| Title | Winter | Spring | Summer | Fall |
| Data Science |
Math Review for Data Science and Analytics (2.5 units)
I&C SCI X414.33
This course provides a foundation of fundamental math skills needed for the emerging fields of Data Science, Predictive Analytics and Data Analytics. Key topics include basic algebra, pre-calculus concepts and fundamental concepts of statistics. Participants will learn these skills by utilizing practical applications of those mathematical concepts key to analytics and eliminates theoretical derivations. The math skills taught will allow you to apply structured thinking, deliver statistical evidence and analytical results for business problems and decision making. A useful course for those who are interested in learning or relearning algebra and pre-calculus concepts.
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Online
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to be scheduled
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to be scheduled
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Hadoop: In Theory and Practice (3 units)
I&C SCI X425.18
Today, organizations in every industry are being showered with imposing quantities of new information. Along with traditional sources, many more data channels and categories now exist. Collectively, these vastly larger information volumes and new assets are known as Big Data. Enterprises are using technologies such as MapReduce and Hadoop to extract value from Big Data. This course provides an in-depth overview of Hadoop and MapReduce, the cornerstones of big data processing. To crystalize the concepts behind Hadoop and MapReduce, you will work through a series of short/ focused exercises; you will configure and install a Hadoop cluster, write basic MapReduce programs, gain familiarity with advanced MapReduce programming practices, and utilize interfaces such as Pig and Hive to interact with Hadoop. You will also learn about real-world situations were MapReduce techniques can be used.
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Online
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Online
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to be scheduled
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Introduction to Big Data (2 units)
I&C SCI X425.80
Web 2.0 has changed the way we conduct business. It affects consumer interactions, information sharing, success measurement in terms of business revenue and customer wallet share, and brand management. Most importantly, it has created a revenue channel like none other. Personalization of products and services by enterprises have increased data volumes and the velocity of data production while creating a variety of data formats. We are now well and truly in the world of Big Data. The key value of this data is the vast amount of intelligence that can be found when data is modeled with geographic and demographic information. This introductory course will help students navigate through the complex layers of Big Data and Data Warehousing while providing information on how to effectively think about using these technologies and architectures to design the next-generation data warehouse. Concepts covered include an introduction to Big Data, discussion of Big Data Processing Architectures, explanation of the integration of Big Data and Data Warehouses, and fundamentals of Big Data Analytics. Examples will be used to step students through an implementation of a Big Data solution.
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Online
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to be scheduled
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Effective Data Preparation (2 units)
I&C SCI X425.63
Broadly speaking, data preparation for data mining consists of three (3) elements:
1. Data Mining Process delineation (understanding the overall process)
2. Data Understanding (data cannot be properly prepared without first understanding it)
3. Data Pre-processing (transforming data into a form compatible with data mining)
This intensive hands-on course gives students the skills necessary to extract stored data elements, understand what they mean in the company, transform their formats and derive new relationships among them to produce a dataset suitable for analytical modeling. Students will learn how to produce a fully processed data set compatible for building powerful predictive models that can be deployed to increase business profitability. Required prerequisite: I&C SCI X425.61 Introduction to Predictive Analytics OR I&C SCI X425.60 Introduction to Data Science.
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Online
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Online
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to be scheduled
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Introduction to Programming with Python (2 units)
I&C SCI X426.64
This hands-on course is specifically designed for students who have very little to no programming experience. In this courses participants will learn about variable types, flow control, and functions. This course will include how to write simple scripts to process text, plot data, interact with web services, and set up a web service. Participants will also learn how to use eclipse, a popular integrated development environment. Prerequisites: None.
