...------------------------------------------------- ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- Submitted by: John Charlemagne Buan ------------------------------------------------- Submitted to: Ms. Harlene Santos ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- Analytic geometry From Wikipedia, the free encyclopedia Analytic geometry, or analytical geometry, has two different meanings in mathematics. The modern and advanced meaning refers to the geometry of analytic varieties. This article focuses on the classical and elementary meaning. In classical mathematics, analytic geometry, also known as coordinate geometry, or Cartesian geometry, is the study of geometry using a coordinate system and the principles of algebra and analysis. This contrasts with the...
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...companies in every industry are using analytics to replace intuition and guesswork in their decision-making. As a result, managers are collecting and analyzing enormous data sets to discover new patterns and insights and running controlled experiments to test hypotheses. This course prepares students to understand structured data and business analytics and become leaders in these areas in business organizations. This course teaches the scientific process of transforming data into insights for making better business decisions. It covers the methodologies, algorithms, issues, and challenges related to analyzing business data. It will illustrate the processes of analytics by allowing students to apply business analytics algorithms and methodologies to real-world business datasets from finance, marketing, and operations. The use of real-world examples and cases places business analytics techniques in context and teaches students how to avoid the common pitfalls, emphasizing the importance of applying proper business analytics techniques. In addition to cases, this course features hands-on experiences with data collection using Python programs and analytics software such as SAS Enterprise Guide. Throughout the semester, each team works to frame a variety of business issues as an analytics problem, analyze data provided by the company, and generate applicable business insights as a secondary objective, while also learning essential business analytics techniques. Students benefit...
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...Introduction Sprint has nearly 54 million customers and offers a host of products for consumers, businesses and government. The company recently began using analytics tools to try to make sense of the mountains of data created by Sprint network users on a daily basis. With approximately 70,000 employees worldwide and nearly $27 billion in annual revenues, Sprint is widely recognized for developing, engineering and deploying state-of-the-art network technologies, including the United States' first nationwide all-digital, fiber-optic network and an award-winning Tier 1 Internet backbone. Sprint provides local voice and data services in 18 states and operates the largest 100-percent digital, nationwide wireless network in the United States. The decision to focus our project on Sprint was based on a couple of factors. Sprint is currently the 3rd largest telecom operator in USA and with the recent take over by SoftBank the prospects to grow look promising. This provides a clear opportunity to help the business grow using analytics. Secondly because of our connections in the company we were able to get more information on Sprint’s analytics strategy and future plans. Recommendations Sprint Telecom is part of an industry, which is one of the largest providers of data in the world. Sprint’s initial big data steps have been in the right direction. They have successfully used their current data sets for quick profits and short-term results. It is now time to take the jump...
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...Analytic Geometry is a branch of mathematics in which problems are solved using the principles of Geometry and the processes of Algebra. He is regarded as the founder of Analytic geometry by introducing coordinates system in 1637. René Descartes The Cartesian Coordinate System * also known as Rectangular Coordinate System or xy-Coordinate System. * It is made up of two mutually perpendicular number lines with the same unit of length and intersecting at their origin. The origin of its number line is its zero point. * The number lines are called the coordinate axes. * The horizontal line is called the x-axis and the vertical line is called the y-axis. * The coordinate axes divide the whole plane into four regions called quadrants. * The plane on which these axis are constructed is called the Coordinate Plane or xy-plane. * The distance of any point P from the y-axis is called x-coordinate or abscissa of the point P. * The distance of any point P from the x-axis is called the y-coordinate or ordinate of the point P. * The pair of real numbers (x,y) is called the coordinate pair of point P. * The symbol P(x,y) is used to indicate the point P on the plane with abscissa x and ordinate y. * The signs of the coordinates determine the quadrant where the point lies. * QI: (+,+) QIII: (-,-) QII: (-,+) QIV: (+,-) Exercise 1.1 Indicate the quadrant or the axis on which the point lies. 1. A(3,-2) 6. F(5,0) 2. B(-1,5)...
