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DSA

Data Structure and Algorithm<br />
<br />
Ch#_1 C++ Premier<br />
Ch#_2 Object Oriented Design<br />
Ch#_3 Arrays, LinkList , Recursion<br />
Ch#_4 Analysis Tools<br />
Ch#_5 Stack , Queues And Deques<br />
Ch#_6 list and Iterator ADTs<br />
Ch#_7 Trees<br />
Ch#_8 Heaps and Priority Queues<br />
Ch#_9 Hash Tables, Maps, and Skip Lists<br />
Ch#_10  Search Trees<br />
Ch#_11 Sorting, Sets, and Selection<br />
Ch#_12 Strings and Dynamic Programming<br />
Ch#_13 Graph Algorithms<br />
Ch#_14 Memory Management and B-Trees<br />
Appendix<br />
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DSA

Data Structure and Algorithm Ch#_1 C++ Premier Ch#_2 Object Oriented Design Ch#_3 Arrays, LinkList , Recursion Ch#_4 Analysis Tools Ch...

IT Group Of Cs

Object Oriented Programming C++ 4th Edition .pdf

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<span style="color: #6aa84f; font-size: x-large;">Programming Fundamentals</span></h2>
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<span style="color: red;"> For 1st Semester BsCs</span></h2>
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Overview<br />
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<ul>
<li> Introduction 1 1</li>
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<li> C++ Programming Basics 29  </li>
<li> 3 Loops and Decisions 75</li>
<li> 4Structures 131</li>
<li> 5 Functions 161 </li>
<li> 6 Objects and Classes 215</li>
<li> 7 Arrays and Strings 263 </li>
<li> 8 Operator Overloading 319 </li>
<li> 9 Inheritance 371</li>
<li> 10 Pointers 429</li>
<li> 11 Virtual Functions 503  </li>
<li> 12 Streams and Files 567 </li>
<li> 13 Multifile Programs 633</li>
<li> 14 Templates and Exceptions 681 </li>
<li> 15 The Standard Template Library 725 </li>
<li> 16 Object-Oriented Software Development 801</li>
<li> A ASCII Chart 849</li>
<li> B C++ Precedence Table and Keywords 859</li>
<li> C Microsoft Visual C++ 863</li>
<li> D Borland C++Builder 871</li>
<li> E Console Graphics Lite 881</li>
<li> F STL Algorithms and Member Functions 895 </li>
<li> G Answers to Questions and Exercises 913</li>
<li> H Bibliography 977 Index 981</li>
</ul>
<blockquote class="tr_bq">
<span style="color: red; font-size: large;"><b>Info ::</b></span> </blockquote>
                            Object Oriented Programming C++ 4th Edition by Robert Lafore .<br />
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Object Oriented Programming C++ 4th Edition .pdf

Programming Fundamentals For 1st Semester BsCs Overview Introduction 1 1 The Big Picture 9 2 C++ Programming Basics 29 3 ...

