申请季申了三个学校的 BA：SMU UCI CWRU，拿到后面两个的录取，先分析一下这俩求选校意见（如有不准确请留言）：
| UCI（11 months） | CWRU（16 months） | |
| Location | 尔湾地处南加，离 LA 和 SD 都不远，尔湾本身 tech company 也很多；天气好又靠海 | 克利夫兰。。。美国铁岭?比较衰败的城市，本身工作机会也不多；天气冷 |
| 项目成熟度 | 第二年，第一年未毕业 | 第三年，之前就业率也还 ok，大多集中在 health care，金融，tech,传统制造业，零售等，在克利夫兰芝加哥波士顿之类的 |
| class size&diverity | 第一年基本全华班，enroll了39人；今年根据二月份面试的小伙伴提供的信息打算录60多人分两个班。另外我觉得 UCI 今年很强调 diversity，目前被录取的68，其中中国人（加台湾）29，美国人23，印度人9，其他就是韩国加拿大俄罗斯等其他国家了，感觉美本和工作党不少。但目前好像 R2还没审完，不知道最后会 enroll 多少人。 | 前两年分别是21和18人。。今年分了11和16两个时间，说是人数会 double，但是我感觉大部分人都会选16个月，且目前已经 enroll 超过60个中国人了。。对 small class 有些怀疑 |
| capstone project及实习 | 有半年的CP，合作企业有可口可乐 Experian IBM之类的，根据在读生反馈我觉得还不错；以及，可以实习！可以实习！可以实习！根据在读生反馈冬季学期学校好像就可以给你办 CPT 了，但冬季太忙没有人去，春季有几位找到了实习准备去。 | 16个月有 summer intern 是这个项目对我最大的吸引点；目前没有CP |
| 找工作情况及 career services | 根据在读生三月份的反馈，有一半人想留美，目前有两位有美国全职 offer。Career service 普遍反馈很用心，基本催着你找工作，但现在的政策形势自己疯狂networking+提高 skills 才是重点，关键还是靠自己 | 根据 GA 的数据，目前12个国际生有四个找到暑假实习，有 local 拿到全职。新加了 mentor program，每个人会有 mentor 带，感觉学院也会尽力提供很多内推机会给你。包括在申请的时候也能感觉到 faculty 都很认真负责，也是促使我申的一个原因。根据 UCI 在读的反馈，特别提到了 CWRU目前找工作的情况好于 UCI 和某些比较有名的学校。。。 |
| 课程（具体 course description 请看附件 | 在读生的普遍反馈是全面但不深入，少一点系统性，但是需要的tools都有 cover 到，再深入的也会涉及到，但往深了是还是靠自学。分了三个 track 但是可以自己随意组合。。我觉得分这个没什么卵用。但是选修课还是能选出来一些比较tech的课。 摘一段在读生对前两个学期的描述：summer两节课： 一节是foundation of Business analytics. instructor 是BA项目director， 主要是大概介绍一下BA是怎样一个存在，cover哪些工具，skill 等等。这节课最后的project是用明州一个航空公司的客户数据进行了clustering analysis 以及对结果的storytelling， 主要工具涉及到R 和Tableau 另一节是Statistics for Data Science. professor 是个MIT 的Econ phd，主要内容是prepare 一些necessary 的 以后分析可能用得到的知识点。中间穿插Python 的学习，每节课上半节课是professor讲知识点，下半节课由professor 和TA带领python practice。 Fall 四节课： Programming for data analysis， 这节课instructor是个清华本 stern博的女神，自身能力极强，可能刚工作没两年吧，讲课水平说不上特别强，但本人及其热心。 课程主要cover SQL，Python 的各种应用，比如抓取数据，preprocess， 随机森林在python里的实现等。 Business Intelliegnce： professor 是IS领域的大牛， 主要cover machine leaning 的基础skill 和应用. research design： 设计来prepare CAP（certificate of Analytics Professional） 的一节课 marketing: professor mkt大牛系列，研究营销领域analytics和投资回报率等，可能大牛上课都比较随意？ 举的行业的案例比较多，有时整节课都在discuss各种案例。 | CWRU的课程在我看来有点。。奇怪？并没有太看出来 BA 的感觉(请大佬们指点）我感觉很 OR。上课使用 SPSS SAS R 更多，Python 和 SQL是各六周的workshop上老师提供材料引入门，然后靠自学。。说实话我不太懂这样的安排，课程上没有太多时间带你学编程。。。然后没有选修课。。。 |
