一年后更新：
在家里著手更新簡(jiǎn)歷，整理實(shí)習(xí)時(shí)用到的資料，為構(gòu)思今后面試會(huì)被cue到的storyline和insights絞盡腦汁。空閑時(shí)間，隨手寫一篇文，聊聊實(shí)習(xí)這些天來(lái)，我在想些什么。
滿打滿算，我在BDA做PTA的總時(shí)間不到十天。正常實(shí)習(xí)都有一個(gè)固定的周期，一般是三個(gè)月起步，但BDA他家很特別，所有的PTA都根據(jù)全職員工招人的項(xiàng)目時(shí)間來(lái)定，非常靈活。在不到半個(gè)月的時(shí)間里，我跟進(jìn)了兩個(gè)項(xiàng)目，比價(jià)和survey。
PTA沒(méi)有機(jī)會(huì)進(jìn)入BDA的辦公地點(diǎn)，而是會(huì)被安置在國(guó)貿(mào)大飯店的共享際辦公空間，毗鄰中金辦公室。這里有BDA租下的一些辦公空間，專門用來(lái)安置PTA和帶我們的leader。
leader是當(dāng)初在PTA pool群里招募PTA的全職員工，他們都很年輕，剛?cè)肼毑痪谩１热纾瑂urvey項(xiàng)目的leader去年才社招加入BDA。年輕的junior和我們一樣，每天大部分時(shí)間都在這些外包的辦公室工作，坐在我們旁邊。
咨詢公司的PTA無(wú)非就是做些dirty work，臟活累活。比價(jià)就是不停地ctrl c，ctrl v，把一些數(shù)據(jù)扒到excel里，然后互相QC（quality control），中國(guó)話叫檢查。搞完了一個(gè)篇目以后打包發(fā)送給leader，他再做一些數(shù)據(jù)分析。據(jù)說(shuō)我們?nèi)MPTA兩個(gè)星期辛苦做出來(lái)的整個(gè)項(xiàng)目數(shù)據(jù)，在項(xiàng)目最終的成品報(bào)告里，占據(jù)不到一頁(yè)紙。
survey輕松一些，就是打電話。依照預(yù)先設(shè)計(jì)好的調(diào)查問(wèn)卷，用虛擬平臺(tái)撥打電話做客戶訪談。訪談講究效率，我從一開(kāi)始愿意聽(tīng)聽(tīng)客戶嘮嗑，到學(xué)會(huì)即時(shí)掐斷客戶的閑話，只用了不到一天時(shí)間。
btw，這個(gè)survey項(xiàng)目迫切需要三線以下城市的樣本做調(diào)研，但是leader哥哥和我們這些PTA全部來(lái)自一二線城市或者海外，沒(méi)有人connect得到中國(guó)最廣大的下沉市場(chǎng)。
矛盾點(diǎn)正在于此。BDA的PTA，不會(huì)密切跟進(jìn)項(xiàng)目，而是自始至終都在干同一種活。比如你報(bào)名參加了持續(xù)兩個(gè)星期的比價(jià)項(xiàng)目，那這兩個(gè)星期你都在ctrl c、ctrl v；又比如你去做了survey，那么從早到晚你都在打電話。
優(yōu)點(diǎn)在于，只要你把整個(gè)流程都熟悉摸透，只干幾天和干幾個(gè)月都是同樣的效果，因?yàn)檫@些都是重復(fù)性的機(jī)械工作。
然而缺點(diǎn)是致命的。這些重復(fù)性的dirty work，已經(jīng)dirty到了連簡(jiǎn)歷都寫不出來(lái)的地步。實(shí)習(xí)幾天里，我向同期的北大，對(duì)外經(jīng)貿(mào)的同學(xué)討教了這段經(jīng)歷的寫法，還得到了一些不錯(cuò)的模板。
但是，一旦面試官問(wèn)我“tell me your experience at BDA consulting firm”或者“show me your insights in this field”，我立馬就呆若木雞。誠(chéng)然，我總不能說(shuō)我熟練掌握復(fù)制黏貼快捷鍵，午餐晚餐時(shí)經(jīng)常和其他實(shí)習(xí)生朋友一起討論how to deal with leader的話題吧。
同期的伙伴倒是給我們敲了一個(gè)警鐘。他說(shuō)認(rèn)識(shí)一個(gè)同學(xué)，以前也做過(guò)BDA的PTA，后來(lái)去面字節(jié)的戰(zhàn)投部，面試官對(duì)他在BDA的PTA經(jīng)歷很感興趣，一直追著深挖細(xì)節(jié)。同學(xué)經(jīng)受不住，最終老實(shí)交代了做dirty work的真相。
面試官說(shuō)，就是這些了？你對(duì)這個(gè)項(xiàng)目有沒(méi)有一些自己的見(jiàn)解？同學(xué)說(shuō)，就是這些了。面試官隨即在面試意見(jiàn)中寫，該同學(xué)缺乏獨(dú)立思考能力，在實(shí)習(xí)中不主動(dòng)思考。隨即被刷。
（btw，一天后得到消息，字節(jié)戰(zhàn)投部被裁撤了，不用再為面字節(jié)戰(zhàn)投而考慮了）
我覺(jué)得好笑。這個(gè)面試官但凡學(xué)生時(shí)代也做過(guò)BDA的PTA，就應(yīng)該知道這個(gè)問(wèn)題本身十分無(wú)聊。我即使帶著腦子來(lái)這里上班，最終也會(huì)淪為一個(gè)不加思考的機(jī)器。
所有和項(xiàng)目相關(guān)的信息PTA都接觸不到。我們不知道客戶是誰(shuí)，不知道這個(gè)項(xiàng)目的目的是什么，甚至不知道這個(gè)項(xiàng)目的全貌如何。以我們PTA團(tuán)隊(duì)所做的不到一頁(yè)紙的功勞，猜測(cè)項(xiàng)目的全貌無(wú)異于盲人摸象。
不僅我們，連我們的leader可能也看不到項(xiàng)目的全貌。他們每天做的事，不比我們高級(jí)多少。我們提交數(shù)據(jù)結(jié)果給他們，他們做一個(gè)最終的QC，然后做一些數(shù)據(jù)分析處理的工作。如果他們QC出錯(cuò)誤，就遣返至我們這里要求訂正；如果他們發(fā)現(xiàn)數(shù)據(jù)不利于呈現(xiàn)結(jié)果，就遣返至我們這里要求美化。
