《C++ Design Patterns and Derivatives Pricing》Mark S. Joshi
对于懂得C++基础的人来说很重要,更重要的是教你学会Monte Carlo。
《Modeling Derivatives in C++ (Wiley Finance)》Justin London 学习了一年的金工,其实就这本书最核心最实用,其他的理论书看看就好。很多理论书还有重复部分,注意区分。《The Concepts and Practice of Mathematical Finance》Mark S. Joshi 这本书的目标在于覆盖一个优秀quant应该知道的知识领域,其中包括强列推荐你在应聘工作之前看的一些编程项目。《Interest Rate Models – Theory and Practice》Damiano Brigo / Fabio Mercurio 评价超高的书。这本书最大的精华是关于Libor market model的论述。本书的特点是作者将所有细节和盘托出,包括大量的数值结果,这样可以方便读者自学和验证。《Probability with Martingales》David Williams主要是围绕martingale展开的,前面一部份介绍必要的measure theory的部分,点到即止,都是后面基本的probability theory需要用到的。即使你之前不懂measure theory也能看懂。难怪是给undergraduate用的。Williams是这个方向上文笔最好的数学家了。《Monte Carlo Methods in Financial Engineering》 Paul Glasserman本书很实用,紧扣标题,就是围绕着金融工程中蒙特卡洛的应用展开,真正读过的人可能会有感受,此书不太适合作为first book来读,最好两方面都已经有所涉及,再来读收获更大也更舒服些。《My Life as a Quant: Reflections on Physics and Finance》Emanuel Derman作者是第一代quant,以前是GS的quant 研究部门head,现在哥大。是stochastic vol领域顶尖人物,其实也是很多其他领域顶尖人物。福利领取方式1. 关注公众号:UniCareer2. 回复关键字:我爱读书
面试官更在乎你对基本知识的了解是否透彻,而不是你懂得多少东西,展示你对这个领域的兴趣也很重要,你需要经常阅读Economist, FT 和Wall Street Journal,面试会问到一些基本微积分或分析的问题,例如Logx的积分是什么。问到类似Black-Scholes公式怎么得出的问题也是很正常的,他们也会问到你的论文相关的问题。
法国数学家庞加莱(Jules Henri Poincaré)是现代拓扑学的奠基人。拓扑学研究几何体,例如流形,在连续形变下的不变性质。我们可以想象曲面由橡皮膜制成,我们对橡皮膜拉伸压缩,扭转蜷曲,但是不会撕破或粘联,那么这些形变都是连续形变,或被称之为拓扑形变,在这些形变下保持不变的量就是拓扑不变量。如果一张橡皮膜曲面经由拓扑形变得到另外一张橡皮膜曲面,则这两张曲面具有相同的拓扑不变量,它们彼此拓扑等价。如图2 所示,假设兔子曲面由橡皮膜做成,我们象吹气球一样将其膨胀成标准单位球面,因此兔子曲面和单位球面拓扑等价。
The roots of entrepreneurship are old. But, the fruits were never so lucrative as they have been recently. Until 2010, not many of us had heard of the term ‘start-up’. And now, not a day goes by when business newspapers don’t quote them. There is sudden gush in the level of courage which people possess.
Today, I see 1 out of 5 person talking about a new business idea. Some of them even succeed too in establishing their dream company. But, only the determined ones sustain. In data science, the story is bit different.
The success in data science is mainly driven by knowledge of the subject. Entrepreneurs are not required to work at ground level, but must have sound knowledge of how it is being done. What algorithms, tools, techniques are being used to create products & services.
In order to gain this knowledge, you have two ways:
You work for 5-6 years in data science, get to know things around and then start your business.
You start reading books along the way and become confident to start in first few years.
I would opt for second option.
Why read books ?
Think of our brain as a library. And, it’s a HUGE library.
How would an empty library look like? If I close my eyes and imagine, I see dust, spider webs, brownian movement of dust particles and darkness. If this imagination horrifies you, then start reading books.
The books listed below gives immense knowledge and motivation in technology arena. Reading these books will give you the chance to live many different entrepreneurial lives. Take them one by one. Don’t get overwhelmed. I’ve displayed a mix of technical and motivational books for entrepreneurs in data science. Happy Reading!
