of all modules in the MS Analytics program. It provides an experiential learning opportunity that connects all of the materials covered in the MS Analytics program. Students will be formed into teams and assigned to an industry sponsored project. Capstone projects will be agreed in advance with sponsoring companies and will represent real-world business issues that are amenable to an analytic approach. These projects will be conducted in close oversight by the sponsoring company, as well as, a University faculty member and may be conducted on the sponsoring company’s premises using their preferred systems and tools, at the sponsoring company’s discretion. Prerequisite: Successful completion of Semester 2 (Spring).
ANLT 283. Weekly Hot Topics. 1 Unit.
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 cannot be predicted in advance.