另外一個(gè)阻礙我思考的是工作時(shí)長(zhǎng)。10點(diǎn)前上班打卡，理論晚上8點(diǎn)下班打卡，即算當(dāng)天加班完畢，得到350元報(bào)酬（不加班六點(diǎn)下班，250一天）。但是遇到leader很push時(shí)，九點(diǎn)半到十點(diǎn)半下班如同家常便飯。
據(jù)我的觀察，全職員工下班更晚。帶我們的leader，這些junior consultant，每天都在凌晨一兩點(diǎn)鐘下班（一兩點(diǎn)下班，不是一兩點(diǎn)睡覺(jué)）。
實(shí)習(xí)期間的某天晚上，我刷短視頻刷的上頭，一不留神，發(fā)現(xiàn)已經(jīng)凌晨?jī)牲c(diǎn)半。翻一下微信，發(fā)現(xiàn)leader還在項(xiàng)目群里發(fā)文件，@相關(guān)的同學(xué)布置任務(wù)。據(jù)說(shuō)項(xiàng)目沖刺階段，consultant甚至凌晨三四點(diǎn)才下班。
實(shí)習(xí)過(guò)后就知道，這樣的工作狀態(tài)，是我所不能接受的。哪怕BDA的起薪比MBB都高，哪怕應(yīng)屆入職年薪就有五六十萬(wàn)，哪怕工作三到五年后可以獨(dú)立帶項(xiàng)目，哪怕在30歲之前年薪就可以妥妥地上百萬(wàn)。這樣的hours，都是我不能夠接受的。
我可以接受每天都12點(diǎn)左右睡覺(jué)，但我不能接受哪怕有一小段日子需要凌晨2、3點(diǎn)，甚至3、4點(diǎn)睡覺(jué)。12點(diǎn)之后睡覺(jué)只是亞健康狀態(tài)，長(zhǎng)期1、2點(diǎn)以后睡覺(jué)會(huì)直接折損陽(yáng)壽。“努力奮斗”一詞我舉雙手贊成，但是想到要用身體健康，甚至生命作賭注，我就不愿意了。
況且，高強(qiáng)度的工作，不給人喘息，又何來(lái)時(shí)間讓人進(jìn)行反思，進(jìn)行工作以外的學(xué)習(xí)精進(jìn)呢？
考慮到這些，職業(yè)規(guī)劃感覺(jué)越來(lái)越清晰了。找工作一定考慮的是性價(jià)比，而不能單看絕對(duì)薪酬。我敢肯定，在21世紀(jì)20年代，人不可能單靠為公司打工就能實(shí)現(xiàn)財(cái)務(wù)自由，即使他在投行咨詢這些給pay頂流的行業(yè)；同樣，人也不可能期望從一家公司的底層一路能干到合伙人級(jí)別，因?yàn)橹懈邔釉缫岩粋€(gè)蘿卜一個(gè)坑。
想財(cái)務(wù)自由，或是想做出更大的事業(yè)，創(chuàng)業(yè)是唯一的通路；要么就考慮性價(jià)比，不要單純追求誰(shuí)家的工資高。在國(guó)企和事業(yè)單位的發(fā)展未必不如私企，體制內(nèi)的發(fā)展未必不如體制外，大家只是在不同的賽道而已。
寫到這里，我萌發(fā)出一點(diǎn)感嘆。
咨詢真的是一個(gè)很神奇的行業(yè)。尤其以MBB為代表的management consulting，作為舶來(lái)品從美國(guó)波士頓席卷世界，也隨著中國(guó)市場(chǎng)化經(jīng)濟(jì)的改革而出現(xiàn)在華夏大地，一直以一種售賣IQ，售賣brain wise的形象示人。所有consultant都西裝革履，實(shí)地駐扎本土企業(yè)，從總裁辦對(duì)話CEO，到車間技術(shù)間對(duì)話基層員工，然后兜售各種戰(zhàn)略模型，化身business coach，為企業(yè)量身定做畫一套deck，拿天價(jià)的咨詢費(fèi)用；
但另一面，咨詢行業(yè)進(jìn)入壁壘又幾乎為零。consulting opens up to nearly all major.不管你專業(yè)是經(jīng)管，理工，文史，甚至藝術(shù)，只要你case interview secrets背的熟，case mock的好，excel和ppt的快捷鍵用的溜，那么頂尖咨詢公司對(duì)你的唯一要求，就剩下名校title了。前文提到的熬夜到兩三點(diǎn)鐘，無(wú)非就是當(dāng)一個(gè)excel boy/girl，或者畫一畫ppt。很難讓人覺(jué)得咨詢行業(yè)能夠產(chǎn)生價(jià)值。
咨詢還有一個(gè)特質(zhì)，它非常吸引留學(xué)背景的同學(xué)。可能行業(yè)的觀感賦予了consulting十足的魅力。深度對(duì)話業(yè)內(nèi)公司合伙人，show how smart you are，持續(xù)跟蹤了解不同的商業(yè)模式，會(huì)帶來(lái)一種非常fancy的體驗(yàn)。
更何況，從理論上，咨詢給員工帶來(lái)一種未來(lái)有無(wú)限可能的前景：深耕咨詢行業(yè)，以后可以跳槽去甲方公司做戰(zhàn)略，可以跳去PEVC做一級(jí)市場(chǎng)投融資，甚至在積累了無(wú)數(shù)business case后，你可能會(huì)想親身下場(chǎng)創(chuàng)業(yè)，將心中燃燒已久的business plan落地，打破薪酬天花板，畢竟小紅書毛文超的案例也在前方激勵(lì)著各位consultant。
上述職業(yè)路徑我不便過(guò)多評(píng)論，但是我一直覺(jué)得：對(duì)于2000年后出生的一代來(lái)說(shuō)，很多窗口已經(jīng)越來(lái)越小了。
survey組里有位在Arizona讀書的姐姐，曾經(jīng)在四大某家咨詢做了兩個(gè)月的日常實(shí)習(xí)，目前在羅蘭貝格做了六個(gè)月的PTA，還在蔚來(lái)汽車做戰(zhàn)略。
她回憶起在四大和羅蘭貝格做PTA的艱苦情景，告訴我們那些地方都學(xué)不到什么東西，全是dirty work，和BDA的PTA半斤八兩。我承認(rèn)“學(xué)得到/學(xué)不到東西”這種問(wèn)題見(jiàn)仁見(jiàn)智，如同小馬過(guò)河。但對(duì)方的描述，大概讓人有了一個(gè)底。
當(dāng)然，她還講了一些實(shí)習(xí)中的fun facts，比如在羅蘭貝格做PTA，周末加班還不給錢；蔚來(lái)汽車很好，空間舒適工作輕松，公司給她分了一間辦公室，她在里面給羅蘭貝格畫ppt。