| Ranking&Reputation | 本科42, 商学院42，QS164, USNEWS75 | 37；55；213；146。不是那么在乎排名吧这俩我觉得差不多。 |
| 个人原因 | 之前去 UC 其他校交换过，超级爱加州的天气和海。从小也喜欢海边，讨厌冬天和雨雪。以后也比较想待在暖和的地方。对南加要更熟悉。尔湾连续多少年都是全美最安全的城市前几名，今年好像是第二，爸妈和我都比较在意安全问题。还有吃喝之类的都更丰富。UCI 是大年初一一早给我发的录取解决失学问题哈哈哈，因为之前一年都过于丧了，所以觉得这是一个好开始~ | 楼主8年詹密哈哈哈，对克利夫兰还是有一些感情的。但是一是担心机会少，二是担心不安全。 |
综上的话其实内心是偏向 UCI 的，问了一些人的意见也比较推荐 UCI。。。但是又怕自己因为太多不相关的因素（比如天气之类的） 影响自己的判断，所以还是想听听大家的意见，目标留美做个 DA，如果有更多关于这两个项目的信息也请分享~我这段时间也在一直和各种学长学姐聊，不管去哪都做好了疯狂 networking 和海投的准备。。
BG ：中下游985，法学（是的你没看错）加国贸。3.4，103，660（是的你没看错就是怎么都刷不上去的GMAT）。 UC 某校交换，实习的话一份不水四大咨询，文书里吹得比较好，一份证券一份法院（微笑脸）。15年年底开始想申BA 的，从一门高数都没有到申请前补了高数12，概统，统计，MATLAB，C++，SQL, Python(Coursera),当然去之前要补的还有很多。
简单总结一下啦，高一就知道自己想学商，结果阴差阳错学了一点兴趣都没有的法学，大概大学没开始之前就有转专业的打算了，之后的学习也证明法学真的不是我的菜。我这个人呢又属于那种就算撞南墙也要撞开一条血路的那种。所以大二决定出国后研究了商学院的各种大众小众专业，从 MKT-MA-BA。那个时候 BA 还很小众哇，当时想申的原因很多，比如雾草这专业真有趣！比如 STEM,比如很小众（现在真是啪啪打脸）。当时连一门数学都没有，于是就开始想尽办法补课。无奈法学院本来就课多（最多大概一个学期十几节）加上二专，加上我们不太允许修其他专业选修课，加上学院不太鼓励交换且不给转学分，各种心酸血泪史。比如为了抢到数统的课每个小时刷一遍教务系统，比如交换前半个月被副院长告知不许去然后开始想尽办法argue，比如一周三次校车往返快两个小时去上SQL。这两年多呢，身边家人朋友的质疑一直都有，自我怀疑也有过不少，觉得这两年唯一的目标就是 BABABA，自己也很功利地想尽一切办法把自己往 data 上靠。但最后还是通过课程+project+实习确定了自己对 BA 的喜爱。
整个申请季呢蛮奇葩的，毕竟在大家都申八九十来所的情况下我最后只申了三个。SMU 和 UCI 是 R1想用来保底的（啪啪打脸）。。。因为那个时候觉得GMAT肯定能考出来，托福也能刷高。结果 SMU 第一轮因为一些不可描述原因基本死卡700，无面据，本来还觉得自己口语不错（S26）能和 Hettie 吹一波，结果。。。UCI 当时就是顺手申，结果等到1月中才来 kira，后面的时间倒还比较有效率。CASE 是赶在 R2前申的，当时 uci 没什么消息，天天焦虑失学，后来被 Case 的认真和实习圈粉，再加上是老詹球迷就顺手申了。说到 GMAT 呢，其实是我最早开始准备的考试了，大二觉得时间还长，没考出来就去干别的了。托福倒是很轻松，半裸考的样子。GMAT 在申请季密集考了三四次，660640620轮流来，但是平常各种软件模考都稳在700好几的样子。所以整个申请季整个人都很抑郁，心态也很差，再加上学院破事太多，每天难过且焦虑，不知道下一步该怎么走。年前一周面了 UCI 的真人，知道就是过年那周出结果，每天都担心悲剧了怎么办，大年三十的时候还担心要没有ad 过年了。一个有趣的细节是年三十儿晚上12点我家吃饺子，每年家人都会在几十个饺子里面选几个放花生，前几年我基本都没吃到过，然后今年就随手抓了两个吃，两个都有花生，当时就在求好运??结果第二天一早还在床上玩手机的时候 uci 录了，整个人瞬间放松（颓废）下来。其实后来也越来越佛系了，也越来越信命??。前一阵子看到一个 gap 的帖子求大家建议，底下有很多同要 gap 的人说自己在申请季有些抑郁之类的，其实真的很能理解的，希望还在挣扎的 gap 党今后申请一切都好。
感觉上面的总结好像卖惨哈哈哈，虽然我三维不高申请结果也没那么好，但还是给之后的文科申请者一点点个人建议吧。
