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    Economics

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    Semester I Revised June 2012 Revised Business Economics I Syllabus for F.Y.B.Com from June 2012 onwards University of Mumbai Semester I Module I: Demand analysis Demand function and determinants of demand – Concept and importance of Elasticity of demand‚ Income‚ cross‚ promotional. Case studiesConsumer surplus‚ Demand forecasting: meaning significance and methods-case studies Module II Theory of production and cost Production function-short run and long run- Law of variable proportions- Isoquantproducers’

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    Big Data

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    Trang Vuong Big Data and Its Potentials Data exists everywhere nowadays. It flows to every area of the economy and plays an important role in the decision-making process. Indeed‚ “businesses‚ industries‚ governments‚ universities‚ scientists‚ consumers‚ and nonprofits are generating data at unprecedented levels and at an incredible pace” to ensure the accuracy and reliability of their data-driven decisions (Gordon-Murnane 30). Especially when technology and economy are growing at an unbelievable

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    First two lectures about big data. So why this surge? Isn’t about data at all Systems being able to process data Tools exploit and derive valuable nuggets NOT NEW: Walmart Wallstreet decades Why not out? Competition Teradata 20 years‚ contest MR patent Digital exhaust incr Know your Stats‚ gaga tweet sentiment analysis Correlation causation 2 Old: Centralized systems (data came from humans) Sun hardware Oracle software Moore’s law -> data grew. Data exhausts growing larger. Can’t

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    Secondary data

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    Secondary data refers to the data which an investigator does not collect himself for his purpose rather he obtains them from some other source‚ agency or office. In other words‚ this data has already been collected by some other source and an investigator makes use of it for his purpose. Secondary data is different from primary data on the basis of the sources of their collection. The difference between the two is relative - data which is primary at one place become secondary at another place.

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    Consumer Data

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    Handling Consumer Data Introduction When I visit my local Caltex Woolworths petrol station on “cheap fuel Wednesday” to cash in the 8c per litre credit that my Wife earned the previous Friday buying the groceries with our “Everyday Rewards” card‚ I did not‚ until researching this report‚ have any clue as to the contribution I was making to a database of frightening proportions and possibilities… nor that‚ when I also “decide” to pick up the on-sale‚ strategically-placed 600mL choc-milk‚ I am

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    Big data

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    Big data describes innovative methods and technologies to capture‚ distribute‚ manage and analyze larger-sized data sets with high rate and diverse structures that conventional data management methods are unable to handle. Digital data is now everywhere—in every sector public or private‚ economy‚ organization and customer of digital technology. There are many ways that big data can be used to create value across sectors of the global economy. It has demonstrated the capacity to improve predictions

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    Recording DATA

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    Activity 1 Reasons why organisations need to collect HR Data. It is important for organisations to collect and retain HR data as this will be key for strategic and HR planning. It will also help to have all the information necessary to make informed decisions‚ for the formulation and implementation of employment policies and procedures‚ to monitor fair and consistent treatment of staff‚ to contribute to National Statistics and to comply with statutory requirements. The key organisational

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    Data Mining

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    university CASE STUDY OF DATA MINING Summitted by Jatin Sharma Roll no -32. Reg. no 10802192 A case study in Data Warehousing and Data mining Using the SAS System. Data Warehouses The drop in price of data storage has given companies willing to make the investment a tremendous resource: Data about their customers

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    Data Breach

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    Americans leave long electronic trails of private information wherever they go. But too often‚ that data is compromised. When they shop—whether online or at brick and mortar stores—retailers gain access to their credit card numbers. Medical institutions maintain patient records‚ which are increasingly electronic. Corporations store copious customer lists and employee Social Security numbers. These types of data frequently get loose. Hackers gain entry to improperly protected networks‚ thieves steal employee

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    Data mining

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    University CS 450 Data Mining‚ Fall 2014 Take-Home Test N#1 Date: September 22nd‚ 2014 Final deadline for submission September 29th‚ 2014 Weighting: 5% Total number of points: 100 Instructions: 1. Attempt all questions. 2. This is an individual test. No collaboration is permitted for assessment items. All submitted materials must be a result of your own work. Part I Question 1 [20 points] Discuss whether or not each of the following activities is a data mining task.

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