我認(rèn)識(shí)一個(gè)北師大數(shù)院的師姐，在去年申請(qǐng)到了畢馬威的精英計(jì)劃，選擇了喜歡的咨詢條線。她對(duì)我說(shuō)，招人的hr很開(kāi)心，說(shuō)招的這一批全都是理工背景，沒(méi)有一個(gè)純商科的。
可是，最近從師姐朋友圈里看到，在咨詢口最后的定崗中，師姐被分去做了運(yùn)營(yíng)。我承認(rèn)運(yùn)營(yíng)也有獨(dú)特的發(fā)展，但這和師姐當(dāng)初想做的咨詢業(yè)務(wù)差了十萬(wàn)八千里，甚至簡(jiǎn)歷都完全不知道該怎么寫了。
商科求職越來(lái)越偏好理工背景是不爭(zhēng)的事實(shí)，但一邊慶祝理工背景的加入，一邊隨意分配給大家毫無(wú)含金量的非核心崗，這完全在利用信息差坑害辛辛苦苦打牢數(shù)理基礎(chǔ)的同學(xué)。
一言以蔽之，沒(méi)有壁壘的行業(yè)反而是最難的。當(dāng)人沒(méi)有了核心競(jìng)爭(zhēng)力，就只能靠長(zhǎng)時(shí)間的工作，熬夜，來(lái)?yè)Q取收入。或者出現(xiàn)資源型合伙人一家獨(dú)大的局面。
不僅咨詢，四大，甚至國(guó)內(nèi)以合規(guī)業(yè)務(wù)為主的投行，PEVC（主要指VC），都是沒(méi)有壁壘的行業(yè)，或者說(shuō)，它們的壁壘是從業(yè)經(jīng)驗(yàn)。它們往往伴隨著很差的hours，很多的dirty work。
更廣義地說(shuō)，整個(gè)一級(jí)市場(chǎng)都更注重平臺(tái)和資源的壁壘，而二級(jí)市場(chǎng)更注重個(gè)人能力的壁壘；一級(jí)市場(chǎng)更重視法律和會(huì)計(jì)，二級(jí)市場(chǎng)更偏好數(shù)理基礎(chǔ)。
慢慢來(lái)咯，正如survey組里另一位同學(xué)一樣。她是廣州人，在華南理工讀本科，寒假到北京玩，抽出一段時(shí)間來(lái)BDA做onsite PTA。她說(shuō)，實(shí)習(xí)就當(dāng)是旅行的一部分。
好呀，我也把對(duì)前路的探索當(dāng)作是生活的一部分好了。但不管怎么樣，hours太差的地方我是不會(huì)干的，我想睡覺(jué)。
In the world of finance, the term BDA has gained significant attention in recent years. BDA stands for Big Data Analytics, and it refers to the process of collecting, analyzing, and interpreting large sets of financial data to gain insights and make informed decisions.
In finance, data plays a crucial role in decision-making. However, with the increasing volume and complexity of data, traditional methods of analysis have become inadequate. This is where BDA comes into play.
BDA is a technology-driven approach that leverages advanced analytics techniques to extract meaningful patterns, trends, and correlations from massive amounts of data. It involves the use of specialized software and tools to process structured and unstructured data from various sources, such as market data, financial statements, social media, and more.
By applying statistical models, machine learning algorithms, and artificial intelligence, BDA enables financial institutions to uncover valuable insights that can drive strategic decision-making, risk management, fraud detection, and customer relationship management.
BDA has revolutionized the finance industry by providing a more accurate and comprehensive understanding of market trends, customer behavior, and financial risks. Here are some key reasons why BDA is important in finance:
As technology continues to advance, BDA is expected to play an even more significant role in the finance industry. With the proliferation of digital platforms, mobile banking, and online transactions, the amount of data generated is growing exponentially.
This presents both challenges and opportunities for financial institutions. BDA will be crucial in efficiently processing and analyzing this vast amount of data, enabling quick decision-making, risk management, and innovation.