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...Google Analytics March 3, 2012 Google Analytics Google analytics is a service the Google Company gives to clients. Through this service Google wants to attract more of the traffic in the internet to get more site visitors into customers for the site owner. The owner of the site can use Google Analytics to learn which online marketing initiatives are cost effective and how visitors actually interact with the site. It shows how the site owners can make informed site design improvements, drive targeted traffic, and increase your conversions and profits. Google Analytics uses a first-party cookie and Java script codes to collect information about visitors and track your advertising campaign data. Google Analytics anonymously tracks how visitors interact with a website, including where they came from, what they did on the site, and whether they completed any of the site`s conversion goals. Analytics also keep track of your e-commerce data, and combines this with campaign and conversion information to provide insight into the performance of your advertising campaigns. All this information is presented in an easy to read, yet through manner, through intuitive visual reports. Google Analytics won`t affect the performance or the appearance of your website and there are no extra files to host on your website. Some of the things Google Analytics do are: 1. Goals Track Sales and Conversions - Measure your site engagement goals against: threshold levels that you define. 2....
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...5 Insights for executives Predictive analytics The C-suite’s shortcut to the business of tomorrow Of special interest to Chief executive officer Chief financial officer Chief marketing or sales officer Chief information officer In the era of big data, companies across a range of industries are recognizing the need for better intelligence and insight about their business. They want to work out how to make the best decisions, drawing on the right information, at the right time. • Finding and accelerating growth opportunities — drawing on internal and external data to help model and predict business outcomes, identify the most profitable opportunities and differentiate the business from its rivals. One organization that has been pioneering in its use of predictive analytics has been the United States Postal Service. Using an analytical approach, it predicted which workers’ compensation claims and payments were unwarranted — and saved some US$9.5 million during 2012 alone. This is not an isolated example: many leading organizations have started to regard their information as a corporate asset. • Improving business performance — enabling agile planning, more accurate forecasting, better budgeting and trusted decision-making support. Business benefit can be gained by creating systems that can convert information into actionable insights, all within the context of key business priorities. Some of these include: 2 | 5 Insights for executives ...
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...Answer the following questions in a total of 200 to 300 words: How are pragmatism and analytic philosophy uniquely American movements? What ideas make them different from the way Europeans of the same period were thinking? The pragmatism and analytic philosophy are uniquely American movements because pragmatists (C.S. Peirce, William James, and John Dewy) rejected the idea that there is such thing as fixed, meaning absolute truth. They believed that truth is relative to a time, place, purpose, and is ever changing when new data is introduced. Analytic philosophy deals with analysis of ideas that are complicated and make them easier to understand. Basically what it does is turn complicated propositions or concepts into simpler ones. Kant, Hegel, and Bertrand Russell were idealist. Kant thought that knowledge is possible if we limit our inquiries to things as they are experienceable, because the mind imposes categories on experienceable objects. Hegel expanded Kant’s knowledge, he believed that that the categories of thought are the categories of being. Bertrand Russell started having interest in philosophy was because he studied mathematics and wanted to find a satisfactory account of numbers and mathematics. He got into philosophy when he started to make irrelevant assumptions when trying do so, basically he stumbled upon it and just pursued it. What made them different from the way that Europeans of the same period were thinking, was that European philosophers took more...
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...Assignment 1: DDS, BI, Business Analytics, and Predictive Analytics LaShonda Spell Prof. S. Mirajkar CIS 356 Operating a successful business today involves utilizing the correct tools to make the best decisions for that business. The main tools that are used for making critical business decisions are DSS, DDS, BI, Business Analytics and Predictive Analytics systems. The concepts/ systems mentioned assist management in the major decision-making processes by providing crucial operational data in comprehensible formats for monitoring/ reviewing and analyzing. Making the best decisions regarding business operations determine the success or failure of the company and ensures that all business strategies are implemented and effective. In this essay, there is a brief overview of the similarities/ differences, methodologies/ technologies and evaluation of the capabilities of DDS, BI, Business Analytics and Predictive Analytics systems. Similarities and Differences among DDS, BI, Business Analytics, and Predictive Analytics regarding business scope/origins /histories/ methodologies/ technologies DDS (Data Distribution Service) are data communications based on the standards managed by the OMG (Object Management Group). The standards set by the OMG of DDS describe different latency levels of data communications for distributed applications (Twin Oaks Computing, Inc., 2011). DDS standard support data defining applications, dynamic publishing/ subscribing discovery and QoS policy...