Statitics Notes of Mam Nazima Anwar

<h2 style="text-align: center;">
Here are notes of  15_BSCS_3rd semester</h2>
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<h1 align="CENTER">
Definitions, Uses, Data Types, and Levels of Measurement</h1>
<h4>
Lesson Overview</h4>
<ul>
<li>What is Statistics: Descriptive Statistics <i>vs</i> Inferential Statistics</li>
<li>General terms Used Throughout Statistics<ul>
<li>Population</li>
<li>Sample</li>
<li>Parameter</li>
<li>Statistic</li>
</ul>
</li>
<li>Basic Mathematics for Statistics</li>
<li>Accuracy <i>vs.</i> Precision</li>
<li>Uses and Abuses of Statistics</li>
<li>Types of Data<ol>
<li>Qualitative</li>
<li>Quantitative: Discrete <i>vs.</i> Continuous</li>
</ol>
</li>
<li>Levels of Measurement: Nominal, Ordinal, Interval, Ratio</li>
<li>Homework</li>
</ul>
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<span style="text-align: start;">The term </span><a href="https://www.blogger.com/u/2/null" name="STATISTICS" style="text-align: start;"><b>statistics</b></a><span style="text-align: start;"> has several basic meanings. First, statistics is a subject or field of study closely related to mathematics. This four week, sixteen lesson unit will first introduce and briefly cover the area known as descriptive statistics.</span></div>
<table style="width: 100%px;"><tbody>
<tr><td align="CENTER" bgcolor="LIGHTBLUE"><a href="https://www.blogger.com/u/2/null" name="DESC"><b>Descriptive statistics</b></a> generally characterizes or describes a set of data elements by graphically displaying the information or describing its central tendancies and how it is distributed.</td></tr>
</tbody></table>
The last half of the course will cover inferential statistics.<br />
<table bgcolor="LIGHTBLUE" style="width: 100%px;"><tbody>
<tr><td align="CENTER"><a href="https://www.blogger.com/u/2/null" name="INFER"><b>Inferential statistics</b></a> tries to infer information about a population<br />by using information gathered by sampling.</td></tr>
</tbody></table>
<table bgcolor="LIGHTBLUE" style="width: 100%px;"><tbody>
<tr><td align="CENTER"><b>Statistics</b>: The collection of methods used in planning an experiment<br />and analyzing data in order to draw accurate conclusions.</td></tr>
</tbody></table>
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<a href="https://www.blogger.com/u/2/null" name="GENERAL">General</a> Terms Used Throughout Statistics</h3>
<table bgcolor="LIGHTBLUE" style="width: 100%px;"><tbody>
<tr><td align="CENTER"><b>Population</b>: The complete set of data elements is termed the population.</td></tr>
</tbody></table>
The term population will vary widely with its application. Examples could be any of the following proper subsets: animals; primates; human beings; <i>homo sapiens</i>; U.S. citizens; who are attending Andrews University, as graduate students, in the School of Education, as Masters students, female, last name starting with S, who web registered.<br />
<table bgcolor="LIGHTBLUE" style="width: 100%px;"><tbody>
<tr><td align="CENTER"><b>Sample</b>: A sample is a portion of a population selected for further analysis.</td></tr>
</tbody></table>
How samples are obtained or types of sampling will be studied in lesson 7. Most any of the examples above for population could serve as a sample for the next higher level data set.<br />
<table bgcolor="LIGHTBLUE" style="width: 100%px;"><tbody>
<tr><td align="CENTER"><b>Parameter</b>: A parameter is a characteristic of the whole population.</td></tr>
</tbody></table>
<table align="CENTER" bgcolor="LIGHTBLUE" style="width: 100%px;"><tbody>
<tr><td align="CENTER"><b>Statistic</b>: A statistic is a characteristic of a sample, presumably measurable.</td></tr>
</tbody></table>
The plural of statistic just above is another basic meaning of statistics.<br />
Assume there are 8 students in a particular statistics class, with 1 student being male. Since 1 is 12.5% of 8, we can say 13% are male. The 13% represents a <i>parameter</i> (not a <i>statistic</i>) of the class because it is based on the entire population. If we assume this class is representative of all classes, and we treat this 1 student as a sample drawn from a larger population, then the 13% becomes a statistic.<br />
