金融工程，金融数学，统计学，计量经济学，量化金融学，量化经济学，计算金融，会计与金融，傻傻分不清？
摘要：
对于想投身金融行业的莘莘学子，望子成龙，望女成凤的家长们来说，选择出最适合的职业方向是人生中最重要的一件事情之一，job satisfaction is , indeed , the prerequisite of life satisfaction, 尤其是在这个“不能依靠别人”的社会，只有自身的职业定位准确，做出很好的职业成就的人，才能真正拥有“幸福的人生”！ 即使很多人到了而立之年，仍然会很后悔，自己在年轻人的时候，没有找到“最匹配自身性格发展”的the best match 的specialty and career ladder.
“鸥思网”将致力于在“繁综复杂”的细分行业中，通过quantitative analysis ，结合情感的灵活性，来尽可能的帮助“迷茫中”的小伙伴们找到职业的方向。
本期我们将为各位详细分析在金融行业的若干细节方向，金融数学，统计学，金融学，风险管理，计量金融，计量经济学，量化金融学，量化经济学，计算金融，会计与金融，傻傻分不清？我们将甄别出这些细节方向会有哪些不同？ 即使细微的差别，对于一个人来说，也往往会带来深远的影响。因为对于大部分普通人来说，choice prevails action.
接下来，将以下行业做详细分析：
第一组：金融工程 VS 金融学
In summary, the MFE is for quant roles (quant trader at a hedge fund) and is meant for math/science/engineering majors looking to get into finance. If you haven't taken up to Calc 3, forget about this program as you will not be able to keep up with the coursework, let alone do well enough to secure an offer. This degree is 'better' if you have a strong math/programming background and are looking for a job at a hedge fund, for example.
On the other hand, the MSF is for all other roles within finance that do not require a quant background. This includes corporate finance, consulting, and banking. This degree is 'better' if you majored in economics or another liberal arts major and are looking to break into investment banking.
总之，MFE金融工程适用于量化角色（对冲基金的量化交易员），适用于希望涉足金融业的数学/科学/工程专业。如果您还没有掌握高阶数学Calc 3，请不要考虑此程序，因为您将无法跟上课程的进度，更不用说做得足够好以确保录取了。如果您具有较强的数学/编程背景并且正在寻找对冲基金的工作，则该学位会“更好”。核心课程包括：运筹学，复变函数、数值分析、数学建模、实变函数；
注意：一般金融工程是属于工程学院申请。去美国加拿大等名校需要提供GRE成绩。
另一方面，MSF金融硕士适用于不需要定量背景知识的金融中的所有其他角色。这包括公司融资，咨询和银行业务。如果您主修经济学或其他文科专业，并希望涉足投资银行业务，则该学位“更好”。具有普通数学水平即可。
注意：一般金融硕士是属于商学院，MBA等项目里面申请。名校需要提供GMAT成绩首选，当然部分学校GRE可以取代GMAT.
第二组：金融工程，金融计算和计算金融学？
financial engineering, Financial computing and Computational finance?
Computational Finance is the application of Mathematical Financial theory using applicable software. Financial computing and Computational finance are both very similar and cross paths in many modules. However, Financial Computing favours programming over mathematical finance; and vice versa.
计算金融是使用适用软件的数学金融理论的应用。金融计算和计算金融在许多模块中都是非常相似且交叉的路径。但是，金融计算相对于数学金融更喜欢编程。反之亦然。
Financial Engineering shows a rather different picture, which is a wide subject. Financial Engineering is the manipulation of assets and liabilities in order to create a favorable“stance” for a company with the purpose of leverage, or borrowing, advantages. Most of them are made possible by the results coming from both numerical and analytical efforts.
金融工程显示的情况截然不同，是比较宽的专业。金融工程是操纵资产和负债，以便为利用杠杆或借贷优势的公司创造有利的“立场”。这些结果往往通过数值和分析结果来实现。
Generally speaking, financial computing or computational finance is going to be a more programming-focused curriculum while financial engineering will be slightly more financially focused, while the financial computing programs would more specifically set you up for programming-intensive roles in high-frequency trading or modeling positions.
一般而言，金融计算或计算金融将是更注重编程的课程，而金融工程将更加注重金融，而金融计算程序员将更专门用来解决设置高频交易或建模中的编程密集型角色的需要 。
Financial Computing comprises both Computational Finance (algorithmic trading, risk management, market simulation, portfolio optimization) and Financial IT (financial software engineering, cloud computing, GPU and scalable high-performance computing), providing taught courses at undergraduate (BSc, MEng) and postgraduate (MSc) levels, and training in research at PhD level.
金融计算包括计算金融（算法交易，风险管理，市场模拟，投资组合优化）和金融IT（金融软件工程，云计算，GPU和可扩展的高性能计算），在本科生（BSc，MEng）和研究生中提供授课课程 （MSc）水平，以及博士学位水平的训练。
注意：金融计算一般是计算机学院（至少是工程学院）提供的课程。
第三组：计量经济学，统计学，机器学习？
econometrics, statistics, and machine learning?
In comparing econometrics, statistics, and machine learning methodologies, one must distinguish between standard and advanced machine learning. The former, exemplified by deep learning and neural networks, fits a function to a stream of data and plays the same role as statistical analysis, taking us from samples to properties of distribution functions. Advanced machine learning, on the other hand, goes beyond distributions onto the process that generates the data, and so, allows us to manage policy interventions and counterfactual reasoning (e.g., "what if we have done things differently").Advanced machine learning was developed in artificial intelligence in the past 25 years, under the rubric "causal inference" or "structural causal models" (SCM).