In conclusion, BDA, or Big Data Analytics, is a game-changer in the finance industry. It empowers financial institutions with data-driven decision-making, enhanced risk management, improved customer experiences, fraud detection, and compliance with regulatory requirements. As the demand for data-driven insights grows, BDA will continue to shape the future of finance.
Thank you for reading this article on the meaning of BDA in finance. We hope it has provided you with valuable insights into the importance of Big Data Analytics in the finance industry.
大數(shù)據(jù)分析（BDA）是券商中一項(xiàng)重要的部門，扮演著至關(guān)重要的角色。在當(dāng)今信息爆炸的時(shí)代，數(shù)據(jù)已經(jīng)成為決策制定和業(yè)務(wù)發(fā)展的關(guān)鍵。隨著金融行業(yè)的不斷發(fā)展，券商也越來(lái)越重視數(shù)據(jù)分析，以幫助他們更好地了解市場(chǎng)趨勢(shì)、客戶需求和風(fēng)險(xiǎn)管理等方面。
BDA是券商什么部門 這個(gè)問(wèn)題關(guān)乎整個(gè)企業(yè)的運(yùn)作和發(fā)展。大數(shù)據(jù)分析可以幫助券商更好地理解客戶行為模式，從而提供個(gè)性化的服務(wù)和產(chǎn)品。通過(guò)分析市場(chǎng)數(shù)據(jù)和資產(chǎn)波動(dòng)，券商可以更準(zhǔn)確地預(yù)測(cè)未來(lái)市場(chǎng)走勢(shì)，從而做出更明智的投資決策。此外，大數(shù)據(jù)分析還能幫助券商發(fā)現(xiàn)潛在的風(fēng)險(xiǎn)，并采取相應(yīng)的措施來(lái)規(guī)避這些風(fēng)險(xiǎn)，保障企業(yè)的穩(wěn)健發(fā)展。
在競(jìng)爭(zhēng)激烈的金融市場(chǎng)中，券商需要不斷創(chuàng)新和提升自身的競(jìng)爭(zhēng)力。借助大數(shù)據(jù)分析，券商能夠更好地洞察市場(chǎng)動(dòng)態(tài)，優(yōu)化業(yè)務(wù)流程，提升客戶滿意度，實(shí)現(xiàn)可持續(xù)發(fā)展。因此，BDA部門在券商中的地位日益凸顯。
大數(shù)據(jù)分析技術(shù)不僅可以幫助券商更好地了解市場(chǎng)和客戶，還能夠提升企業(yè)的運(yùn)營(yíng)效率。通過(guò)分析海量數(shù)據(jù)，券商可以發(fā)現(xiàn)業(yè)務(wù)流程中的瓶頸和問(wèn)題，進(jìn)而采取針對(duì)性的措施進(jìn)行優(yōu)化，提高工作效率。此外，大數(shù)據(jù)分析還可以自動(dòng)生成報(bào)表和分析結(jié)果，減輕員工的工作負(fù)擔(dān)，讓他們更專注于策略性的工作。
另外，BDA部門還可以利用數(shù)據(jù)挖掘和機(jī)器學(xué)習(xí)算法，幫助券商發(fā)現(xiàn)市場(chǎng)中的商機(jī)和潛在風(fēng)險(xiǎn)。通過(guò)建立預(yù)測(cè)模型和風(fēng)險(xiǎn)評(píng)估模型，券商可以更加準(zhǔn)確地預(yù)測(cè)市場(chǎng)走勢(shì)，及時(shí)調(diào)整投資組合，提升投資收益率。
以某券商為例，他們利用大數(shù)據(jù)分析技術(shù)對(duì)客戶數(shù)據(jù)進(jìn)行深度挖掘，發(fā)現(xiàn)了不同客戶群體的特點(diǎn)和需求。基于這些數(shù)據(jù)分析結(jié)果，券商對(duì)客戶進(jìn)行分類管理，并推出了一系列個(gè)性化服務(wù)，大大提升了客戶滿意度和忠誠(chéng)度。
此外，該券商通過(guò)大數(shù)據(jù)分析，建立了風(fēng)控模型和資產(chǎn)配置模型，幫助客戶規(guī)避投資風(fēng)險(xiǎn)，實(shí)現(xiàn)資產(chǎn)的穩(wěn)健增值。這些成功的應(yīng)用案例充分展示了BDA部門在券商中的重要作用，以及大數(shù)據(jù)分析技術(shù)在金融行業(yè)中的巨大潛力。
綜上所述，BDA是券商什么部門 已經(jīng)成為券商發(fā)展不可或缺的重要組成部分。大數(shù)據(jù)分析技術(shù)的應(yīng)用，不僅可以幫助券商更好地了解市場(chǎng)和客戶，提升企業(yè)的競(jìng)爭(zhēng)力，還可以幫助券商優(yōu)化業(yè)務(wù)流程，提升工作效率。
隨著科技的不斷進(jìn)步和信息化的深入發(fā)展，大數(shù)據(jù)分析技術(shù)在券商中的應(yīng)用將會(huì)越來(lái)越廣泛，為券商帶來(lái)更多發(fā)展的機(jī)遇和挑戰(zhàn)。因此，券商需要不斷加強(qiáng)對(duì)BDA部門的重視，投入更多資源和人力，推動(dòng)大數(shù)據(jù)技術(shù)在券商中的深入應(yīng)用，實(shí)現(xiàn)企業(yè)的可持續(xù)發(fā)展。
開(kāi)機(jī)藍(lán)屏的原因有幾種 開(kāi)機(jī)啟動(dòng)項(xiàng)問(wèn)題 處理方案：點(diǎn)擊開(kāi)始----運(yùn)行----msconfig----看下啟動(dòng)里面，勾選都去掉 ，然后點(diǎn)擊確定開(kāi)機(jī)再試下 內(nèi)存條問(wèn)題 處理方案：將內(nèi)存條拔出，用橡皮擦擦金手指的位置，然后插上內(nèi)存之后開(kāi)機(jī)試試 顯卡問(wèn)題 處理方案：
（1）重裝顯卡驅(qū)動(dòng) （2）重新插拔顯卡硬件 （3）檢修顯卡硬件 病毒問(wèn)題 處理方案：用殺軟進(jìn)入安全模式全盤查殺 系統(tǒng)問(wèn)題 處理方案：檢查開(kāi)機(jī)啟動(dòng)項(xiàng)是否有異常的東西，如果沒(méi)有的話，重裝系統(tǒng) 硬盤問(wèn)題 處理方案：檢查硬件是否有壞道。 重裝系統(tǒng)的方法：
1、使用其他電腦下載工具制作pe引導(dǎo)工具（一般下載通用pe5.0）