<table align="CENTER" border="5"><tbody>
<tr><td align="CENTER">Remember: <b>Parameter</b> is to <b>Population</b> as <b>Statistic</b> is to <b>Sample</b>.</td></tr>
</tbody></table>
Inferential statistics is used to draw conclusions about a population by studying a sample. It is not guesswork! We test hypotheses about a parameter's value with a certain risk of being wrong. That risk is carefully specified. Also, descriptive and inferential statistics are not mutually exclusive. The inferences made about a population from a sample help describe that population. We also tend to use Roman letters for statistics and Greek letters for parameters.<br />
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<h3 align="CENTER">
<a href="https://www.blogger.com/u/2/null" name="BM">Basic Mathematics for Statistics</a></h3>
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<span style="text-align: start;">This course will avoid complex models utilizing complicated mathematics. You will need to be familiar with, however, the fundamental arithmetic operations, elementary algebra, and some basic symbolism.</span></div>
An interesting subset of the natural numbers generated by addition are called Triangular Numbers. These are so called because these are the total number of dots, if we arrange the dots in a triangle with one additional dot in each layer.<br />
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<center>
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<tr><td></td><td></td><td></td><td>•</td><td></td><td></td><td></td><td></td></tr>
<tr><td></td><td></td><td>•</td><td></td><td>•</td><td></td><td></td></tr>
<tr><td></td><td>•</td><td></td><td>•</td><td></td><td>•</td><td></td></tr>
<tr><td>•</td><td></td><td>•</td><td></td><td>•</td><td></td><td>•</td></tr>
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<span style="text-align: start;">The triangular numbers thus are: 0, 1, 3, 6, 10, 15, 21, ....</span></div>
Suppose we wish to add together the first 100 natural numbers, which is equivalent to finding the 100th triangular number. One way to do this is by grouping them as follows:<br />
<table><tbody>
<tr><td>T<sub>100</sub></td><td>=</td><td>(1 +100) + (2+99) + (3 + 98) + ... + (50 + 51)</td></tr>
<tr><td></td><td>=</td><td>101• 50</td></tr>
<tr><td></td><td>=</td><td>101• 100/2</td></tr>
</tbody></table>
In general we write: <img alt="[The sum over i from 1 to n of i =n(n+1)/2]" height="100" src="https://www.andrews.edu/~calkins/math/webtexts/trinum.gif" width="350" /> where mathematicians use the capital Greek letter <img alt="[Sigma]" src="https://www.andrews.edu/~calkins/math/webtexts/sum.gif" /> (sigma) to represent <b>summation</b>. Your teacher has a particular fondness for this symbol since the first computer he had much access to had that nickname.<br />
There are three important rules for using the summation operator:<br />
<ol>
<li>Since multiplication distributes over addition, the sum of a constant times a set of numbers is the same as the constant times the sum of the set of numbers.<b>Example:</b> <i>Cx</i><sub>1</sub> + <i>Cx</i><sub>2</sub> + ... + <i>Cx</i><sub><i>n</i></sub> = <i>C</i>(<i>x</i><sub>1</sub> + <i>x</i><sub>2</sub> + ...+ <i>x</i><sub><i>n</i></sub>)</li>
<li>The sum of a series of constants is the same as <i>N</i> times the constant, where <i>N</i> represents how many constants there are.<b>Example:</b> 4 + 4 + 4 + 4 + 4 = 5 × 4 = 20.</li>
<li>Since addition is commutative, the total sum of two or more scores for several individuals can be achieved either by summing the scores separately and then combining them or by summing an individual's scores and then combining them.<b>Example:</b> Joe got scores of 500 and 550 for his verbal and quantitative SAT scores whereas Jim got scores of 520 and 510, respectively. 500 + 550 + 520 + 510 = 1050 + 1030 = 500 + 520 + 550 + 510 = 1020 + 1060 = 2090.</li>
</ol>
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<span style="text-align: start;">In addition to the operations of addition, subtraction, multiplication, and division, several other arithmetic operators often appear. </span><b style="text-align: start;">Exponentiation</b><span style="text-align: start;"> and </span><b style="text-align: start;">absolute value</b><span style="text-align: start;"> are two such. Also, various symbols of inclusion (parentheses, brackets, braces, vincula) are used.</span></div>
