加州大学伯克利分校数据科学专业设置

《数据科学》硕士专业设置俞梦怡 14723396专业（方向)名称：Data Science学位名称：professional Master of Information and Data Science (MIDS)信息和数据科学专业硕士级别：master 硕士所属院系：The UC Berkeley School of Information (I school) 信息学院所属学校：加州大学伯克利分校网址：/专业介绍：Designed by I School faculty, our curriculum is multidisciplinary. You will bring together a range of methods to define a research question; to gather,store, retrieve, and analyze data; to interpret results; and to conveyfindings effectively. Using the latest tools and practices, you will identifypatterns in and gain insights from complex data sets.由信息学院的教师设计，课程是多学科的。

你将使用一系列方法来定义一个研究问题：去收集、存储、检索和分析数据，去解释结果并有效地传达发现。

采用最新的工具和实践，你会识别模式，并从复杂的数据集中获得见解。

专业培养目标：train leaders in the ever-evolving field of data science培养在数据科学领域的领导人专业培养方案：The program focuses on problem solving, preparing you to creatively apply methods of data collection, analysis, and presentation to solvethe world’s most challenging problems.侧重于问题解决，帮助你准备创造性地运用数据的收集、分析和图像的方法来解决世界上最具挑战性的问题。

学生背景要求： 1. A bachelor’s degree学士学位2. Test scores 考试成绩（GRE/GMAT/TOEFL）3. A high level of quantitative ability 高层次的定量能力4. A problem-solving mindset 解决问题的思维方式5. A working knowledge of fundamental concepts基本概念的应用知识6. The ability to communicate effectively 有效的沟通能力7. Programming proficiency 编程能力学分：27学分（九门课）完成时间：5 terms，20 months五个学期，20个月授课方式：The UC Berkeley School of Information’s Mas ter of Information and Data Science (MIDS) is a web-based program featuring immersive courseworkand live, online classes you can attend from anywhere in the world.Delivered on a state-of-the-art learning platform, datascience@berkeleyfacilitates collaboration and discussion to help you build a professionalnetwork of faculty and peers from the start.Students can access all datascience@berkeley content 24 hours a day, 7days a week.加州大学伯克利分校信息学院的信息与数据科学硕士（MIDS）是一个基于网络的项目，这是具有身临其境的课程和直播，你可以在世界任何地方参加网上课程。

在国家最先进的学习平台上进行传送，伯克利分校的数据科学有助于协作和讨论，以帮助学生从一开始就建立一个与教师和同行一起的专业网络。

学生可以一周七天，每天24小时访问伯克利分校所有数据科学的内容。

课程架构/课程体系：Below is a sample course schedule and the expected paththrough the degree program. Students who are interested intaking the program on an accelerated basis can complete theircoursework in 3 or 4 terms with approval from the School bytaking up to 3 courses in one or more terms.下面是一个示例课程安排，以及通过学位课程的预期路径。

有兴趣在加速基础上参加该项目的学生能够在3或4学期完成他们的课程，这需要获得学院批准其在一个或多个学期内完成3门课程。

每门课程简介：1.Research Design and Application for Data and Analysis数据和分析研究设计与应用技能：Research design / Question formulation / Data and decision making / Understanding cognitive bias / Data for persuasion and action /Integrating data and domain knowledge / Storytelling with data研究设计/问题制定/数据和决策/了解认知偏差/数据进行劝说和行动/数据集成和领域知识/用数据讲故事课程简介：This course introduces students to the burgeoning data sciences landscape, with a particular focus on learning how to apply datascience techniques to uncover, enrich, and answer questions facingindustries today. After an introduction to data sciences and anoverview of the program, students will explore how organizationsmake decisions and the emerging role of big data in guiding bothtactical and strategic decisions. Lectures, readings, discussions, andassignments will teach how to apply disciplined, creative methods toask better questions, gather data, interpret results, and conveyfindings to various audiences in ways that change minds and changebehaviors. The emphasis throughout is on making practicalcontributions to real decisions that organizations will and shouldmake. Industries and domains that we will explore include sportsmanagement, finance, energy, journalism, intelligence, health care,and media entertainment.本课程向学生介绍了新兴的数据科学的情况，尤其侧重于学习如何运用数据的科学技术来发现、丰富并回答如今所面临的行业问题。

在介绍了数据科学和项目的概况后，学生将探讨企业如何做出决策和大数据在指导战术和战略决策中扮演的新兴角色。

讲座、阅读、讨论、作业会教学生如何运用学科和创造性的方法来提出更好的问题，收集数据、解释结果并向大量听众传达调查结果可以改变思想和行为方式。

整体的重点是为组织提供切实有效的决策。

我们将探讨的行业和领域包括体育管理，金融，能源，新闻，情报，医疗保健和媒体娱乐。

2. Exploring and Analyzing Data 探索和分析数据技能：Research design / Statistical analysis 研究设计/统计分析工具：R课程简介：The goal of this course is to provide students with an introduction to many different types of quantitative research methods and statisticaltechniques for analyzing data. We begin with a focus on measurement,inferential statistics, and causal inference. Then, we will explore arange of statistical techniques and methods using the open-sourcestatistics language, R. We will use many different statistics andtechniques for analyzing and viewing data, with a focus on applyingthis knowledge to real-world data problems. Topics in quantitativetechniques include: descriptive and inferential statistics, sampling,experimental design, parametric and non-parametric tests ofdifference, ordinary least squares regression, and logistic regression.本课程的目的是为学生提供介绍许多不同类型的定量研究方法和分析数据的统计技术。