1.我应该是 BA 转专业申请者里最文的那类了吧，这两年多也一点点看着 BA 从一个小众专业到各种背景的人都要来试试的火爆专业，自己本身也从小白对这个专业有更深刻的认识，当然也从曾经的 STEM 好就业到现在地理性面对就业惨状（当然对比纯商还是好很多的）。老实讲我并不是很推荐大家一股脑地都涌向这个可能你自身也不太了解，只是听从无良中介吹捧的所谓就业好高薪专业，当然我也并不是劝退帖。对于tech背景特别薄弱的文科申请者，去补补课，看看自己是不是真喜欢，且自己是不是可以 handle，然后再做决定。
2. 请考 GRE。并不是因为我觉得 GRE 更简单，而是我在申请季后期才发现各种各样奇怪但有趣的 data 项目，但那个时候再考已经来不及了。在 BA 这么爆炸的年代，也许小众 data 项目，尤其是可以和你本专业扯上关系的 data 项目不失为一个好的选择。
3. 不要因为自己是文科专业而妄自菲薄，而要挖掘自己专业和 BA 的结合点以及你自己的特点。我其实刚刚接触这个专业的那一年超自卑的，当时加了17fall的申请群，旁观了17fall 整个申请季，每天看大佬们的干货然后默默记下，但一般都不敢说话。。。后来也是在构思文书的时候突然想到，该怎样把自己的文科背景从劣势转化为优势。比如说最简单的，我们增加项目的 diversity 了呀。再比如以我的法学为例，其实有关大数据，AI 这些和法学结合的点有很多，最简单的比如是数据提供了一种不同的研究方法，我文书里也举了一个自己是如何用 Python 来分析大量离婚案件的事例，从而怎样启发自己巴拉巴拉。我了解到的情况是，国内也有很多法学院建立了类似法学大数据研究中心，人工智能法学院之类的，包括我自己的毕业论文也是有关人工智能立法的。当然这只是我本专业的例子， 是自己在法律学习（虽然不那么认真哈哈）以及在法院实习过程中感受到的可以用data 来驱动法律的例子。具体怎么结合，还是要看大家各自的专业以及自己在学习过程中的体会啦。还有一点呢也是因人而异啦，是 uci 面试的时候面试官问我有什么优点，我当时提到了一点是我是一个 quick learner（因为他们 kira 有提过他们比较看重这个），原因呢就是两年前我决定转 BA 的时候什么基础都没有，但是两年后我已经掌握了各种巴拉巴拉。。。然后再延伸。我记得当时面试官表现得很满意这个答案。总而言之呢，data 是个神奇的东西，看你想要怎么结合。我在写文书前也没想到我还能把自己以为没什么卵用的法学专业这么结合。所以文科专业的请好好 brainstorm，挖掘自己有而其他 candidate 没有的特质吧~还有就是，做到自己的最好就很好啦。
4. 在人人都有几门 Coursera 的今天，光上网课并不够，请补 project 经验，哪怕是期末大作业或者网上 project。
5. 可能我有点理想主义吧，觉得自己喜欢最重要。也知道一些法学转 CS 新闻转统计之类的大神，但还是知道自己想要什么最好~
大概就这样啦希望能给文科生们一点点帮助和信心。也麻烦大家帮忙选校。最后感谢这几年各种各样帮助过我的人和自己~
附上课程求大佬们分析
Foundations of Probability and Statistics (3.0 credit hours)
Data of many kinds are typically available in practice, but the challenge is to use those data to make effective professional decisions. This software-intensive course begins with useful descriptions of data and the probability theory foundation on which statistics rests. It continues to statistics, including the central limit theorem, which explains why data often appear to be normally distributed, and the Palm-Khintchine theorem which explains why data often appear to have a Poisson distribution. The remainder of the course focuses on regression and forecasting, including detecting and overcoming some of the deadly sins of regression, and the surprising flexibility of regression models. Recommended preparation: One semester of undergraduate calculus or consent of instructor. Offered as MSOR 433, OPRE 433 and MSBA 433.
