{"id":90,"date":"2016-04-30T23:04:35","date_gmt":"2016-04-30T23:04:35","guid":{"rendered":"http:\/\/blogs.softwareclue.com\/?p=90"},"modified":"2016-05-15T03:39:36","modified_gmt":"2016-05-15T03:39:36","slug":"15-must-read-books-for-entrepreneurs-in-data-science","status":"publish","type":"post","link":"http:\/\/blog.softwareclues.com\/zh\/15-must-read-books-for-entrepreneurs-in-data-science","title":{"rendered":"15 Must Read Books for Entrepreneurs in Data Science"},"content":{"rendered":"

Source: http:\/\/www.analyticsvidhya.com\/blog\/2016\/04\/15-read-books-entrepreneurs-data-science\/<\/a><\/p>\n

Manish Saraswat <\/a> <\/span>,

Introduction<\/h2>\n

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\u00a0of the term \u2018start-up\u2019. And now, not a day goes by when business newspapers don\u2019t quote them. There is sudden gush in the level of courage which people possess.<\/p>\n

Today, I see\u00a01 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\u00a0different.<\/p>\n

The success in data science is mainly\u00a0driven by knowledge of the subject. Entrepreneurs are not\u00a0required 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.<\/p>\n

In order to gain this knowledge, you have two ways:<\/p>\n

    \n
  1. You work for 5-6 years in data science, get to know things around and then start your business.<\/li>\n
  2. You start reading books along the way and become confident to start in first few years.<\/li>\n<\/ol>\n

    I would opt for\u00a0second option.<\/p>\n

    \"15<\/p>\n

    Why read books ?<\/h2>\n

    Think of our brain as a library. And, it\u2019s a HUGE\u00a0library.<\/p>\n

    How would an empty library look like? If I close my eyes and imagine, I see dust, spider webs, brownian movement<\/a> of dust particles and darkness. If this imagination horrifies you, then start reading books.<\/p>\n

    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\u2019t get overwhelmed. I\u2019ve displayed a mix of technical and motivational books for entrepreneurs in data science. Happy Reading!<\/p>\n

    List of Books<\/h2>\n

    Data Science For Business\"data<\/a><\/h3>\n

    This book is written by\u00a0Foster Provost & Tom Fawcett. It gives a great head start to anyone, who is\u00a0serious 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\u2019s required. More importantly, it teaches you the winning approach to become a master at business problem solving.<\/p>\n

    Get the book: Buy Now<\/a><\/p>\n

    Big Data at Work\"b2\"<\/a><\/h3>\n

    This book is written by Thomas H. Davenport. It reveals the increasing importance of big data in organizations. It talks with interesting \u00a0numbers, 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\u00a0data. They don\u2019t want to miss even a single bit of information.\u00a0This book unveils the real side of big data, it\u2019s 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.<\/p>\n

    Get the book: Buy Now<\/a><\/p>\n

    Lean Analytics\"b3\"<\/a><\/h3>\n

    This book is written by Alistair Croll and Benjamin Yoskovitz. It\u2019s 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.<\/p>\n

    Get the book: Buy Now<\/a><\/p>\n

    Moneyball\"b4\"<\/a><\/h3>\n

    This book is written by Michael Lewis. It\u2019s a brilliant tale which sprinkles some\u00a0serious 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\u2019t 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\u2019t know baseball, chances are you might struggle in initial chapters. A movie also has been made on this book. Do watch it!<\/p>\n

    Get the book: Buy Now<\/a><\/p>\n

    Elon Musk\"b5\"<\/a><\/h3>\n

    This book is written by Ashlee Vance. I\u2019m sure none of us are fortunate to\u00a0live the life of Elon Musk, but this book let\u2019s 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.<\/p>\n

    Get the book: Buy Now<\/a><\/p>\n

    Keeping up with the Quants\"b6\"<\/a><\/h3>\n

    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.<\/p>\n

    Get the book: Buy Now<\/a><\/p>\n

    The Signal and the Noise\"Cover<\/a><\/h3>\n

    The author of this book is Nate Silver, the famous statistician\u00a0who 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.<\/p>\n

    Get the book: Buy Now<\/a><\/p>\n

    When Genius Failed\"b8\"<\/a><\/h3>\n

    This book is written by Roger Lowenstein. It\u00a0is an epic story of rise and failure of a hedge fund.\u00a0For an entrepreneur, this book has ample lessons on investing, market conditions and capital management. It\u2019s 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\u2019t 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\u2019a bankruptcy would have large negative influence on world\u2019s economy.<\/p>\n

    Get the book: Buy Now<\/a><\/p>\n

    Lean Startup\"b9\"<\/a><\/h3>\n

    This book is written by Eric Ries. In one line, it\u00a0teaches 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\u2019t work out. This book applies to all industries and is not specific to data science.<\/p>\n

    Get the book: Buy Now<\/a><\/p>\n

    Web Analytics 2.0<\/h3>\n

    \"b10\"<\/a>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\u00a0accounting. 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.<\/p>\n

    Get the book: Buy Now<\/a><\/p>\n

    Predictive Analytics\"b11\"<\/a><\/h3>\n

    This book is written by Eric Seigel. It is a good follow up book after web analytics 2.0. So, once you\u2019ve 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\u2019s simple to understand and covers many interesting case studies displaying how companies predict our behavior and sell us products. It doesn\u2019t 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:<\/p>\n

    Get the book: Buy Now<\/a><\/p>\n