莱斯大学 Rice数据科学硕士+ 查看更多
项目授课地点：美国 德克萨斯州 休斯顿
- COMP 614: Computer Programming for Data Science
An introduction to computer programming designed to give an overview of programming and algorithmic topics commonly seen in Data Science, such creating and manipulating data structures, graphs, dynamic programming, sorting and heuristic search algorithms. Students learn how to think about these problems and how to structure effective solutions to them using Python. No prior programming knowledge is required or expected.
- COMP 642: Machine Learning
Machine learning is the automation of the inductive learning process that humans do so well. Machine learning is critical to the fields of robotics, medicine, security and transportation. In this course that focuses on practical applications, you will gain a foundational understanding of modern algorithms in machine learning.
- COMP 643: Big Data
Data science is the study of how to extract actionable, non-trivial knowledge from data. This course will introduce you to data science and focus on the software tools used by practitioners of modern data science and the mathematical and statistical models that are employed in conjunction with those tools. You will learn how to apply these tools and systems to different problems and domains with a focus on the analysis of “big” data — datasets that are too large to be analyzed on a typical personal computer.
- COMP 665: Data Visualization
Data is being generated by humans and algorithms at an astounding rate. Analyzing and interpreting this data visually is key to informed decision-making across industries. This class will cover the basic ways that various types of data can be visualized and what properties distinguish useful visualizations from not-so-useful ones. You will learn to use Python as both the primary tool for processing the data and for creating visualizations of this data.
- COMP 680: Statistics for Computing and Data Science
Probability and statistics are essential tools in data science and central to fields like bioinformatics, social informatics, and machine learning. They are the foundation for quantifying uncertainty and assessing support for hypotheses and derived models, and are at the heart of areas such as efficiency analysis of algorithms and randomized algorithms. This course covers topics in probability and statistics, including probability and random variables, basic stochastic processes, basic descriptive statistics, and various methods for statistical inference and measuring support.
- BUSINESS ANALYTICS
· Introduction to Operations Management: Introduction to the design and integration of successful operations tactics both within the organization and across supply chains. The course focuses on understanding, managing and improving processes and flows of products, customers and information and touches on bottlenecks, inventory, quality management, and strategic issues in operations.
· Introduction to Finance: Introduction to the theory and practice of corporate finance and the analytical tools necessary to answer the most important questions related to firms’ financing and investment decisions, focusing the following building blocks: Valuation, Investment Decisions, Risk and Return, Financing Decisions, Derivative Securities.
· Introduction to Marketing: Introduction to the key concepts underlying the function of marketing and its interaction with other functions in a business enterprise. Explores marketing's role in defining, creating, and communicating value to customers.
· Quantitative Operations: This applied course focuses on the digital transformation of operations management including topics such as process optimization and adaptive decision-making using AI and internet-of-things data and inventory and supply chain management using advanced, data-driven technologies.
· Quantitative Marketing: This applied course focuses on using customer information to optimize implementation of marketing strategies and measuring success. Topics include digital marketing campaigns, customer experimentation, advanced market research, and pricing.
· Quantitative Finance: This applied course focuses on analytical finance to support business decision-making. This includes applying machine learning and other data analytic tools to improve investment, financing, and risk management decisions.
- IMAGE PROCESSING
- MACHINE LEARNING
Understand the basis for machine learning and how a machine can learn without being programmed. In the machine learning customization, three 3-credit courses will help you gain experience in using machine learning to aid in tasks including data visualization, pattern classification and more:
· Algorithms for Machine Learning: An introduction to the machine learning algorithms that automatically create models from data.
· Deep Learning: An introduction to the multi-stage machine learning methods that learn representations of complex data.
- BREADTH (The Master of Data Science (MDS) breadth is an area of specialization comprised of electives from the other areas of specialization.)
Choose from a number of courses in computing, ethics, and security.
- DSCI 535: APPLIED MACHINE LEARNING AND DATA SCIENCE PROJECTS
In this project-based course, you gain a unique opportunity to put your new knowledge into practice. You will be part of a student team that will complete a semester-long data science research or analysis project sponsored by a client from across a variety of industries and disciplines. As a team, you will conduct and report on your work, receive and provide feedback and deliver a presentation about your recommendations.
相关专业申请 - 数据科学DS
相关专业申请 - 数据科学DS
相关专业申请 - 商业分析BA
相关专业申请 - 商业分析BA