大数据分析专业名校核心课程

什么是DA?

数据分析是指 用适当的统计分析方法对收集来的大量数据进行分析，提取有用信息和形成结论而对数据加以详细研究和概括总结的过程，这一过程也是质量管理体系的支持过程。 在实用中，数据分析可帮助人们作出判断，以便采取适当行动。

数据分析的数学基础在20世纪早期就已确立，但直到计算机的出现才使得实际操作成为可能，并使得数据分析得以推广。可以说，数据分析是数学与计算机科学相结合的产物。

如今世界进入了大数据时代，几乎每个企业都有巨量数据需要分析。因此不论国内外，数据分析专业都非常受到学生的欢迎。就读于该专业将会学习到的课程包括计算机、计算机编程、应用统计学、应用数学、运筹学、优化选择、决策理论等等。

以哈佛大学、哥伦比亚大学、南加州大学为例，为大家详细介绍一下：

Harvard University

Master of Science in Data Science

https://www.seas.harvard.edu/applied-computation/graduate-programs/masters-> DATA SCIENCE COURSES (课程设置)

Core Courses:

AC 209a Data Science 1: Introduction to Data Science

AC 209b Data Science 2: Advanced Topics in Data Science

AM 207 Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference, and Optimization

CS 207 Systems Development for Computational Science

AC 221 Critical Thinking in Data Science

Research Courses:

(At least one research experience. This requirement can be satisfied by the AC 297r Capstone project course, a semester-length independent study project, or a master's thesis project.)

AC 297r Data Science Capstone Research Project Course

AC 299r Independent Study in Applied Computation

At least one Computer Science elective and one Statistics:

CS 165 Data Systems

CS 171 Visualization

CS 181 Machine Learning

CS 182 Artificial Intelligence

CS 281 Advanced Machine Learning

CS 282r Topics in Machine Learning

STAT 131 Time Series & Prediction

STAT 139 Linear Models

STAT 149 Generalized Linear Models

STAT 195 Statistical Machine Learning

Up to one seminar course - AC 298r or similar

Up to four other data science electives (from other FAS departments or other schools at Harvard)

As a final requirement, the presentation of a poster on a data science project at the annual IACS Project Showcase.

Admissions Criteria: (关于学术背景方面的招生要求)

We are looking for candidates who have demonstrated a capacity for advanced computational work by excelling in courses in math, computer science, statistics or scientific computing;Exploring computational or statistical approaches in undergraduate research,or through distinctive professional accomplishment.

There are no formal prerequisites for applicants to our master’s programs. However successful applicants do need to have sufficient background in Computer Science, Math, and Statistics - including fluency in at least one programming language and knowledge of calculus, linear algebra, and statistical inference.

Columbi__a University

Master of Science in Data Science

https://datascience.columbia.edu/master-of-science-in->

CURRICULUM: (课程设置)

Required/Core Courses:

STAT GR5701 Probability and Statistics for Data Science

CSOR W4246 Algorithms for Data Science

STAT GR5703 Statistics Inference and Modeling

COMS W4121 Computer Systems for Data Science

COMS W4721 Machine Learning for Data Science

STAT GR5702 Exploratory Data Analysis and Visualization

ENGI E4800 Data Science Capstone and Ethics

Electives:

Nine (9) credits of elective courses should be drawn upon existing graduate level courses at Columbia University.

In addition to advisor approval, elective course selection will be subject to course prerequsities, course availability, and the cross-registration procedures of the school/department offering the requested courses.

ELIGIBILITY REQUIREMENTS：(关于学术背景方面的招生要求)

Undergraduate degree

Prior quantitative coursework (calculus, linear algebra, etc.)

Prior introductory computer programming coursework

University of Southern California

Master of Science in Applied Data Science

https://datascience.columbia.edu/master-of-science-in->

CURRICULUM: (课程设置)

Core Courses:

INF 551 Foundations of Data Management

INF 552 Machine Learning for Data Science

INF 553 Foundations and Applications of Data Mining

Electives:

Take five (5) courses

CSCI 544 Applied Natural Language Processing

CSCI 550 Advanced Data Stores

CSCI 570 Analysis of Algorithms

CSCI 572 Information Retrieval and Web Search Engines

CSCI 587 Geospatial Information Management

INF 510 Principles of Programming for Data Science

INF 529 Security and Privacy in Informatics

INF 549 Introduction to Computational Thinking and Data Science

INF 550 Data Science at Scale

INF 554 Information Visualization

INF 555 Interaction Design and Usability Testing

INF 556 User Experience Design and Strategy

INF 558 Building Knowledge Graphs

INF 560 Data Science Professional Practicum

INF 599 Special Topics

REQUIREMENTS (关于学术背景方面的招生要求)

Bachelor’s degree from an accredited institution in any engineering or engineering-related disciplines including but not limited to: Statistics, Mathematics, Computer Science, Information Systems, Economics, Information Technology, Physics, Software Engineering, and Chemical Engineering.

以上就是哈佛大学、哥伦比亚大学、南加州大学官方的课程设置、关于学术背景的招生要求信息，在此列出来方便大家更加直观的了解。

正因为市场的需求，数据分析员成为了一个重要的职位。即使是听上去和数据完全不挂钩的企业，也有超出你想象的大量数据需要进行分析，才能进行决策帮助公司更好地运营。悄然无息地，数据分析专业的毕业生默默成为了各大企业争相抢夺的人才。

根据行业的不同，数据分析师可能有不同的头衔（比如：商业分析师，商业智能分析师，业务/运营分析师，数据分析师）不管头衔是什么，数据分析师是一个能适应不同角色和团队的多面手以帮助别人做出更好的数据驱动的决策。

数据分析师的薪酬

在美国数据分析师的平均薪酬为＄59,559，接下来，让我们一起来看看随着工作年限的增加，薪酬的变化趋势以及不同公司的薪酬规定吧！

工作年限角度

• 不到1年经验的入门级数据分析师平均工资总额约为54K美元（基于2,712名员工）;
• 具有1-4年经验的早期数据分析师平均薪资总额约59K美元;
• 具有5-9年经验的职业数据分析师平均薪酬总额约为67K美元;
• 具有10-19年丰富经验的数据分析师平均薪资总额约为69K美元；
• 20年以上经验的数据分析师平均总薪酬约为71K美元。

据统计： 数据分析师最高薪酬是Google,Inc., 其次是Amazon.com Inc，Kaiser Permanente、Booz, Allen, and Hamilton和UnitedHealth Group也都纷纷上榜。未来励志做数据分析师的童鞋们可以参考以上公司的薪资标准哦！