显示模板-统计DS
美国-莱斯大学

莱斯大学 Rice
数据科学硕士


简 介

  • 该项目用以解决现实世界的一切问题,学生将在这里获得各种各样的商业培训和实践经验,这样有助于提高学生的工程专业技能

  • 该项目专注于学生的职业技能发展,将工程中遇到的实际问题与商业,管理,通信,领导力和创业课程相结合,致力于学生数据分析方面专业发展

项目优势:
 
完成该项目后,学生将:
 
  · 了解数据科学的计算和统计基础
 
  · 了解和理解如何将数据科学的核心方法应用于专业领域或广泛领域

  · 应用数据科学技术来解决现实世界的困难问题,从原始数据和脏数据开始,到有效传达给非专业客户的可行见解结束

项目时长:一年(三个学期)全日制

项目授课地点:美国 德克萨斯州  休斯顿
申请要求

学术


  • 申请人必须具有学士学位或认可机构的认可同等学历,最低平均绩点 (GPA) 为 3.0 (B)
  • 申请人需要具备数学或科学本科背景


TOEFL


总分:90+

GRE


不强制但强烈建议递交

IELTS


总分:7+

其他要求




申请流程

第1步:准备好申请文件

  • 推荐信

三封推荐信

推荐人要能够证明申请人的量化能力、职业理想/或领导技能的人

推荐人可以是前教授、学术顾问、雇主等均可


  • SOP

Applicants must submit a statement of purpose with their online application. This should include a brief overview of your research experiences (more detail should be placed in your resume or CV) as well as your reason for attending graduate school.

Share your passion for what you want to do with respect to your research. Take time to connect yourself with several faculty members and their research.

 This statement should include the applicants intended plan of study or area of specialization. This is where you should discuss any papers or research completed or in progress. Briefly describe other factors you would like the Graduate Committee to consider while reviewing your application (e.g. personal background, work experience, leadership roles, etc.)

  • Resume

第2步:需要的其他文件

  • 扫描并上传每所就读大学的学校颁发的成绩单
  • 官方学位证书(申请时如未毕业可不提供)
  • 不强制提交 GRE 成绩,但强烈建议;
  • 提交英语语言能力成绩
  • 护照复印件
  • 本科和/或研究生学位的记录 

第3步:在线填表申请
4步:递交申请费

GT备考
核心课程

核心课程:课程包括旨在帮助学生了解以计算和统计为基础的数据科学的核心课程。

  • 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.
 
专业性:
通过选择业务分析、机器学习或图像处理专业,学生将获得更深入的数据科学知识。*目前,图像处理课程仅提供给校内课程。

 
  • 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.)


选修课:
Choose from a number of courses in computing, ethics, and security.
 
Capstone:
一个为期一学期的项目,将原始数据转换为现实世界问题领域中的可操作知识。

  • 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.

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