Valley Glen Voice OFFICIAL NEWSLETTER OF THE VALLEY GLEN NEIGHBORHOOD ASSOCIATION Volume 12‚ Issue 1 www.valleyglen.org Winter 2013 Your VGNA Board: Carlos Ferreyra President Dean Abston Vice President From Our President Carlos Ferreyra — Another year is upon us and we must ask ourselves‚ “Did the Mayans just run out of space on their rock when carving out their calendar?” As your Association (“VGNA”) moves forward this new year to continue its work of improving the quality of life
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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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Data Mining DeMarcus Montgomery Dr. Janet Durgin CIS 500 June 9‚ 2013 Determine the benefits of data mining to the businesses when employing 1. Predictive analytics to understand the behavior of customers Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model‚ which has‚ in turn been trained over your data‚ learning from the experience
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Lab – Data Analysis and Data Modeling in Visio Overview In this lab‚ we will learn to draw with Microsoft Visio the ERD’s we created in class. Learning Objectives Upon completion of this learning unit you should be able to: ▪ Understand the concept of data modeling ▪ Develop business rules ▪ Develop and apply good data naming conventions ▪ Construct simple data models using Entity Relationship Diagrams (ERDs) ▪ Develop entity relationships and define
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INFORMATION RESOURCES Part I Introduction to Dta Modeling and MSAccess Code FMITB02004 Course title Information Resourses Course volume 3‚0 cr. (4‚50 ECTS cr.) Teaching methods (Full-time‚ daytime studies): Lectures - 16 h per semestre Laboratory works - 32 h per semestre Individual work - 72 h per semester Course aim Understandig of models and system of information resourses. Jelena Mamčenko Introduction to Data Modeling and MSAccess CONTENT 1 2 3 4 5 6 Introduction to Data Modeling .
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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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Professor Faleh Alshamari Submitted by: Wajeha Sultan Final Project Hashing: Open and Closed Hashing Definition: Hashing index is used to retrieve data. We can find‚ insert and delete data by using the hashing index and the idea is to map keys of a given file. A hash means a 1 to 1 relationship between data. This is a common data type in languages. A hash algorithm is a way to take an input and always have the same output‚ otherwise known as a 1 to 1 function. An ideal hash function is
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Module 815 Data Structures Using C M. Campbell © 1993 Deakin University Module 815 Data Structures Using C Aim After working through this module you should be able to create and use new and complex data types within C programs. Learning objectives After working through this module you should be able to: 1. Manipulate character strings in C programs. 2. Declare and manipulate single and multi-dimensional arrays of the C data types. 3. Create‚ manipulate and manage C pointers
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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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research in secondary sources‚ such as journals‚ magazines‚ or books. We can also use it as the focus of our writing project. In primary data collection‚ we collect the data ourselves by using methods such as interviews and questionnaires. The key point here is that the data we collect is unique to us and our research and‚ until we publish‚ no one else has access to it. There are many methods of collecting primary data and the main methods include: • QUESTIONNAIRES • INTERVIEWS • FOCUS GROUP
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