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    data abstruction

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    WEEK-3 Data Abstraction Destructors • Destructors are functions without any type • The name of a destructor is the character ’~’ followed by class name – For example: ~clockType(); • A class can have only one destructor – The destructor has no parameters • Destructor automatically executes when the class object goes out of scope C++ Programming: Program Design Including Data Structures‚ Sixth Edition 2 Data Abstract‚ Classes‚ and Abstract Data Types • Abstraction – i

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    Data Integrity

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    Types of Data Integrity This section describes the rules that can be applied to table columns to enforce different types of data integrity. Null Rule A null rule is a rule defined on a single column that allows or disallows inserts or updates of rows containing a null (the absence of a value) in that column. Unique Column Values A unique value rule defined on a column (or set of columns) allows the insert or update of a row only if it contains a unique value in that column (or set of columns)

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    Data Case

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    Group Project Berk DeMarzo Data Case chapter 10 1. Collect price information for each stock from Yahoo! Finance Professor has provided it for us. The specific data of question 2-4 is in the appendix. We only include the functions and simple answers below every question. 2. Return=(P2-P1)/P1 3. mean monthly returns‚ standard deviations for the monthly returns and annual statistics Ticker | AAPL | ADM | BA | C | CAT | DE | HSY | MOT | PG | SIRI | WMT | YHOO | Mean

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    Data Gathering

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    survey especially through the Internet and questionnaires. The description of source of data and sample The researcher have a survey will be used in this proposed study where in 25 2nd year BSIT Students From Interface Computer College in Davao City‚ Because they have their own knowledge about the effects of Social Networking Sites on their academic performance. Procedure on Data Gathering We gather our data first‚ by conducting a survey. Second‚ we research on the internet. The Instruments The

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    DataBig Data and Future of Data-Driven Innovation A. A. C. Sandaruwan Faculty of Information Technology University of Moratuwa chanakasan@gmail.com The section 2 of this paper discuss about real world examples of big data application areas. The section 3 introduces the conceptual aspects of Big Data. The section 4 discuss about future and innovations through big data. Abstract: The promise of data-driven decision-making is now being recognized broadly‚ and there is growing enthusiasm

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    To Collect Data

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    Bachelor of Management Studies (B.M.S) Eligibility A) Passed H.S.C. examination of Maharashtra State Board of Secondary and Higher Sec- ondary Education‚ Pune or its equivalent Examination. B) Every candidate admitted to the Degree Course in the institute/affiliated college/ recognized institution conducting the course‚ shall have to register himself/herself with the University. Fees Structure FY Total Fees 16890 SY 15870 TY 15050 * Subject to revision of fees as per the directives from Director

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    Unemployment Data

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    Unemployment Data for May-June 7 of 2013 Employment (rose/fell/remained unchanged)‚ and the unemployment rate edged (up/down/stayed unchanged) to (?) percent in the last month. Employment for nonfarm payroll rose by 175‚000 in May‚ which includes construction and manufacturing companies. The unemployment rate stayed unchanged at 7.6 percent in the last month. Employment for professional and business services‚ food services and drinking places‚ and retail trade rose also. The summary states

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    RESEARCH PROCESS AND SECONDARY DATA Secondary data: Data previously collected and assembled for some project other than the one at hand {text:list-item} {text:list-item} Can almost always be gathered more quickly and inexpensively than primary data But may be outdated or may not exactly meet the researchers needs Secondary data‚ or historical data‚ are data previously collected and assembled for some project other than the one at hand. Secondary data often

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    (FOR BSIT) I. INTRODUCTION 1. Context of the Study 2. Statement of the Problem a. General Statement b. Specific Statement 3. Objective of the Study a. General Objective b. Specific Objective 4. Scope and Limitation of the Study 5. Significance of the Study 6. Motivation REVIEW OF RELATED LITERATURE AND STUDIES 1. Review of Related Literature 2. Review of Related Studies 3. Conceptual Framework 4. Operational Definition of Terms METHODOLOGY 1. Methods of Research 2. Data Gathering Techniques 3. Statistical

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    Qualitative Data

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    READING QUESTIONS DAY ANSWERS 1. Which of the following reading components do you find the easiest and the most difficult? Vocabulary Sentence/Syntax Getting the main idea Comprehension Others:_______________ Easiest: _____________ Difficult: _______________ EASIEST COMPONENTS: Vocabulary - 3 Sentence/Syntax - 11 Getting the main idea - 16 Comprehension - 1 DIFFICULT COMPONENTS: Vocabulary - 5 Sentence/Syntax - 9 Getting the main idea - 5 Comprehension – 11 2. Why do you

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