2、下載系統(tǒng)鏡像文件 3、打開(kāi)下載好的工具，選擇一鍵制作制作u盤pe引導(dǎo)啟動(dòng)工具，將鏡像文件拷貝到u盤上 4、使用U盤pe引導(dǎo)啟動(dòng) 5、格式化系統(tǒng)盤 6、使用鏡像文件重做系統(tǒng)。
cda和bda數(shù)據(jù)分析師都好
BDA數(shù)據(jù)分析師是由教育部主辦的“調(diào)查分析師”升級(jí)而來(lái)，傳承統(tǒng)計(jì)分析和數(shù)據(jù)挖掘技術(shù)的專業(yè)性。BDA在國(guó)內(nèi)外市場(chǎng)研究行業(yè)廣泛認(rèn)同，證書頒發(fā)機(jī)構(gòu)是中國(guó)信息協(xié)會(huì)市場(chǎng)研究業(yè)分會(huì)，是由經(jīng)濟(jì)、科技、社會(huì)等領(lǐng)域?qū)＜覉F(tuán)體組成，經(jīng)國(guó)務(wù)院同意和民政部批準(zhǔn)成立的全國(guó)性社團(tuán)組織，是市場(chǎng)調(diào)查、市場(chǎng)研究、數(shù)據(jù)分析、數(shù)據(jù)挖掘、數(shù)據(jù)洞察領(lǐng)域的專業(yè)行業(yè)協(xié)會(huì)組織。
“CDA數(shù)據(jù)分析師”，是在數(shù)字經(jīng)濟(jì)大背景和人工智能時(shí)代趨勢(shì)下，面向全行業(yè)的專業(yè)權(quán)威國(guó)際資格認(rèn)證，旨在提升全民數(shù)字技能，助力企業(yè)數(shù)字化轉(zhuǎn)型，推動(dòng)行業(yè)數(shù)字化發(fā)展。“CDA數(shù)據(jù)分析師”具體指在互聯(lián)網(wǎng)、金融、零售、咨詢、電信、醫(yī)療、旅游等行業(yè)專門從事數(shù)據(jù)的采集、清洗、處理、分析并能制作業(yè)務(wù)報(bào)告、提供決策的新型數(shù)據(jù)分
In today's constantly evolving financial landscape, it can be challenging to stay abreast of all the different investment options available. One term that has gained traction in recent years is BDA finance. But what exactly is BDA finance, and how does it work? In this article, we will delve into the basics of BDA finance, exploring its structure, operations, and benefits.
BDA finance stands for Business Development Company. It is a type of closed-end investment company that primarily focuses on providing capital and financial support to small and mid-sized businesses. Similar to a mutual fund or a private equity firm, a BDA operates by pooling funds from individual and institutional investors. These funds are then invested in various companies to enable their growth and expansion.
A BDA is structured as a corporation and is regulated under the Investment Company Act of 1940 in the United States. It must meet certain requirements to qualify as a BDA. For example, at least 70% of its total assets must be invested in private or public companies with market values of under $250 million.
BDAs can be internally or externally managed. Internally managed BDAs have their own investment professionals who make decisions regarding which companies to invest in and manage the day-to-day operations. Externally managed BDAs, on the other hand, outsource these responsibilities to an external investment advisor.
Investing in BDA finance offers several benefits for both individual and institutional investors:
BDA finance, or Business Development Companies, offer investors the opportunity to invest in small and mid-sized businesses and benefit from their growth and success. With a focus on diversification, income generation, and professional management, BDAs can be an attractive investment option. However, like all investments, it is essential to conduct thorough research and consider your individual financial goals and risk tolerance before investing in BDA finance.
Thank you for taking the time to read this article. We hope it has provided you with a better understanding of BDA finance and its potential benefits. If you have any further questions or would like more information, please don't hesitate to reach out.