Exponentiation is a general term which includes squaring (12<sup>2</sup>=144), cubing (6<sup>3</sup>=216), and square roots (16<sup>½</sup>=<img src="https://www.andrews.edu/~calkins/math/webtexts/sqrt.gif" />(16)=4. When the square root symbol (surd and symbol of inclusion, in recent history a vinculum, but historically parentheses) is used, we general (although not quite always) mean only the positive square root.<br />
The absolute value operator indicates the distance (always non-negative) a number is from the origin (zero). The symbol used is a vertical line on either side of the operand. Thus, if <i>x</i>>0, then |<i>x</i>|=<i>x</i>, if <i>x</i><0, then |<i>x</i>|=-<i>x</i>, and if <i>x</i>=0, |<i>x</i>|=0. <img src="https://www.andrews.edu/~calkins/math/webtexts/sqrt.gif" />(<i>x</i><sup>2</sup>)=|x|.<br />
There is a proscribed <b>order for arithmetic operations</b> to be performed.<br />
<b>Example:</b> If we write 4 × 5 + 3 it is conventional to multiply the 4 and 5 together before adding the 3 and thus obtain 23. Some calculators are algebraic and handle this appropriately, others do not.<br />
<b>P</b>arentheses and other symbols of inclusion are used to modify the normal order of operations. We say these symbols of inclusion have the highest priority or precidence.<br />
<b>E</b>xponentiation is done next. There is confusion when exponents are stacked which we will not deal with here except to say computer scientists tend to do it from left to right while mathematicians know that is wrong.<br />
<b>M</b>ultiplication and <b>D</b>ivision are done next, in order, from <b>left to right</b>.<br />
<b>A</b>ddition and <b>S</b>ubtraction are done next, in order, from <b>left to right</b>.<br />
A <b>mnemonic</b> such as <b>P</b>lease <b>E</b>at <b>M</b>iss <b>D</b>aisy's <b>A</b>pple <b>S</b>auce can be useful for remembering the proper order of operation.<br />
<h3 align="CENTER">
<a href="https://www.blogger.com/u/2/null" name="ACCURATE">Accuracy <i>vs.</i> Precision</a></h3>
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<span style="text-align: start;">The distinction between accuracy and precision, reviewed in </span>Numbers lesson 9<span style="text-align: start;">, is very important.</span></div>
This ties in with significant figures, and proper rounding of results. I have several major concerns regarding significant digits.<br />
<ol>
<li>There needs to be sufficient (not to few). Slide rule accuracy or three significant digits has a long-standing precident in science. We are not doing science here so two may suffice, but rarely one.</li>
<li>There should not be too many significant digits. Generally, more than 5 is probably a joke, especially in the "softer" sciences. Thus representing <span>1/3</span> or <span>1/7</span> with infinite precision (by indicating the repeated unit) should not occur.</li>
<li>Care must be taken so that a primary statistics (such as variance) is not incorrectly derived from a secondary statistic (such as standard deviation) in such a way that accuracy is lost. We will discuss this more in textbook Chapter 3.</li>
<li>A mean and standard deviation or mean and margin of error should be given to compatible precision.</li>
<li>There are proper rules, but they are difficult to explain to the general public. Thus every statistics book gives its own heuristic.</li>
</ol>
<h3 align="CENTER">
<a href="https://www.blogger.com/u/2/null" name="ABUSE">Uses and Abuses of Statistics</a></h3>
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<span style="text-align: start;">Most of the time, samples are used to infer something (draw conclusions) about the population. If an experiment or study was done cautiously and results were interpreted without </span>bias<span style="text-align: start;">, then the conclusions would be accurate. However, occasionally the conclusions are inaccurate or inaccurately portrayed for the following reasons:</span></div>
<ul align="CENTER">
<li>Sample is too small.</li>