List of Books
Data Science For Business
This book is written by Foster Provost & Tom Fawcett. It gives a great head start to anyone, who is serious about doing business with big data analytics. It makes you believe, data is now business. No business in the world, can now sustain without leveraging the power of data. This books introduces you to real side of data analysis principles and algorithms without technical stuff. It gives you enough intuition and confidence to lead a team of data scientists and recommend what’s required. More importantly, it teaches you the winning approach to become a master at business problem solving.
This book is written by Thomas H. Davenport. It reveals the increasing importance of big data in organizations. It talks with interesting numbers, researches and statistics. So until 2009, companies worked on data samples. But with advent of powerful devices and data storage capabilities, companies now work on whole data. They don’t want to miss even a single bit of information. This book unveils the real side of big data, it’s influence on our daily lives, on companies and our jobs. As an entrepreneur, it is extremely important for you understand big data and its related terminologies.
This book is written by Alistair Croll and Benjamin Yoskovitz. It’s one of the most appreciated books on data startups. It consist of practical & detailed researches, advice, guidance which can help you to build your startup faster. It gives enough intuition to build data driven products and market them. The language is simple to understand. There are enough real world examples to make you believe, a business needs data analytics like a human needs air. To an entrepreneur, this will introduce the practical side of product development and what it takes to succeed in a red ocean market.
This book is written by Michael Lewis. It’s a brilliant tale which sprinkles some serious inspiration. A guy named billy bean does what most of the world failed to imagine, just by using data and statistics. He paved the path to victory when situations weren’t favorable. Running a business needs continuous motivation. This can be a good place to start with. However, this book involves technical aspects of baseball. Hence, if you don’t know baseball, chances are you might struggle in initial chapters. A movie also has been made on this book. Do watch it!
This book is written by Ashlee Vance. I’m sure none of us are fortunate to live the life of Elon Musk, but this book let’s us dive in his life and experience rise of fantastic future. Elon is the face behind Paypal, Tesla and SpaceX. He has dreamed of making space travel easy and cheap. Recently, he was applauded by Barack Obama for the successful landing of his spaceship in an ocean. People admire him. They want to know his secrets and this is where you can look for. As on entrepreneur, you will learn about must have ingredients which you need to a become successful in technology space.
This book is written by Thomas H Davenport and Jinho Kim. As we all know, data science is driven by numbers & maths (quants). Inspired from moneyball, this book teaches you the methods of using quantitative analysis for decision making. An entrepreneur is a terminal of decision making. One must learn to make decisions using numbers & analysis, rather than intuition. The language of this book is easy to understand and suited for non-maths background people too. Also, this book will make you comfortable with basics statistics and quantitative calculations in the world of business.
The author of this book is Nate Silver, the famous statistician who correctly predicted US Presidential elections in 2012. This books shows the real art and science of making predictions from data. This art involves developing the ability to filter out noise and make correct predictions. It includes interesting examples which conveys the ultimate reason behind success and failure of predictions. With more and more data, predictions have become prone to noise errors. Hence, it is increasingly important to understand the science behind making predictions using big data science. The chapters of this book are interesting and intuitive.
This book is written by Roger Lowenstein. It is an epic story of rise and failure of a hedge fund. For an entrepreneur, this book has ample lessons on investing, market conditions and capital management. It’s a story of a small bank, which used quantitative techniques for bond pricing throughout the world and ensured every invested made gives a profitable results. However, they didn’t sustain for long. Their quick rise was succeeded by failure. And, the impact of their failure was so devastating that US Federal bank stepped in to rescue the bank, because the fund’a bankruptcy would have large negative influence on world’s economy.
This book is written by Eric Ries. In one line, it teaches how to not to fail at the start of your business. It reveals proven strategies which are followed by startups around the world. It has abundance of stories to make you walk on the right path. An entrepreneur should read it when he/she feel like draining out of motivation. It teaches to you to learn quickly, implement new methods and act quickly if something doesn’t work out. This book applies to all industries and is not specific to data science.