大數(shù)據(jù)分析金融（Big Data Analytics in Finance，簡(jiǎn)稱BDA Finance）是指在金融行業(yè)中應(yīng)用大數(shù)據(jù)分析技術(shù)來(lái)獲取、處理和分析海量金融數(shù)據(jù)，以支持業(yè)務(wù)決策和風(fēng)險(xiǎn)管理。
隨著金融行業(yè)信息化程度的提高和數(shù)據(jù)量的劇增，大數(shù)據(jù)分析已經(jīng)成為金融行業(yè)的熱門話題。傳統(tǒng)金融數(shù)據(jù)處理方法已經(jīng)無(wú)法處理海量的結(jié)構(gòu)化和非結(jié)構(gòu)化數(shù)據(jù)，而大數(shù)據(jù)分析技術(shù)可以幫助金融機(jī)構(gòu)從龐大的數(shù)據(jù)中發(fā)現(xiàn)隱藏的模式、趨勢(shì)和關(guān)聯(lián)關(guān)系，為業(yè)務(wù)決策和風(fēng)險(xiǎn)管理提供有力支持。
大數(shù)據(jù)分析在金融行業(yè)的應(yīng)用非常廣泛。以下是幾個(gè)典型的應(yīng)用領(lǐng)域：
BDA Finance在金融行業(yè)的應(yīng)用仍處于不斷發(fā)展和探索階段。隨著大數(shù)據(jù)技術(shù)的不斷成熟和金融行業(yè)對(duì)數(shù)據(jù)分析的不斷需求，BDA Finance在金融行業(yè)的應(yīng)用將會(huì)越來(lái)越廣泛。未來(lái)，人工智能、機(jī)器學(xué)習(xí)等技術(shù)的發(fā)展將進(jìn)一步推動(dòng)BDA Finance的應(yīng)用，為金融行業(yè)帶來(lái)更多的創(chuàng)新和變革。
In the fast-paced world of finance, acronyms abound. One such acronym that has gained traction is BDA. But what exactly does BDA mean in finance? In this article, we will delve into the meaning of BDA, its significance in the finance industry, and how it is used by professionals.
BDA stands for "Big Data Analytics." It refers to the process of collecting, analyzing, and interpreting large volumes of data to derive meaningful insights and support decision-making within the financial sector. As technology continues to advance and data becomes increasingly abundant, BDA has emerged as a vital tool for financial institutions, investment firms, and professionals seeking a competitive edge.
The significance of BDA in finance cannot be overstated. By leveraging sophisticated algorithms and advanced analytics techniques, financial professionals can make data-driven decisions more accurately and efficiently. BDA allows them to identify patterns, trends, and correlations in vast amounts of data that would otherwise be impossible to uncover manually. Moreover, it enables them to predict market movements, identify investment opportunities, manage risks, detect fraud, and enhance operational efficiency.
BDA finds numerous applications in the finance industry. Some key areas where BDA is widely used include:
While BDA holds immense potential, it comes with its own set of challenges. Data privacy, security, and regulatory compliance are critical considerations when dealing with sensitive financial information. Additionally, the complexity of managing and analyzing large data sets requires advanced technological infrastructure and skilled professionals.
Looking ahead, the future of BDA in finance looks bright. As technologies like artificial intelligence (AI) and machine learning continue to evolve, the capabilities of BDA will expand further. The ability to harness the power of ever-growing data sets will enable financial institutions to make more accurate predictions, develop sophisticated risk models, and enhance overall decision-making processes.
In conclusion, BDA, which stands for Big Data Analytics, is a significant concept in the world of finance. It enables financial professionals to gain insights from large volumes of data, make data-driven decisions, and stay ahead in an increasingly competitive industry. Embracing BDA can lead to improved risk management, investment analysis, algorithmic trading, and customer relationship management. Just remember, in this age of data, understanding BDA is essential for anyone operating in the finance realm.
Thank you for reading this article. We hope it has provided you with a comprehensive understanding of what BDA means in finance and its importance in the industry. By harnessing the power of BDA, financial professionals can unlock valuable insights and make informed decisions to navigate the complex world of finance.
豐田標(biāo)志的三把，應(yīng)該是豐田銳志（或凱美瑞）中低配版的鑰匙（兩把帶遙控+一把備用不帶遙控，豐田大部分中級(jí)車的標(biāo)準(zhǔn)配置）； 兩把本田標(biāo)志的，是老款雅閣的鑰匙。
之前看了Mahout官方示例 20news 的調(diào)用實(shí)現(xiàn)；于是想根據(jù)示例的流程實(shí)現(xiàn)其他例子。網(wǎng)上看到了一個(gè)關(guān)于天氣適不適合打羽毛球的例子。
訓(xùn)練數(shù)據(jù)：
Day Outlook Temperature Humidity Wind PlayTennis
D1 Sunny Hot High Weak No
D2 Sunny Hot High Strong No
D3 Overcast Hot High Weak Yes
D4 Rain Mild High Weak Yes
D5 Rain Cool Normal Weak Yes
D6 Rain Cool Normal Strong No
D7 Overcast Cool Normal Strong Yes
D8 Sunny Mild High Weak No
D9 Sunny Cool Normal Weak Yes
D10 Rain Mild Normal Weak Yes
D11 Sunny Mild Normal Strong Yes
D12 Overcast Mild High Strong Yes
D13 Overcast Hot Normal Weak Yes
D14 Rain Mild High Strong No
檢測(cè)數(shù)據(jù)：
sunny，hot，high，weak
結(jié)果：
Yes=》 0.007039
No=》 0.027418
于是使用Java代碼調(diào)用Mahout的工具類實(shí)現(xiàn)分類。
基本思想：
1. 構(gòu)造分類數(shù)據(jù)。
2. 使用Mahout工具類進(jìn)行訓(xùn)練，得到訓(xùn)練模型。
3。將要檢測(cè)數(shù)據(jù)轉(zhuǎn)換成vector數(shù)據(jù)。
4. 分類器對(duì)vector數(shù)據(jù)進(jìn)行分類。
接下來(lái)貼下我的代碼實(shí)現(xiàn)=》
1. 構(gòu)造分類數(shù)據(jù)：
在hdfs主要?jiǎng)?chuàng)建一個(gè)文件夾路徑 /zhoujainfeng/playtennis/input 并將分類文件夾 no 和 yes 的數(shù)據(jù)傳到hdfs上面。