<li>Even a large sample may not represent the population.</li>
<li>Unauthorized personnel are giving wrong information that the public will take as truth. A possibility is a company sponsoring a statistics research to prove that their company is better.</li>
<li>Visual aids may be correct, but emphasize different aspects. Specific examples include graphs which don't start at zero thus exaggerating small differences and charts which misuse area to represent proportions. Often a chart will use a symbol which is both twice as long and twice as high to represent something twice as much. The area, in this case however, is <b>four times</b> as much!</li>
<li>Precise statisitics or parameters may incorrectly convey a sense of high accuracy.</li>
<li>Misleading or unclear percentages are often used.</li>
</ul>
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<span style="text-align: start;">Statistics are often abused. Many examples could be added, (even books have been written) but it will be more instructive and fun to find them on your own.</span></div>
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<a href="https://www.blogger.com/u/2/null" name="DATA_TYPE">Types of Data</a></h3>
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<span style="text-align: start;">A dictionary defines data as facts or figures from which conclusions may be drawn. Thus, technically, it is a collective, or plural noun. Some recent dictionaries acknowledge popular usage of the word data with a singular verb. However we intend to adhere to the traditional "English" teacher mentality in our grammar usage—sorry if "data are" just doesn't sound quite right! (My mother and step-mother were both English teachers, so clearly no offense is intended above.) </span><b style="text-align: start;">Datum</b><span style="text-align: start;"> is the singular form of the noun data. Data can be classified as either numeric or nonnumeric. Specific terms are used as follows:</span></div>
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<li><table bgcolor="LIGHTBLUE" style="width: 95%px;"><tbody>
<tr><td><b>Qualitative</b> data are nonnumeric.</td></tr>
</tbody></table>
{Poor, Fair, Good, Better, Best}, colors (ignoring any physical causes), and types of material {straw, sticks, bricks} are examples of qualitative data.<br />
Qualitative data are often termed <b>catagorical data.</b> Some books use the terms <b>individual</b> and <b>variable</b> to reference the objects and characteristics described by a set of data. They also stress the importance of exact definitions of these variables, including what units they are recorded in. The reason the data were collected is also important.<br />
</li>
<li><table bgcolor="LIGHTBLUE" style="width: 95%px;"><tbody>
<tr><td><b>Quantitative</b> data are numeric.</td></tr>
</tbody></table>
Quantitative data are further classified as either discrete or continuous.<br />
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<li><table bgcolor="LIGHTBLUE" style="width: 89%px;"><tbody>
<tr><td><b>Discrete data</b> are numeric data that have a finite number of possible values.</td></tr>
</tbody></table>
A classic example of discrete data is a finite subset of the counting numbers, {1,2,3,4,5} perhaps corresponding to {Strongly Disagree... Strongly Agree}.<br />
Another classic is the spin or electric charge of a single electron. <b>Quantum Mechanics</b>, the field of physics which deals with the very small, is much concerned with discrete values.<br />
When data represent <b>counts</b>, they are discrete. An example might be how many students were absent on a given day. Counts are usually considered exact and integer. Consider, however, if three tradies make an absence, then aren't two tardies equal to 0.67 absences?<br />
</li>
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<tr><td><b>Continuous</b> data have infinite possibilities: 1.4, 1.41, 1.414, 1.4142, 1.141421...</td></tr>
</tbody></table>
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<span style="text-align: start;">The </span>real numbers<span style="text-align: start;"> are continuous with no gaps or interruptions. Physically measureable quantities of length, volume, time, mass, </span><i style="text-align: start;">etc.</i><span style="text-align: start;"> are generally considered continuous. At the physical level (microscopically), especially for mass, this may not be true, but for normal life situations is a valid assumption.</span></div>
