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65 Free Data Science Resources for Beginners
Apr 20, · slide 4: Dream no small dreams for they have no power to move the hearts of men. —Johann Wolfgang von Goethe CHAPTER 1 Introduction: Data-Analytic Thinking The past fifteen years have seen extensive investments in business infrastructure which have improved the ability to collect data throughout the enterprise. Apr 20, · science and data mining, except where it will have a substantial effect on understanding the actual concepts. Let’s examine two brief case studies of analyzing data to extract predictive patterns. Introduction to Data Science was originally developed by Prof. explain it to the business leaders” Data scientists “tend to be “hard scientists”, blogger.com] A PRACTICAL DEFINITION Data Science is about the whole processing pipeline to extract information out of data.
Data science for business free pdf download
Copy embed code:. Automatically changes to Flash or non-Flash embed. WordPress Embed Customize Embed. URL: Copy. All rights reserved. Printed in the United States of America. While every precaution has been taken in the preparation of this book the publisher and authors assume no responsibility for errors or omissions or for damages resulting from the use of the information contained herein.
This broad availability of data has led to increasing interest in methods for extracting useful information and knowledge from data—the realm of data science. The convergence of these phenomena has given rise to the increasingly widespread business application of data science principles and data-mining techniques. Probably the widest applications of data-mining techniques are in marketing for tasks such as targeted marketing online advertising and recommendations for cross-selling.
The finance industry uses data mining for credit scoring and trading and in operations via fraud detection and workforce management. Many firms have differentiated themselves strategically with data science sometimes to the point of evolving into data mining companies. The primary goals of this book are to help you view business problems from a data perspective and understand principles of extracting useful knowledge from data. There is a fundamental structure to data-analytic thinking and basic principles that should be understood.
There are also particular areas where intuition creativity common sense and domain knowledge must be brought to bear. A data perspective will provide you with structure and principles and this will give you a framework to systematically analyze such problems.
As you get better at data-analytic thinking you will develop intuition as to how and where to apply creativity and domain knowledge. Throughout the first two chapters of this book we will discuss in detail various topics and techniques related to data science and data mining. At a high level data science is a set of fundamental principles that guide the extraction of knowledge from data. Data mining is the extraction of knowledge from data via technologies that incorporate these principles.
It is important to understand data science even if you never intend to apply it yourself. This does not mean that you will be able to tell whether it will actually succeed—for data mining projects that often requires trying—but you should be able to spot obvious flaws unrealistic assumptions and missing pieces. For each principle there are usually many specific techniques that embody it so in this book we have chosen to emphasize the basic principles in preference to specific techniques.
That said we will not make a big deal about the difference between data 2 Chapter 1: Introduction: Data-Analytic Thinking www. Of course What goes better with strawberry Pop-Tarts than a nice cold beer science and data mining except where it will have a substantial effect on understanding the data science for business free pdf download concepts.
Hays Consider why data-driven prediction might be useful in this scenario. It might be useful to predict that people in the path of the hurricane would buy more bottled water.
Maybe but this point seems a bit obvious and why would we need data science to discover it It might be useful to project the amount of increase in sales due to the hurricane to ensure that local W al-Marts are properly stocked, data science for business free pdf download.
The prediction could be somewhat useful but is probably more general than Ms. T o do this analysts might examine the huge volume of W al-Mart data from prior similar situations such as Hurricane Charley to identify unusual local demand for products, data science for business free pdf download. Indeed that is what happened. Dillman said in a recent interview. This problem will serve as a running example that will illuminate many of the issues raised in this book and provide a common frame of reference.
Data science for business free pdf download the mid-Atlantic region 20 data science for business free pdf download cell phone customers leave when their contracts expire and it is getting increasingly difficult to acquire new customers, data science for business free pdf download. Since the cell phone market is now saturated the huge growth in the wireless market has tapered off.
You have been called in to help understand the problem and to devise a solution. Marketing has already designed a special retention offer. Think carefully about what data you might use and how they would be used.
Specifically how should MegaTelCo choose a set of customers to receive their offer in order to best reduce churn data science for business free pdf download a particular incentive budget Answering this question is much more complicated than it may seem initially. W e will return to this problem repeatedly through the book adding sophistication to our solution as we develop an understanding of the fundamental data science concepts. Data science in the context of various data-related processes in the organization.