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...Disruptive Innovation: A new era of Crowdsourced Data Analytics! Abstract: The existing business paradigm of data analytics is set for a transformation. Today, companies are experimenting to replicate the “Outsourced data analytics” model to “Crowdsourced data analytics”. Companies like Kaggle, Crowdanalytix and others are hitting the headlines of top analytics blogs across the globe. The reason is that the new business model promises a drastic decrease in the cost of analytics for companies long with the flexibility to get the problem solved anytime with much less effort. In short, it’s not just crowdsourcing that is the novelty of the concept, but the manner in which it is put to use that steals the show. Abstract: The existing business paradigm of data analytics is set for a transformation. Today, companies are experimenting to replicate the “Outsourced data analytics” model to “Crowdsourced data analytics”. Companies like Kaggle, Crowdanalytix and others are hitting the headlines of top analytics blogs across the globe. The reason is that the new business model promises a drastic decrease in the cost of analytics for companies long with the flexibility to get the problem solved anytime with much less effort. In short, it’s not just crowdsourcing that is the novelty of the concept, but the manner in which it is put to use that steals the show. General Management General Management MBA Core, 2nd Year MBA Core, 2nd Year Ayush Malhotra NMIMS,Mumbai Ayush Malhotra ...
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...Spotlight on Making Your Company Data-Friendly Spotlight 64 Harvard Business Review December 2013 Artwork Chad Hagen Nonsensical Infographic No. 5 2009, digital hbr.org Analytics 3.0 In the new era, big data will power consumer products and services. by Thomas H. Davenport T hose of us who have spent years studying “data smart” companies believe we’ve already lived through two eras in the use of analytics. We might call them BBD and ABD—before big data and after big data. Or, to use a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analytics 2.0. Generally speaking, 2.0 releases don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul based on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information—big data—that was surely the case. Some of us now perceive another shift, fundamental and farreaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis December 2013 Harvard Business Review 65 Spotlight on Making Your Company Data-Friendly methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy. I’ll develop this argument in what follows, making...
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...INTRODUCTION TO BUSINESS ANALYTICS Sumeet Gupta Associate Professor Indian Institute of Management Raipur Outline • Business Analytics and its Applications • Analytics using Data Mining Techniques • Working with R BUSINESS ANALYTICS AND ITS APPLICATIONS What is Business Analytics? Analytics is the use of: data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions. Evolution of Business Analytics? • Operations research • Management science • Business intelligence • Decision support systems • Personal computer software Application Areas of Business Analytics • Management of customer relationships • Financial and marketing activities • Supply chain management • Human resource planning • Pricing decisions • Sport team game strategies Why Business Analytics? • There is a strong relationship of BA with: • profitability of businesses • revenue of businesses • shareholder return • BA enhances understanding of data • BA is vital for businesses to remain competitive • BA enables creation of informative reports Global Warming Poll Winner Sales Revenue Predicting Customer Churn Credit Card Fraud Loan Default Prediction Managing Employee Retention Market Segmentation Medical Imaging Analyzing Tweets stylus ...
