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Master of Analytics
分析学 硕士
隶属于Berkeley Engineering，课程由工业工程与运筹学系提供。在伯克利运筹系，我们拓展优化、随机和数据科学的前沿，使变革性决策分析和技术能够解决交通、供应链、医疗保健、能源、机器人、金融和风险管理方面的重大挑战。
是一个就业导向的项目，硕士课程主要利用如下工具去解决问题，数据分析、优化、风险建模、模拟；培养出来的学生主要在以下几个领域工作：定价和财务、调度、风险管理、物流和供应链、人力资源、运营管理等。
课程设置包括：（26学分4学分实习 12个月完成）
（1）Python Bootcamp 0学分
（2）核心分析方法课程 17学分
（3）专业选修课9学分
（4）暑期实习4学分
课程链接：
INDENG 215 Analysis and Design of Databases (3 units)数据库分析与设计
INDENG 240 Optimization Analytics (3 units)优化分析
INDENG 241 Risk Modeling, Simulation, and Data Analysis (3 units)风险建模、模拟和数据分析
INDENG 242A Machine Learning & Data Analytics (4 units)机器学习与数据分析
INDENG 243 Analytics Lab (project course with Python, optimization & simulation) (4 units) 分析实验室（包含 Python、优化与模拟的项目课程）
下面的课程选9学分
INDENG 221 Introduction to Financial Engineering金融工程概论
INDENG 222 Financial Engineering Systems I金融工程系统1
INDENG 230 Economics of Supply Chains供应链经济学
INDENG 231 Introduction to Data Modeling, Statistics, and System Simulation数据建模、统计和系统模拟简介
INDENG 242B Machine Learning and Data Analytics II机器学习和数据分析2
INDENG C253 Supply Chain and Logistics Management供应链和物流管理
INDENG 256 Healthcare Analytics医疗保健分析
INDENG 290 Stochastic Optimization for Machine Learning机器学习的随机优化
先修课要求：
Undergraduate-level courses in
1) linear algebra and
2) probability or statistics
UC Berkeley equivalent courses:
Linear algebra: MATH 54, STAT 89A
Probability: STAT 134, STAT C140, IND ENG 172, EECS 126
Statistics: STAT 135, IND ENG 165
A minimum of one course in linear algebra and one course in probability or statistics is required for admission.
It is recommended that linear algebra and probability or statistics courses are completed by the spring semester before the intended admission term; however, having prerequisites successfully completed at the time of application increases the competitiveness of the application. Online and/or extension courses from accredited colleges/universities are acceptable.
Fluency in a computer programming language
Fluency in a computer programming language is recommended by the intended admission term; however, fluency at the time of application increases the competitiveness of the application.
Completing programming coursework at an accredited institution is preferred, but you can take coursework and/or submit certificates from other programs, such as Lynda, EdX, or Coursera.
Programming coursework information can be included and uploaded (if applicable) with the application. Do not include coursework from programs such as Lynda, EdX, or Coursera in the Academic History section of the application. Instead, please list this information on your resume and add coursework information and/or certificates to your transcript upload。
申请学生的背景信息
Although most applicants generally come from computer science, mathematics, statistics, or engineering majors, the Analytics program application is open to all that meet eligibility requirements. Long-term success in analytics requires both strong quantitative foundations and an interest in data-driven problem-solving. Applicants that are stronger in one area or the other can still be successful in admissions. We will also ensure that all students develop the required skills to start a promising career in data analytics upon completion of our Master of Analytics program.
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