Managerial Marketing (3.0 credit hours)
This course will emphasize how to analyze data to support and guide strategic and tactical marketing decisions relevant for supply chain managers for understanding and contributing to marketing decision-making within the firm. Many firms have extensive information, but far fewer have the expertise to act intelligently on such information. Data must be synthesized, analyzed, and interpreted before sound marketing strategies and tactical plans can be developed. The course will emphasize three key themes: (1) Market Opportunity Analysis including competitive analysis, context assessment, and customer analytics (e.g. customer profitability and lifetime value, retention and loyalty), (2) Marketing Mix Analytics including test marketing, pricing, segmentation, and response modeling, and (3) Marketing ROI including the impact of marketing decisions and plans on fundamental financial measures such as return on marketing investment and net contribution to profit. The course uses a combination of lectures, cases, and exercises.
Individual Development (1.5 credit hours)
This course is unique in the sense that its primary focus is on the student as an individual. In this course the student will get to know themselves better by completing assessments and making sense of them, having group discussions, presenting to a group as individuals, engaging in various experiential activities, conducting career interviews, attending various individual development programs and participating in two individual coaching sessions. Offered as: MSOR 485A and MSBA 485A.
Operations Management (3.0 credit hours)
Operations managers, ranging from supervisors to vice presidents, are concerned with the production of goods and services. More specifically, they are responsible for designing, running, controlling and improving the systems that accomplish production. This course is a broad-spectrum course with emphasis on techniques helpful to the practice of management at the analyst level. Its goal is to introduce you to the environments, to help you appreciate the problems that operations managers are confronted with, and provide you with the tools to address these problems. Operations Management spans all value-adding activities of an organization including product and process design, production, service delivery, distribution network and customer order management. As global competition in both goods and services increases, a firm's survival depends upon how well it structures its operations to respond quickly to changing consumer needs. Thus, it is essential for all business managers to acquire an understanding of operations management to maintain their competitive advantage. This course provides students with the basic tools needed to become an analyst in Supply Chain and Operations Management. This course provides an overview of Process analysis, Capacity management, Queuing system, analysis, Forecasting, Quality management, Material Requirements planning, Inventory management, and Supply Chain management. The emphasis of the course is on both real world applications and technical problem solving. Several manufacturing and non-manufacturing environments will be discussed explicitly, like health care, insurance, hotel-management, airlines and government related operations. Also we will explore the interface of operations management with other functional areas such as marketing, finance, accounting, etc. This coursework includes individual and group assignments, case analyses and experiential learning through simulations and educational games. Offered as: MSOR 406 and MSBA 406.
Operations Analytics: Deterministic (3.0 credit hours)
The first half of the course provides a practical coverage of linear programming, a special type of mathematical model. The art of formulating linear programs is taught through the use of systematic model-building techniques. The simplex algorithm for solving these models is developed from several points of view: geometric, conceptual, algebraic, and economic. The role and uses of duality theory are also presented. Students learn to obtain and interpret a solution from a computer package and how to use the associated output to answer What-happens-if... questions that arise in post-optimality analysis. Specific topics include: problem formulation, geometric and conceptual solution procedures, the simplex algorithm (phase 1 and phase 2), obtaining and interpreting computer output, duality theory, and sensitivity analysis. The second half of this course provide a practical approach to formulating and solving combinatorial optimization problems in the areas of networks, dynamic programming, project management (CPM), integer programming, and nonlinear programming. The art of formulating problems, understanding what is involved in solving them, and obtained and interpreting the solution from a computer package are shown. A comparison with formulating and solving linear programming problems is provided as a way to understand the advantages and disadvantages of some of these problems and solutions procedures. Recommended preparation: Knowledge of Excel, one semester each of undergraduate linear algebra and undergraduate calculus (derivatives); or consent of instructor.