在比较计量经济学，统计学和机器学习方法时，必须区分标准机器学习和高级机器学习。前者以深度学习和神经网络为例，它使函数适合于数据流，并起着与统计分析相同的作用，使我们从样本到分布函数的性质。另一方面，高级机器学习不仅仅局限于分布到生成数据的流程上，因此使我们能够管理政策干预措施和反事实推理（例如，“如果我们做的事情有所不同”怎么办”）。在过去的25年中，以“因果推论”或“结构因果模型”（SCM）为标题在人工智能中得到了发展。
Econometrics and statistics have same goals of measurement Econonomic theory which they make Empirical evendence of that theory.Econometrics is statistical application to esimate qualitative results,While statistics is descriptive and inferentials .The difference may econometrics is used Models,through Point of estimation and inferentials hypothesis,meanwhile statistics is almost descriptive such as:Mean,medium,mode And devaition.
计量经济学和统计学具有计量经济学理论的相同目标，它们使该理论具有经验上的均等性。计量经济学是用于模拟定性结果的统计学应用，而统计学是描述性和推论性的。而统计数据几乎是描述性的，例如：平均值，中位数，模式和偏差。
The discipline of statistics was born out of a desire to work with data efficiently, primarily by drawing relatively small samples from larger populations of interest instead of collecting data on everyone. Statistics quantifies this uncertainty by reporting probabilities rather than claiming to discover the absolute truth behind an underlying statistic. Econometricians shares machine learners’ interest in classification and prediction, as well as statisticians’ concern with sample representativeness and sampling variance. We’re distinguished, however, by a longstanding focus on causal effects, especially the consequences of economic decisions and social policy.
统计学科源于对数据进行有效处理的愿望，主要是通过从较大的关注人群中抽取相对较小的样本，而不是收集每个人的数据。统计数据通过报告概率来量化这种不确定性，而不是声称发现潜在统计信息背后的绝对真相。计量经济学家分享了机器学习者对分类和预测的兴趣，以及统计学家对样本代表性和样本方差的关注。但是，我们的突出特点是长期关注因果关系，尤其是经济决策和社会政策的后果。
第四组：量化经济学，量化金融学，会计与金融？
Financial economics，Quantitative finance，Accounting and finance？
“Financial economics” should probably be thought of as the overarching foundation of financial research. Technically, ‘finance’ is an applied subfield of microeconomics, and so ‘financial economics’ would be looking at finance from the perspective of that economic foundation.
“金融经济学”应该被视为金融研究的总体基础。从技术上讲，“金融”是微观经济学的一个应用子领域，因此“金融经济学”将从这种经济基础的角度来研究金融。
“Quantitative finance” is usually thought of as an industry application of in-depth econometrics and mathematical modeling. Whereas academic finance has greater emphasis on causality (search for truth and all that), industry quant finance is really simply about "What will make the most money?".
通常将“定量金融”视为深度计量经济学和数学建模的行业应用。尽管学术金融更加强调因果关系（寻找真相等），但行业量化金融实际上只是关于“什么能赚最大的钱？”
Financial Economics has significant overlap with many quant finance programs, and was intended to prepare students for essentially the same employment prospects. In short, "financial economics" is usually simply more broad a term, while "quantitative finance" is more specific.
``金融经济学与许多定量金融计划有很多重叠之处，旨在使学生为基本上相同的就业前景做好准备。简而言之，“金融经济学”通常只是一个更广义的术语，而“定量金融”则更具体。
Accounting and finance can make a lot of money and practitioners might not have to be such a nerd, if you can however obtain a quantitative background, it will only act as benefit in your later financial career. Even if you would like to remain in accounting in Finance. Accounting and quantitative finance graduates maybe end up accountants, others as quants and investment bankers. Ultimately they all ended up with good salary and good jobs.
会计和财务可以赚很多钱，而从业者不一定非要那么书呆子，但是，如果您能够获得一定数量的背景知识，那么它只会在您以后的财务职业中受益。即使您想继续从事财务会计工作。会计和定量金融专业的毕业生可能最终会成为会计师，其他人则是定量和投资银行家。最终他们都有高薪和好工作。
Quantitative finance and accounting are actually not directly related to economics, and econometrics is merely a tool for empirically analyzing economic activities and events. However, you'll need to take several mathematics and statistics (not the same as econometrics) courses in order to be considered for most masters and PhD programmes in economics. someone prefer the more qualitative aspect of economics (which is good), but having some statistical skills will help your career as an economist.
数量金融和会计实际上并不与经济学直接相关，计量经济学只是用于对经济活动和事件进行实证分析的工具。但是，您需要修几种数学和统计学（与计量经济学不同的课程），才能被经济学的大多数硕士和博士学位课程所考虑。有人更喜欢经济学的质性方面（这很好），但是拥有一些统计技能将有助于您成为经济学家。
注意：会计比较适合性格稳重，为人谨慎的学生，尤其是女生学习，而量化金融更适合有“有野心”的同学申请，而量化经济学主要是为Ph.D或者对于学术比较感兴趣的未来经济学家来申请学习。看似很类似的专业，其实对于人的性格，能力要求都很大。