數(shù)據(jù)文件格式，如D1文件內(nèi)容： Sunny Hot High Weak
2. 使用Mahout工具類進(jìn)行訓(xùn)練，得到訓(xùn)練模型。
3。將要檢測(cè)數(shù)據(jù)轉(zhuǎn)換成vector數(shù)據(jù)。
4. 分類器對(duì)vector數(shù)據(jù)進(jìn)行分類。
這三步，代碼我就一次全貼出來(lái)；主要是兩個(gè)類 PlayTennis1 和 BayesCheckData = =》
package myTesting.bayes;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.classifier.naivebayes.training.TrainNaiveBayesJob;
import org.apache.mahout.text.SequenceFilesFromDirectory;
import org.apache.mahout.vectorizer.SparseVectorsFromSequenceFiles;
public class PlayTennis1 {
private static final String WORK_DIR = "hdfs://192.168.9.72:9000/zhoujianfeng/playtennis";
/*
* 測(cè)試代碼
*/
public static void main(String[] args) {
//將訓(xùn)練數(shù)據(jù)轉(zhuǎn)換成 vector數(shù)據(jù)
makeTrainVector();
//產(chǎn)生訓(xùn)練模型
makeModel(false);
//測(cè)試檢測(cè)數(shù)據(jù)
BayesCheckData.printResult();
}
public static void makeCheckVector(){
//將測(cè)試數(shù)據(jù)轉(zhuǎn)換成序列化文件
try {
Configuration conf = new Configuration();
conf.addResource(new Path("/usr/local/hadoop/conf/core-site.xml"));
String input = WORK_DIR+Path.SEPARATOR+"testinput";
String output = WORK_DIR+Path.SEPARATOR+"tennis-test-seq";
Path in = new Path(input);
Path out = new Path(output);
FileSystem fs = FileSystem.get(conf);
if(fs.exists(in)){
if(fs.exists(out)){
//boolean參數(shù)是，是否遞歸刪除的意思
fs.delete(out, true);
}
SequenceFilesFromDirectory sffd = new SequenceFilesFromDirectory();
String[] params = new String[]{"-i",input,"-o",output,"-ow"};
ToolRunner.run(sffd, params);
}
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
System.out.println("文件序列化失敗！");
System.exit(1);
}
//將序列化文件轉(zhuǎn)換成向量文件
try {
Configuration conf = new Configuration();
conf.addResource(new Path("/usr/local/hadoop/conf/core-site.xml"));
String input = WORK_DIR+Path.SEPARATOR+"tennis-test-seq";
String output = WORK_DIR+Path.SEPARATOR+"tennis-test-vectors";
Path in = new Path(input);
Path out = new Path(output);
FileSystem fs = FileSystem.get(conf);
if(fs.exists(in)){
if(fs.exists(out)){
//boolean參數(shù)是，是否遞歸刪除的意思
fs.delete(out, true);
}
SparseVectorsFromSequenceFiles svfsf = new SparseVectorsFromSequenceFiles();
String[] params = new String[]{"-i",input,"-o",output,"-lnorm","-nv","-wt","tfidf"};
ToolRunner.run(svfsf, params);
}
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
System.out.println("序列化文件轉(zhuǎn)換成向量失敗！");
System.out.println(2);
}
}
public static void makeTrainVector(){
//將測(cè)試數(shù)據(jù)轉(zhuǎn)換成序列化文件
try {
Configuration conf = new Configuration();
conf.addResource(new Path("/usr/local/hadoop/conf/core-site.xml"));
String input = WORK_DIR+Path.SEPARATOR+"input";
String output = WORK_DIR+Path.SEPARATOR+"tennis-seq";
Path in = new Path(input);
Path out = new Path(output);
FileSystem fs = FileSystem.get(conf);
if(fs.exists(in)){
if(fs.exists(out)){
//boolean參數(shù)是，是否遞歸刪除的意思
fs.delete(out, true);
}
SequenceFilesFromDirectory sffd = new SequenceFilesFromDirectory();
String[] params = new String[]{"-i",input,"-o",output,"-ow"};
ToolRunner.run(sffd, params);
}
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
System.out.println("文件序列化失敗！");
System.exit(1);
}
//將序列化文件轉(zhuǎn)換成向量文件
try {
Configuration conf = new Configuration();
conf.addResource(new Path("/usr/local/hadoop/conf/core-site.xml"));
String input = WORK_DIR+Path.SEPARATOR+"tennis-seq";
String output = WORK_DIR+Path.SEPARATOR+"tennis-vectors";
Path in = new Path(input);
Path out = new Path(output);
FileSystem fs = FileSystem.get(conf);
if(fs.exists(in)){
if(fs.exists(out)){
//boolean參數(shù)是，是否遞歸刪除的意思
fs.delete(out, true);
}
SparseVectorsFromSequenceFiles svfsf = new SparseVectorsFromSequenceFiles();
String[] params = new String[]{"-i",input,"-o",output,"-lnorm","-nv","-wt","tfidf"};
ToolRunner.run(svfsf, params);
}
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
System.out.println("序列化文件轉(zhuǎn)換成向量失敗！");
System.out.println(2);
}
}
public static void makeModel(boolean completelyNB){
try {
Configuration conf = new Configuration();
conf.addResource(new Path("/usr/local/hadoop/conf/core-site.xml"));
String input = WORK_DIR+Path.SEPARATOR+"tennis-vectors"+Path.SEPARATOR+"tfidf-vectors";
String model = WORK_DIR+Path.SEPARATOR+"model";
String labelindex = WORK_DIR+Path.SEPARATOR+"labelindex";
Path in = new Path(input);
Path out = new Path(model);
Path label = new Path(labelindex);
FileSystem fs = FileSystem.get(conf);
if(fs.exists(in)){
if(fs.exists(out)){
//boolean參數(shù)是，是否遞歸刪除的意思
fs.delete(out, true);
}
if(fs.exists(label)){
//boolean參數(shù)是，是否遞歸刪除的意思
fs.delete(label, true);
}
TrainNaiveBayesJob tnbj = new TrainNaiveBayesJob();
String[] params =null;
if(completelyNB){
params = new String[]{"-i",input,"-el","-o",model,"-li",labelindex,"-ow","-c"};
}else{
params = new String[]{"-i",input,"-el","-o",model,"-li",labelindex,"-ow"};
}
ToolRunner.run(tnbj, params);
}
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
