| Title | Winter | Spring | Summer | Fall |
Fundamentals of Data Science (2.50 Units)
I&C SCI X427.05
The goal of this course is to demystify data science and to familiarize students with key data scientist skills, techniques, and concepts. Starting with foundational concepts like analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, the course will then move on to compare data science with classical statistical techniques. An overview of the most common techniques used in data science, including data analysis, statistical modeling, data engineering, relational databases, SQL and NoSQL, manipulation of data at scale (big data), algorithms for data mining, data quality, remediation and consistency operations will be covered. Required prerequisites: I&C SCI X425.99 Practical Math and Statistics for Data Science and Analytics AND I&C SCI X426.64 Introduction to Python Programming
|
|
|
Online
|
Online
|
Data Engineering (2.50 Units)
I&C SCI X427.06
This course is designed to enhance student proficiency in data design, data management, data warehouse, data modeling, and query manipulation skills. Topics include techniques and methods for identification, extraction, and preparation of data for processing with database software. Gain an overview of the basic techniques of data engineering, including data normalization, data engineering, relational and non-relational databases, SQL and NoSQL, manipulation of data at scale (big data), algorithms for data operations. Students will work on a final project to explore, analyze, summarize and present findings in a real-world big data set.
Prerequisite: I&C SCI X427.05 Fundamentals of Data Science
|
to be scheduled
|
to be scheduled
|
Online
|
Online
|
Big Data Analysis (2.50 Units)
I&C SCI X427.07
Big data is one of the most important technology trends to fundamentally impact the way organizations operate and compete. As more and more companies collect large amounts of data through their daily operations, the ability to analyze and glean knowledge from big data has become an integral part of a successful business. This course will help students navigate through the complex layers of Big Data while providing insight on ways to effectively use technologies and architectures to create and manage big data workflows. Concepts covered include an introduction to Big Data and related technologies, discussion of Big Data Processing Architectures, explanation of major concepts behind Big Data Management, and how all of those topics are applied in Big Data Analysis. Students will gain an understanding of the characteristics of big data and techniques for working on big data platforms through hands-on exercises in the tools and systems used by data scientists and data engineers including Hadoop (HiveQL & PIG), Apache Spark, and SparkSQL.
Prerequisite: I&C SCI X427.05 Fundamentals of Data Science
|
to be scheduled
|
to be scheduled
|
Online
|
Online
|
Advanced Visualization (2.50 Units)
I&C SCI X427.18
Visualization plays a fundamental role in understanding properties and relationships in data to extract insights and communicate results. Whether the analytics is descriptive, diagnostic, prescriptive, or proscriptive, visualization is essential throughout any analytics cycle. This course will focus on applying various methods and techniques to different stages of the analytics cycle such as during data preparation, modeling, and reporting. Students will learn techniques for visualizing univariate, multivariate, temporal, text-based, hierarchical, and network/graph-based data both in ad hoc analysis as well as in automated generation.
Prerequisite: I&C SCI X427.05 Fundamentals of Data Science
|
to be scheduled
|
to be scheduled
|
Online
|
Online
|
I&C SCI X427.18 OR I&C SCI X426.61
I&C SCI X427.18 OR I&C SCI X426.61
|
Data Exploration, Analytics and Visualization (3.00 Units)
I&C SCI X426.61
Once data has been effectively collected, structured and cleaned, it’s time to explore and uncover applicable meaning from the data. Learn the basics of how to use processes and tools from machine learning, data mining and predictive analytics to begin this journey. Understand how to collect and gain value from the vast amount of often untapped un-unstructured (text) data. Key industry processes for understanding and utilizing data and developing predictive and related models will be covered. An overview of related topics including Graph Analytics (PageRank, community detection, recursive queries, iterative processing) and data provenance, privacy, ethics, governance will also be covered. Students will also learn techniques for visualizing data including multivariate, temporal, text-based, hierarchical, and network/graph-based data. Prerequisite: I&C SCI X426.60 Introduction to Data Science.
|
to be scheduled
|
to be scheduled
|
to be scheduled
|
to be scheduled
|
Introduction to Machine Learning & AI
I&C SCI X426.73
This course introduce concepts and topics essential to Machine Learning and AI. Topics covered include predictive analytics algorithms, artificial neural networks, data analysis, and data mining. The design, architecture, and applications of networks for practical applications will be explored. Students will also learn how artificial neural networks, such as multilayered perception, are implemented using Python.
Prerequisite: I&C SCI X426.64 Introduction to Python Programming AND I&C SCI X427.05 Fundamentals of Data Science
|
|
to be scheduled
|
|
to be scheduled
|
I&C SCI X426.73 OR I&C SCI X426.75
I&C SCI X426.73 OR I&C SCI X426.75
|
Tools and Techniques for Machine Learning (2.00 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.
|
|
to be scheduled
|
|
to be scheduled
|