Accounting and Financial Management (3.0 credit hours)
This course focuses on learning the language of business, how basic accounting information is reported and analyzed, and how basic financial principles can be applied to understanding how value is created within an enterprise. This course is intended for individuals who have a limited background in accounting, finance and business. Most of the exercises will involve evaluating and building models in Excel. Teaching objectives are fairly straightforward: 1. Provide you with a basic understanding of the key principles of accounting and finance. We will quickly cover material that is typically covered in a three-course sequence (Introductory Accounting and Finance I and II). We will fly at a fairly high level, but we want to make sure you understand the basic concepts. 2. Apply these concepts to real (but straightforward) business situations, to gain a better understanding of how companies utilize accounting and financial information. 3. Time permitting, explore how these concepts can be applied to securities, mergers and acquisitions and leveraged buyout transactions, with a specific emphasis on how these concepts are likely to surface in your role in such transactions. Offered as MSBA 410 and MSOR 410.
Operations Analytics: Stochastic (3.0 credit hours)
This course covers modeling and analysis of discrete-event dynamical systems using computer simulations. Topics include an introduction to simulation as a modeling tool, with emphasis on understanding the structure of a simulation model and how to build such models, model validation, random number generation, simulation languages, statistical simulation output analysis, design of simulation experiments and selected current research topics.
Predictive Modeling (3.0 credit hours)
Predictive modeling is a set of procedures and tools for hypothesizing, testing and validating a model to explain and predict the probability or likelihood of a future event, or outcome. A wide range of procedures and tools are available for predictable modeling, and this course will cover a select set of topics with wide applicability. Through applications and case studies involving real-life data, the course will emphasize managerial problem solving. To build models is to capture managerial problem formulation, and to test/validate them is to confront managerial hypotheses with empirical observations. Problem solving is a creative act rooted in validated evidence of managerial hypotheses testing.
Team Development (1.5 credit hours)
This course is unique in the sense that its primary focus is on the student working in teams. In this course the student will assess their team interaction based on team assignments simulated and action learning type projects, presenting to the class as a team, engaging in various experiential activities, participating one team coaching session, working with a team, and expanding their knowledge of team leadership and membership skills and abilities. They are also expected to engage with projects external to the university (similar to an action learning project). Offered as: MSOR 485B and MSBA 485B.
Advanced Marketing Analytics (3.0 credit hours)
In order to improve decision making in various decision areas of marketing like segmentation, positioning, advertising, sales promotions, new product development and pricing, use of quantitative data and analysis has become very popular. Among the strategic roles for measurement are evaluation and control. At the same time, marketing managers have been challenged by top managers' demand to show the value of marketing expenditures to an organization's financial well-being. This course will introduce a variety of data based decision-aids in the marketing area. In addition, the course will also introduce SAS to you. SAS is a very popular tool that analysts in business and economics field have been using for decades now.
Data Mining & Visualization (3.0 credit hours)
Data Mining is the process of identifying new patterns and insights in data. As the volume of data collected and stored in databases grows, there is a growing need to provide data summarization (e.g., through visualization), identify important patterns and trends, and act upon the findings. Insight derived from data mining can provide tremendous economic value, often crucial to businesses looking for competitive advantages. This course is a survey of data visualization methods, supervised and unsupervised learning techniques, and modern tools for discovering knowledge for business decisions.
Marketing Models & Digital Analytics (3.0 credit hours)
Models & analytics suitable for digital marketing data are the focus of this course. The objective to develop analytical skills for making intelligent decisions about marketing investments that create value and build competitive advantage. In short, to build capabilities for marketing ai-analytics for insights. The course content and assignments are designed to (a) enable student learning by using real- world problems and data, (b) emphasize the Problem-Data-Analytics interdependence for effective problem solving, and (c) engage with thoughtful practitioners of digital data analytics to inform current practices and opportunities.
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