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Online
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Online
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to be scheduled
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to be scheduled
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Python for Data Analysis (1.5 units)
I&C SCI X426.62
Python for Data Analysis is an introductory course aimed at students who want to learn python for the purposes of importing and analyzing data. Some programming experience is beneficial but not absolutely necessary. The goal of the course is to introduce enough python so that, after the course, the student can immediately use what they have learned to ingest data, produce plots and analysis, and fit models. Students should note that not everything with-in the python language will be covered (such as user interfaces, web services, and object oriented programming). The main python libraries introduced will be numpy, matplotlib, pandas, and scikit-learn. Along with the basics of the python language, students will learn how to import data and manipulate it efficiently using numpy, how to produce plots and data visualizations with matplotlib, how to run statistical analysis using pandas, and how to build predictive models scikit-learn. There will be a final project that ties everything we have learned together. Students will also learn how to use eclipse, a very handy development environment! Prerequisites: I&C SCI X426.64 Introduction to Programming with Python; Knowledge of Python programming is required.
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Online
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to be scheduled
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Data Modeling (2.5 units)
I&C SCI X425.32
In this course you will learn how to develop data models which are used to publish the information that organizations rely on for their day-to-day decision making processes. Basic data modeling techniques will be explored including capturing data requirements, analyzing data elements, identifying entities and attributes, and determining relationships between entities. Data models will be developed using the entity/relationship graphical model. Once a basic data model has been developed, students will be instructed on how to enhance the data model to manage and enhance the value of the data it contains. This will directly affect the quality of the information produced from the data, and provide an organization with the knowledge necessary to optimally perform its business functions. The emphasis of this course is on analysis and design rather than on the actual implementation of a database.
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Online
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to be scheduled
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Data Management (2.5 units)
I&C SCI X425.35
Data may be an organization's most underrated asset. Organizations often invest a very small percentage of their budget on maintaining the health of their data, which may over time lead to decreasing competitiveness, loss of market share, and even violations of regulatory requirements. This course presents data management from an organizational perspective with the goal of using data management methodologies as a foundation for enterprise data architecture. The course concentrates on data definition and specification, data quality, data organization, data integration, and data equilibrium.
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Online
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to be scheduled
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Tools and Techniques for Machine Learning (2 units)
I&C SCI X426.75
Simply put, machine learning is a form of data analysis. Using algorithms that continuously learn from data, machine learning allows computers to uncover hidden patterns without being explicitly programmed to do so. The key aspect of machine learning is that as models are exposed to new data sets, they adapt to produce reliable and consistent output. In this course, we will cover the tools/techniques that are currently associated with the discipline of machine learning. We will start by exploring the field of machine learning and then gradually delve into practical examples of how to leverage machine learning algorithms to derive insights from big data. Once we have attained practical exposure to utilizing machine learning algorithms, we will use visualization capabilities to surface the output generated by the machine learning algorithms. Finally, we will explore the field of deep learning and understand how it differs from machine learning.
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Online
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to be scheduled
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R Programming (2 units)
I&C SCI X425.20
R is a scripting language for statistical data manipulation and analysis. R is an open source package available under GNU license at no cost. R competes with SPSS, another very well-known statistical package used heavily in many industries. Statistics is used in every part of business data processing and prediction. Data captured by web analytics services need statistics. Statistics is also the foundation of predictive analytics. R business applications include correlation, regression, hypothesis testing, and all inference testing. This course will focus on R programming which is used for solving business problems related to basic math and statistics. First, all relevant math concepts will be reviewed. This will include functions, regression, descriptive and inferential statistics, and matrix operations. All these basic math problems will be solved using R. The programmatic interface and graphic capabilities of R will also be explored. In the end, several real-world business problems will be solved using R.
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Online
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to be scheduled
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Business Intelligence & The Data Warehouse Development Process (2.5 units)
I&C SCI X427.01
Learn how to make better business decisions, use fewer resources, and improve your company's bottom line by developing and using a data warehouse. This course provides an overview of business intelligence and data warehousing and gives you a look at all the major facets of developing and using a data warehouse to make effective business decisions. Students will work on a single project to develop a comprehensive project plan and business case for a data warehouse including how to develop a dimensional model, a data staging process, and a data access process. Additional topics will include information on careers working with business intelligence and data warehousing as well as the educational requirements for this field.
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Online
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to be scheduled
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to be scheduled
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