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数据科学 is an emerging field that combines methods from machine learning, 人工智能, and statistics with entirely new technologies for handling vast amounts of complex and quickly-changing data. The job of a data scientist is to apply these methods to gather information and achieve goals that are otherwise impossible for human experts due the large quantities and complexity of the data.
在网赌上分平台的M.S. 在数据科学项目中, you will develop proficiencies in the key skills expected of leading data scientists, 包括深度学习和统计分析, and the data skills needed to work with unstructured data and 自然语言处理 (NLP).
Our faculty are leaders and innovators in their fields, bringing both deep professional experience 以及课堂上的学术严谨.
The information below is designed to show the many possible careers you could pursue with your major. 该研究由Encoura提供, the leading research and advisory firm focused exclusively on higher education. It includes median national salaries and industry growth projections over the next decade. 点击此处查看报告全文.
软件开发人员
2021-2030年增长21%
数据科学家
2021-2030年增长25%
统计学家
2021-2030年增长26%
An introduction to machine learning theory, design and implementation. 包括数学基础, 分类方法, 回归, 无监督学习, 以及统计学习理论的核心概念. Course will emphasize and use Python tools and machine learning application to real datasets.
An introduction to the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision theoretic modeling paradigm.
This course covers artificial neural networks and modern network architectures with an overview of achievements and open problems in deep learning. Emphasis is placed on hands-on 程序ming using modern frameworks and real data. Topics include convolutional, recurrent, and unsupervised-learning networks.
An exploration for essential data science skills involved in working with unstructured data, including transforming it into structured data types able to be analyzed, 加工过的, 并用于机器学习和信息检索算法. Material focuses on natural language processing and classification techniques used in text mining.
Advanced topics in "Big Data" infrastructure and architectures focusing on computing resources and 程序ming environments to support the development of efficiently scalable high-volume distributed machine learning algorithms.