The structure and nature of data will greatly affect our choice of analysis method. By structure we are referring to the fact that, for example, the data might be pairs of measurements. Consider the legend of Galileo dropping weights from the leaning tower of Pisa. The times for each item would be paired with the mass (and surface area) of the item. Something which Galileo clearly did was measure the time it took a pendulum to swing with various amplitudes. (<b>Galileo Galilei</b> is considered a founder of the experimental method.)<br />
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<a href="https://www.blogger.com/u/2/null" name="LEVELS">Levels of Measurement</a></h3>
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<span style="text-align: start;">The experimental (scientific) method depends on physically measuring things. The concept of measurement has been developed in conjunction with the concepts of numbers and units of measurement. Statisticians categorize measurements according to levels. Each level corresponds to how this measurement can be treated mathematically.</span></div>
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<li><table bgcolor="LIGHTBLUE" style="width: 95%px;"><tbody>
<tr><td><b>Nominal</b>: Nominal data have no order and thus only gives <b>names</b> or labels to various categories.</td></tr>
</tbody></table>
</li>
<li><table bgcolor="LIGHTBLUE" style="width: 95%px;"><tbody>
<tr><td><b>Ordinal</b>: Ordinal data have <b>order</b>, but the interval between measurements is not meaningful.</td></tr>
</tbody></table>
</li>
<li><table bgcolor="LIGHTBLUE" style="width: 95%px;"><tbody>
<tr><td><b>Interval</b>: Interval data have meaningful intervals between measurements, but there is no true starting point (zero).</td></tr>
</tbody></table>
</li>
<li><table bgcolor="LIGHTBLUE" style="width: 95%px;"><tbody>
<tr><td><b>Ratio</b>: Ratio data have the highest level of measurement. Ratios between measurements as well as intervals are meaningful because there is a starting point (zero).</td></tr>
</tbody></table>
</li>
</ol>
Nominal comes from the Latin root <b>nomen</b> meaning <b>name</b>. Nomenclature, nominative, and nominee are related words. <b>Gender</b> is nominal. (Gender is something you are born with, whereas <b>sex</b>is something you should get a license for.)<br />
<b>Example 1: Colors</b><br />To most people, the colors: black, brown, red, orange, yellow, green, blue, violet, gray, and white are just names of colors.<br />
To an electronics student familiar with color-coded resistors, this data is in ascending order and thus represents at least ordinal data.<br />
To a physicist, the colors: red, orange, yellow, green, blue, and violet correspond to specific wavelengths of light and would be an example of ratio data.<br />
<b>Example 2: Temperatures</b><br />What level of measurement a temperature is depends on which temperature scale is used.<br />Specific values: 0°C = 32°F = 273.15 K = 491.69°R     100°C = 212°F = 373.15 K = 671.67°R     -17.8°C = 0°F = 255.4 K = 459.67°R<br />where C refers to Celsius (or Centigrade before 1948); F refers to Fahrenheit; K refers to Kelvin; R refers to Rankine.<br />
Only Kelvin and Rankine have true zeroes (starting point) and ratios can be found. Celsius and Fahrenheit are interval data; certainly order is important and intervals are meaningful. However, a 180° dashboard is not twice as hot as the 90° outside temperature (Fahrenheit assumed)! Rankine has the same size degree as Fahrenheit but is rarely used. To interconvert Fahrenheit and Celsius, see Numbers lesson 12. (Note that since 1967, the use of the degree symbol on tempertures Kelvin is no longer proper.)<br />
Although ordinal data should not be used for calculations, it is not uncommon to find averages formed from data collected which represented Strongly Disagree, ..., Strongly Agree! Also, averages of nominal data (zip codes, social security numbers) is rather meaningless!<br />
<br />

