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

convey

findings effectively. Using the latest tools and practices, you will identify

patterns 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 solve

the 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/TO EFL

）

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 coursework

and

live,

online

classes

you

can

attend

from

anywhere

in

the

world.

Delivered

on

a

state-of- the-art

learning

platform,

datascience@berkeley

facilitates

collaboration

and

discussion

to

help

you

build

a

professional

network of faculty and peers from the start.

Students

can access

all

datascience@berkeley

content 24 hours a day, 7

days 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

path

through

the

degree

program.

Students

who

are

interested

in

taking the program on an accelerated basis can complete their

coursework in 3 or 4 terms with approval from the School by

taking 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< /p>

学期内完成

3

门课程。

< br>

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

data

science techniques to uncover, enrich, and answer questions facing

industries

today.

After

an

introduction

to

data

sciences

and

an

overview

of

the

program,

students

will

explore

how

organizations

make

decisions

and

the

emerging

role

of

big

data

in

guiding

both

tactical and strategic decisions. Lectures, readings, discussions, and

assignments will teach how to apply disciplined, creative methods to

ask

better

questions,

gather

data,

interpret

results,

and

convey

findings to various audiences in ways that change minds and change

behaviors.

The

emphasis

throughout

is

on

making

practical

contributions

to

real

decisions

that

organizations

will

and

should

make.

Industries

and

domains

that

we

will

explore

include

sports

management,

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。< /p>

在介绍了数据科学和项目的概况后，

学生将探讨企业如

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、

< br>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。

< br>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 statistical

techniques for analyzing data. We begin with a focus on measurement,

inferential

statistics,

and

causal

inference.

Then,

we

will

explore

a

range

of

statistical

techniques

and

methods

using

the

open-source

statistics

language,

R.

We

will

use

many

different

statistics

and

techniques for analyzing and viewing data, with a focus on applying

this

knowledge

to

real-world

data

problems.

Topics

in

quantitative

techniques

include:

descriptive

and

inferential

statistics,

sampling,

experimental

design,

parametric

and

non-parametric

tests

of

difference, 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。首先侧重于测量、统计推断和因果推

断。

然后 ，

将探讨一系列使用开源统计语言

R

的 统计技术和方法。

我们将使用许多不同的统计和技术来分析和查看数据，重点是将

这一知识用于解决现实世界的数据问题。定量技术主题包括：描

述和统 计推断，

取样，

实验设计，

参数化和差 异性的非参数检验，

普通最小二乘回归和回归。

3. Storing and Retrieving Data

存储和检索数据

技能：

Data acquisition/Data cleaning and normalization/Building data bases

/

Data classification and indexing

/

Data warehousing

数据采 集

/

数据清理和规范化

/

建筑数据库

/

数据分类和索引

/

数据仓库

工具：

Python / Relational databases / Hadoop / Map reduce/ Spark/

Cloud Computing (AWS)

课程简介：

This course prepares students to deal with large-scale collections of data

as

objects

to

be

stored, searched

over,

selected,

and

transformed

for

use. We examine both the background theory and practical application

of

information

retrieval,

database

design

and

management,

data

extraction,

transformation

and

loading

for

data

warehouses,

and

operational

applications.

We

will

examine

traditional

methods

of

information

retrieval

and

database

management

as

well

as

new

approaches

that

use

massively

parallel

computation

(MapReduce/Hadoop).

Through

readings,

discussion,

and

hands-on

experimentation,

students

will

be

prepared

to

discuss,

plan,

and

implement

storage,

search

and

retrieval

systems

for

large-scale

structured

and

unstructured

information

systems

using

a

variety

of

software

tools.

They

will

also

be

able

to

evaluate

large-scale

information storage and retrieval systems in terms of both efficiency

and effectiveness in providing timely, accurate, and reliable access to

needed information.