This book is written by Avinash Kaushik. It is one of the best book to learn about web analytics. Internet is the fastest mode of collecting data. And, every entrepreneur must learn the art of internet accounting. Most of the businesses today face the challenge of weak presence on social media and internet platforms. Using various proven strategies and actionable insights, this book helps you to solve various challenges which could hamper your way. It also provides a winning template which can be applied in most of the situations. It focuses on choosing the right metric and ways to keep them in control.
This book is written by Eric Seigel. It is a good follow up book after web analytics 2.0. So, once you’ve understood the underlying concept of internet data, metrics and key strategies. This book teaches you the methods of using that knowledge to make predictions. It’s simple to understand and covers many interesting case studies displaying how companies predict our behavior and sell us products. It doesn’t cover technical aspects, but explains the general working on predictive analytics and its applications. You can also check out this funny rap video by Dr. Eric Seigel:
This book is written by Steven D Levitt and Stephen J Dubner. It shows the importance of numbers, data, quantitative analysis using various interesting stories. It says, there is a logic is everything which happens around us. Reading this book will make you aware of the unexplored depth at which data affects our real lives. It draws interesting analogy between school teachers and sumo wrestlers. Also, the bizarre stories featuring cases of criminal acts, real-estate, drug dealers will certainly add up to your exciting moments.
This book is written by Jessica Livingston. Again, this isn’t data science specific but a source of motivation to get you moving forward. It’s a collection of interviews with the founders of various startups across the world. The focus has been kept on early days i.e. how did they act when they started. This book will give you enough proven ideas, strategies and lessons to anticipate and avoid pitfalls in your initial days of business. It consist of stories by Steve Wozniak (Apple), Max Levchin (Paypal), Caterina Fake (Flikr) and many more. In total, there are 32 interviews listed which means you have the chance to learn from 32 mentors in one single book. Must read for entrepreneurs.
This book is written by Greg Gianforte and Marcus Gibson. It teaches about the things to do when you are running short of money and still don’t want to stop. This is a must read book for every entrepreneur. Considering the amount of investment required in data science startups, this book should have a special space in an entrepreneur’s heart. It reveals various eye opening truths and strategies which can help you build a great company. Greg and Marcus proves that money is not always the reason for startup failure, it’s all about founder’s perspective. This book has stories of success and failures, again a great chance for you to live many lives by reading this book.
This book is written by Thomas H Davenport, Jeanne G Harris and Robert Morrison. This books reveals the increased use of analytical tools & concepts by managers to make informed business decisions. The decision making process has accelerated. For a greater impact, it also consists of examples from popular companies like hotels.com, best buy and many more. It talks about recruiting, coordination with people and the use of data and analytics at an enterprise level. Many of us are aware of data and analytics. But, only a few know how to use them together. This quick book has it all !
This marks the end of this list. While compiling this list, I realized most of these books are about sharing experience and learning from the mistake of others. Also, it is immensely important to posses quantitative ability to become good in data science. I would suggest you to make a reading list and stick to it throughout the year. You can take up any book to start. I’d suggest to start with a motivational book.
Have you read any other book ? What were your key takeaways? Did you like reading this article? Do share your knowledge & experiences in the comments below.
odds= p/ (1-p) = probability of event occurrence / probability of not event occurrence ln(odds) = ln(p/(1-p)) logit(p) = ln(p/(1-p)) = b0+b1X1+b2X2+b3X3....+bkXk
这是我最喜欢也是能经常使用到的算法。它属于监督式学习,常用来解决分类问题。令人惊讶的是,它既可以运用于类别变量(categorical variables)也可以作用于连续变量。这个算法可以让我们把一个总体分为两个或多个群组。分组根据能够区分总体的最重要的特征变量/自变量进行。更详细的内容可以阅读这篇文章Decision Tree Simplified。
从上图中我们可以看出,总体人群最终在玩与否的事件上被分成了四个群组。而分组是依据一些特征变量实现的。用来分组的具体指标有很多,比如Gini,information Gain, Chi-square,entropy。
延伸阅读:Simplified Version of Decision Tree Algorithms
Python 代码
#Import Library
#Import other necessary libraries like pandas, numpy...
from sklearn import tree
#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
# Create tree object
model = tree.DecisionTreeClassifier(criterion='gini')# for classification, here you can change the algorithm as gini or entropy (information gain) by default it is gini
# model = tree.DecisionTreeRegressor() for regression
# Train the model using the training sets and check score
Partly due to how robust and powerful it is, Drupal has a learning curve. Like other content management systems, it has some of its own lingo. Understanding the fundamental terms below will help anyone (technically inclined or not) better grasp the system. These terms are applicable to the three versions of Drupal (5, 6, and 7) that I have used.