Figure places data science in the context of various other closely related and data- related processes in the organization. It distinguishes data science from other aspects of data processing that are gaining increasing attention in business. Data-driven decision-making DDD refers to the practice of basing decisions on the analysis of data rather than purely on intuition. For example a marketer could select advertisements based purely on her long experience in the field and her eye for what will work.
She could also use a combination of these approaches, data science for business free pdf download. DDD is not an all-or-nothing practice and different firms engage in DDD to greater or lesser degrees. The benefits of data-driven decision-making have been demonstrated conclusively.
The business presents a problem we would like to solve. Rarely is the business problem directly one of our basic data mining tasks. We decompose the problem into subtasks that we think we can solve usually starting with existing tools. For some of these tasks we may not know how well we can solve them so we have to mine the data and conduct evaluation to see. If that does not succeed we may need to try something completely different.
In the process we may discover knowledge that will help us to solve the problem we had set out to solve or we may discover something unexpected that leads us to other important successes.
Omitting the engineering aspect usually makes it much less likely that the results of mining data will actually solve the business problem. In this book we have introduced a collection of the most important fundamental concepts. These concepts span the process from envisioning how data science can improve business decisions to applying data science techniques to deploying the results to improve decision-making. We can group our fundamental concepts roughly into three types: 1. General ways of thinking data-analytically which help us to gather appropriate data and consider appropriate methods.
The concepts include the data mining process the collection of different high-level data science tasks as well as principles such as the following. General concepts for actually extracting knowledge from data which undergird the vast array of data science techniques. These include concepts such as the following. As we have illustrated throughout the book these principles not only allow us to understand the theory and practice of data science much more deeply they also allow us to understand the methods and techniques of data science very broadly because these methods and techniques are quite often simply particular instantiations of one or more of the fundamental principles.
At a high level we saw how structuring business problems using the expected value framework allows us to decompose problems into data science tasks that we understand better how to solve and this applies across many different sorts of business problems. It is the basis for clustering the unsupervised grouping of data objects. It is the basis for finding documents most related to a search query. When it comes to measurement we see the notion of lift—determining how much more likely a pattern is than would be expected by chance—appearing broadly across data science when evaluating very different sorts of patterns.
One evaluates algorithms for targeting advertisements by computing the lift one gets for the targeted population. One calculates lift for judging the weight of evidence for or against a conclusion. Instead of missing important aspects of a discussion completely we can dig in and ask questions that will reveal critical aspects that otherwise would not have been uncovered. You ask how exactly they are personalizing the news.
They say they use support vector machines. Y ou should be ready to ask whether you really think they will have reliable training labels for such a task. And so on. Recently as of this writing there has been a marked shift in consumer online activity from traditional computers to a wide variety of mobile devices.
Companies many still working to understand how to reach consumers on their desktop computers now are scrambling to understand how to reach consumers on their mobile devices: smart phones tablets and even increasingly mobile laptop computers as WiFi access becomes ubiquitous, data science for business free pdf download. In particular mobile devices are associated with data on their location. For example in the mobile advertising ecosystem depending on my privacy settings my mobile device may broadcast my exact GPS location to those entities who would like to target me with advertisements daily deals and other offers.
Figure shows a scatterplot of a small sample of locations that a potential advertiser might see sampled from the mobile advertising ecosystem. Even if I do not broadcast my GPS location my device broadcasts the IP address of the network it currently is using which often conveys location information. A scatterplot of a sample of GPS locations captured from mobile devices. As an interesting side point this is just a scatterplot of the latitude and longitudes broadcast by mobile devices there is no map It gives a striking picture of population density across the world.
A particular data science for business free pdf download might have certain problems to solve and be focused on one or two. Follow us on:. Go to Application. US Go Premium. PowerPoint Templates.
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Introduction: Data-Analytic Thinking given rise to the increasingly widespread business application of data science principles and data-mining techniques. Probably the widest applications of data-mining techniques are in marketing for tasks such as targeted marketing, online advertising, and recommendations for cross-selling. Apr 20, · slide 4: Dream no small dreams for they have no power to move the hearts of men. —Johann Wolfgang von Goethe CHAPTER 1 Introduction: Data-Analytic Thinking The past fifteen years have seen extensive investments in business infrastructure which have improved the ability to collect data throughout the enterprise. Here is a great collection of eBooks written on the topics of Data Science, Business Analytics, Data Mining, Big Data, Machine Learning, Algorithms, Data Science Tools, and Programming Languages for Data Science. 60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more Data Science, Data Mining.
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