System.out.println("生成訓(xùn)練模型失敗！");
System.exit(3);
}
}
}
package myTesting.bayes;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.fs.PathFilter;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.mahout.classifier.naivebayes.BayesUtils;
import org.apache.mahout.classifier.naivebayes.NaiveBayesModel;
import org.apache.mahout.classifier.naivebayes.StandardNaiveBayesClassifier;
import org.apache.mahout.common.Pair;
import org.apache.mahout.common.iterator.sequencefile.PathType;
import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirIterable;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.Vector.Element;
import org.apache.mahout.vectorizer.TFIDF;
import com.google.common.collect.ConcurrentHashMultiset;
import com.google.common.collect.Multiset;
public class BayesCheckData {
private static StandardNaiveBayesClassifier classifier;
private static Map<String, Integer> dictionary;
private static Map<Integer, Long> documentFrequency;
private static Map<Integer, String> labelIndex;
public void init(Configuration conf){
try {
String modelPath = "/zhoujianfeng/playtennis/model";
String dictionaryPath = "/zhoujianfeng/playtennis/tennis-vectors/dictionary.file-0";
String documentFrequencyPath = "/zhoujianfeng/playtennis/tennis-vectors/df-count";
String labelIndexPath = "/zhoujianfeng/playtennis/labelindex";
dictionary = readDictionnary(conf, new Path(dictionaryPath));
documentFrequency = readDocumentFrequency(conf, new Path(documentFrequencyPath));
labelIndex = BayesUtils.readLabelIndex(conf, new Path(labelIndexPath));
NaiveBayesModel model = NaiveBayesModel.materialize(new Path(modelPath), conf);
classifier = new StandardNaiveBayesClassifier(model);
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
System.out.println("檢測(cè)數(shù)據(jù)構(gòu)造成vectors初始化時(shí)報(bào)錯(cuò)。。。。");
System.exit(4);
}
}
/**
* 加載字典文件，Key: TermValue； Value：TermID
* @param conf
* @param dictionnaryDir
* @return
*/
private static Map<String, Integer> readDictionnary(Configuration conf, Path dictionnaryDir) {
Map<String, Integer> dictionnary = new HashMap<String, Integer>();
PathFilter filter = new PathFilter() {
@Override
public boolean accept(Path path) {
String name = path.getName();
return name.startsWith("dictionary.file");
}
};
for (Pair<Text, IntWritable> pair : new SequenceFileDirIterable<Text, IntWritable>(dictionnaryDir, PathType.LIST, filter, conf)) {
dictionnary.put(pair.getFirst().toString(), pair.getSecond().get());
}
return dictionnary;
}
/**
* 加載df-count目錄下TermDoc頻率文件，Key: TermID； Value：DocFreq
* @param conf
* @param dictionnaryDir
* @return
*/
private static Map<Integer, Long> readDocumentFrequency(Configuration conf, Path documentFrequencyDir) {
Map<Integer, Long> documentFrequency = new HashMap<Integer, Long>();
PathFilter filter = new PathFilter() {
@Override
public boolean accept(Path path) {
return path.getName().startsWith("part-r");
}
};
for (Pair<IntWritable, LongWritable> pair : new SequenceFileDirIterable<IntWritable, LongWritable>(documentFrequencyDir, PathType.LIST, filter, conf)) {
documentFrequency.put(pair.getFirst().get(), pair.getSecond().get());
}
return documentFrequency;
}
public static String getCheckResult(){
Configuration conf = new Configuration();
conf.addResource(new Path("/usr/local/hadoop/conf/core-site.xml"));
String classify = "NaN";
BayesCheckData cdv = new BayesCheckData();
cdv.init(conf);
System.out.println("init done...............");
Vector vector = new RandomAccessSparseVector(10000);
TFIDF tfidf = new TFIDF();
//sunny，hot，high，weak
Multiset<String> words = ConcurrentHashMultiset.create();
words.add("sunny",1);
words.add("hot",1);
words.add("high",1);
words.add("weak",1);
int documentCount = documentFrequency.get(-1).intValue(); // key=-1時(shí)表示總文檔數(shù)
for (Multiset.Entry<String> entry : words.entrySet()) {
String word = entry.getElement();
int count = entry.getCount();
Integer wordId = dictionary.get(word); // 需要從dictionary.file-0文件（tf-vector）下得到wordID，
if (StringUtils.isEmpty(wordId.toString())){
continue;
}
if (documentFrequency.get(wordId) == null){
continue;
}
Long freq = documentFrequency.get(wordId);
double tfIdfValue = tfidf.calculate(count, freq.intValue(), 1, documentCount);
vector.setQuick(wordId, tfIdfValue);
}
// 利用貝葉斯算法開(kāi)始分類,并提取得分最好的分類label
Vector resultVector = classifier.classifyFull(vector);
double bestScore = -Double.MAX_VALUE;
int bestCategoryId = -1;
for(Element element: resultVector.all()) {
int categoryId = element.index();
double score = element.get();
System.out.println("categoryId:"+categoryId+" score:"+score);
if (score > bestScore) {
bestScore = score;
bestCategoryId = categoryId;
}
}
classify = labelIndex.get(bestCategoryId)+"(categoryId="+bestCategoryId+")";
return classify;
}
public static void printResult(){
System.out.println("檢測(cè)所屬類別是："+getCheckResult());
}
}