1. Node – a single piece of content. When my clients refer to a web page on their Drupal site, they are referring to a node. It is important to note that nodes do not have to be a web page, in many cases they are, but they can be other things like an item listed in a view (mentioned below) among other things.
2. Content Type – a standard configuration of a node. When constructing a site, many pages have the same type of information laid out in the same fashion. For instance, all of the bio pages on a site likely have a person’s name, picture, brief biographical text, and contact information. Instead of creating these fields and laying out each page one at a time, Drupal content types allow one to set a standard set of data fields and layout of them.
3. Views – an organized list of nodes. Through views sites can display a set of nodes in an organized fashion. Sorting options include – but are not limited to: alphabetical order, publication date, most viewed, random, and taxonomy groupings. The display format is customizable, and I’ve seen straight text, tables, and images all used in various ways.
4. Taxonomy – a list of related terms used to tag content. Through Drupal, taxonomy terms allow for classifying nodes in a way other than content types. For instance, a site can have a taxonomy term for each of the continents of the world. Once that is done, one of the terms (say “North America”) can be applied to some bio pages, blog posts, and vendor pages even though they are nodes of different content types. Then they can be organized by this term to be displayed in a view.
5. Block – an area on a page that can contain content and then can be placed in a certain area of the site as defined by the site’s template. A common use of blocks is to place items in a sidebar. Such items can include images, sub menus, and views. The great thing about blocks is that they can be restricted to only appear on certain pages of a site. Further, configuration options also allow them to be only displayed to certain user roles (perhaps premium members or site administrators).
6. Webform – a content type that enables site administrators to create forms to gather information from site visitors. A very common webform application is to create contact forms through which site administrators can create fields such as sender’s name, sender’s e-mail/phone number, type of inquiry (like general inquiry, price quote request, and media contact), and message. Upon submission, the form can send an e-mail with the provided information to predefined recipients. The data is also stored and can be exported into a spreadsheet.
7. User Role – a set of permissions granted to a user account. Through roles site administrators can carefully grant specific abilities to certain users. For instance, users with access to a premium member area of the site not intended for the public shouldn’t also have the ability to change the site’s menus, for instance. However, through roles, users can be allowed to create, edit, and delete nodes of certain content types without necessarily granting them the ability to manage the site in other ways.
8. Module – a program specifically designed for Drupal that adds functionality to it. One of the greatest features of Drupal is its modular design that allows for site administrators to tact on functionality to the CMS. Modules do a variety of things ranging from controlling a node’s slug (the “…” part of sample.com/… to a specific format) to logging in users to an account based upon their IP address to connecting with third-party systems like Google Analytics, Eventbrite, and Salesforce. There is a wide variety of modules that the Drupal open source community develops and maintains free of charge.
9. Themes – more or less a template for the site. Drupal supports multi-themed sites. One major use of this is creating a desktop theme and a mobile theme. For sites that we create a mobile version using Drupal, we have created a mobile theme and then make sure that the theme that is displayed is based upon the device a site visitor is using.
10. Input Formats – modes that control the type of content entered into a field. Most nodes have body fields where one can input the text and images for the node. One does not need to know much about HTML or PHP to use Drupal, and this input formats will find ways to add line breaks and link urls and e-mail addresses for people without the need for the coding. Used in concert with a WYSIWYG editor, a person can still present styled text and images using the Filtered or Full HTML formats. Further, when judiciously granting rights to use formats through user roles (particularly to the Full HTML and PHP code formats), it can help secure the site since using HTML and PHP can be used by external users to hack the site.