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...Download Infographic: Must Read Books in Data Science / Analyt… Resources - Data Science, Analytics and Big Data discussions Home Blog Jobs Trainings Learning Paths 21/07/15 8:48 pm j ADVERTISEMENT Download Infographic: Must Read Books in Data Science / Analytics books data_science datavisualization Manish ! Data Hackers 28d Hey there ! You can think of this infographic as an ideal list of books to have in bookshelf of every data scientist / analyst. These books cover a wide range of topics and perspective (not only technical knowledge), which should help you become a well rounded data scientist. If you have other suggestions, please feel free to add them below: Books related to data science decisioning: When Genius Failed: The Rise and fall of Long-Term Capital Management A fast paced thriller, this book not only brings out how you can compete on data based decisions, but also why you need to keep human behavior in mind while taking decisions on data. Scoring Points: How Tesco Continues to Win Customer Loyalty this book brings out some of the practical challenges faced by Tesco and how they overcame them to create one of the biggest success story of customer loyalty. The Signal and the Noise: The Art and Science of Prediction . From the stock market to the poker table, from earthquakes to the economy, Nate Silver takes us on an enthralling insider’s tour of the high-stakes world of forecasting, showing how we can use information...
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...Analytics Concepts and Definitions Types of Analytics Descriptive Analytics: * Post Event Analytics * Add features to website and measure its effectiveness in form of clicks, link sharing, page views * Descriptive Analytics Tools -> Google Analytics, Optimizely Diagnostic Analytics: * Post Event Analytics * Analytics used to diagnose why something/phenomenon happened the way it did * It basically provides a very good understanding of a limited piece of the problem you want to solve. * Usually less than 10% of companies surveyed do this on occasion and less than 5% do so consistently. Predictive Analytics: * Used for Prediction of Phenomenon using past and current data statistics * Essentially, you can predict what will happen if you keep things as they are. * However, less than 1% of companies surveyed have tried this yet. The ones who have, found incredible results that have already made a big difference in their business. * Eg:- SAS, RapidMiner, Statistica Prescriptive Analytics: * Prescriptive analytics automatically synthesizes big data, multiple disciplines of mathematical sciences and computational sciences, and business rules, to make predictions and then suggests decision options to take advantage of the predictions. * It is considered final phase of Analytics Some Analytics Techniques used Linear Regression In statistics, linear regression is an approach for modeling the relationship between a scalar...
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...Date: 04-19-2015 The New Frontier: Data Analytics (Course title: Info System Decision Making) Professor: Clifton Howell Student: Deep Ajabani Data analysis is the process of finding the right data to answer your question, understanding the processes underlying the data, discovering the important patterns in the data, and then communicating your results to have the biggest possible impact. Analytics have been used in business since the management exercises were put into place by Frederick Winslow Taylor in the late 19th century. Henry Ford measured the time of each component in his newly established assembly line. But analytics began to command more attention in the late 1960s when computers were used in decision support systems. Since then, analytics have changed and formed with the development of enterprise resource planning (ERP) systems, data warehouses, and a large number of other software tools and processes. In later years the business analytics have exploded with the introduction to computers. This change has brought analytics to a whole new level and has made the possibilities endless. As far as analytics has come in history, and what the current field of analytics is today many people would never think that analytics started in the early 1900s with Mr. Ford. We are going to have a look on Big Data Analytics. Let’s have a look on advantages of big data analytics. It helps marketing companies build models based on historical data to predict...
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...O C T O B E R 2 0 11 m c k i n s e y g l o b a l i n s t i t u t e Are you ready for the era of ‘big data’? Brad Brown, Michael Chui, and James Manyika Radical customization, constant experimentation, and novel business models will be new hallmarks of competition as companies capture and analyze huge volumes of data. Here’s what you should know. The top marketing executive at a sizable US retailer recently found herself perplexed by the sales reports she was getting. A major competitor was steadily gaining market share across a range of profitable segments. Despite a counterpunch that combined online promotions with merchandizing improvements, her company kept losing ground. When the executive convened a group of senior leaders to dig into the competitor’s practices, they found that the challenge ran deeper than they had imagined. The competitor had made massive investments in its ability to collect, integrate, and analyze data from each store and every sales unit and had used this ability to run myriad real-world experiments. At the same time, it had linked this information to suppliers’ databases, making it possible to adjust prices in real time, to reorder hot-selling items automatically, and to shift items from store to store easily. By constantly testing, bundling, synthesizing, and making information instantly available across the organization— from the store floor to the CFO’s office—the rival company had become a different, far nimbler type of business...
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