Statitics Notes of Mam Nazima Anwar

Here are notes of 15_BSCS_3rd semester click here to read online click here to read exercise or Download it now. Definition...

Data Privacy Questions And Answers

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<span style="font-family: "times new roman" , "serif"; font-size: 22.0pt; line-height: 107%;">Data Privacy Questions And Answers<o:p></o:p></span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;"><span style="color: #6aa84f;">Q1-What is Data Privacy?</span><o:p></o:p></span></div>
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<span style="color: red;"><span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;">Ans:</span><span style="font-family: "times new roman" , serif; font-size: 28pt; line-height: 107%;"> </span></span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;">       </span><b><span style="font-family: "times new roman" , serif; line-height: 107%;">  <o:p></o:p></span><span style="background-color: white; color: #222222; font-family: "arial" , sans-serif;">Data privacy</span><span style="background-color: white; color: #222222; font-family: "arial" , sans-serif;">, also called information </span><span style="background-color: white; color: #222222; font-family: "arial" , sans-serif;">privacy</span><span style="background-color: white; color: #222222; font-family: "arial" , sans-serif;">, is the aspect of information technology (IT) that deals with the ability an organization or individual has to determine what </span><span style="background-color: white; color: #222222; font-family: "arial" , sans-serif;">data</span><span style="background-color: white; color: #222222; font-family: "arial" , sans-serif;"> in a computer system can be shared with third parties.</span></b></div>
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<span style="font-family: "times new roman" , serif; font-size: 16pt; line-height: 22.8267px;"><span style="color: #6aa84f;">Q2-What steps to protect yourself online ?</span><o:p></o:p></span></div>
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<span style="color: red;"><span style="font-family: "times new roman" , serif; font-size: 16pt; line-height: 22.8267px;">Ans:</span><span style="font-family: "times new roman" , serif; font-size: 28pt; line-height: 39.9467px;"> </span></span></div>
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<ul>
<li>      <b>   Make Sure Your Are using a trusted internet Network</b></li>
<li><b>         Make Sure Your using trusted Web Browser Like Chrome , Opera etc</b></li>
<li><b>         Make Sure Your Website Host is Trusted</b></li>
<li><b>         Make Sure Only Trusted Persons Can use Your Device </b></li>
<li><b>         Make Sure You have no Bad Affair With <span style="color: #3d85c6;">ITs </span>.</b></li>
</ul>
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<span lang="EN-GB" style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;"><span style="color: #6aa84f;">Q3-What is </span></span><span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;"><span style="color: #6aa84f;">Mix & Match Method ?</span><o:p></o:p></span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;">          People
often like to use names, places, dates and other familiar things associated
with them. This can be too easy to guess, but you can make your password
stronger by combining 2 into one. For instance, you can  use your dog’s
name and the date you </span><span style="font-family: "times new roman" , serif; font-size: 16pt;">graduated together for a
super strong password.</span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;"><br /></span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;"><span style="color: #6aa84f;">Q4- What are examples of
personal data worth protecting ?</span><o:p></o:p></span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;">        Following are examples<o:p></o:p></span></div>
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<ul>
<li>      <b>  Online Personal Drive</b></li>
<li><b>        Bank account Numbers</b></li>
<li><b>        Credit Card Numbers</b></li>
<li><b>        Personal Health Information</b></li>
</ul>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;"><span style="color: #6aa84f;">Q5-What is privacy means ?<o:p></o:p></span></span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;">          Privacy is mainly
defined as a human value consisting of 4 rights: solitude, anonymity, intimacy
and reserve.<o:p></o:p></span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;"><span style="color: #6aa84f;">Q6-What is Identity Theft ?</span><o:p></o:p></span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;">

</span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;"><span style="color: red;">Ans:</span> </span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;">            This is where unauthorized individuals use another person’s name to receive
credit cards, driver’s licenses and various other items in their victim’s name.<o:p></o:p></span></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 16.0pt; line-height: 107%;"><br /></span></div>
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<b><span style="color: #38761d;"><span style="font-family: "arial" , sans-serif;">Q7 Will smart cards, or ID cards that use bio-metric enhancements, help protect privacy</span><span style="font-family: "arial" , "tahoma" , "helvetica" , "freesans" , sans-serif;"><span style="font-family: "arial" , sans-serif;">?</span></span></span></b></div>
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<b><span style="font-family: "arial" , "tahoma" , "helvetica" , "freesans" , sans-serif;"><span style="font-family: "arial" , sans-serif;"><span style="color: red;"><br /></span></span></span></b></div>
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<b><span style="font-family: "arial" , "tahoma" , "helvetica" , "freesans" , sans-serif;"><span style="font-family: "arial" , sans-serif;"><span style="color: red;">Ans:</span></span></span></b></div>
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<b><span style="font-family: "arial" , "tahoma" , "helvetica" , "freesans" , sans-serif;"><span style="font-family: "arial" , sans-serif;">           </span></span></b></div>
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<b><span style="font-family: "arial" , "tahoma" , "helvetica" , "freesans" , sans-serif;"><span style="font-family: "arial" , sans-serif;"><span style="color: #38761d;"><br /></span></span></span></b></div>
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<span style="background-color: white; font-family: "arial" , "tahoma" , "helvetica" , "freesans" , sans-serif;"><span style="font-family: "arial" , sans-serif;"><b><span style="color: #38761d;">Q8 Will privacy policies create a situation that will protect illegal acts?</span></b></span></span></div>
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<span style="background-color: white; font-family: "arial" , "tahoma" , "helvetica" , "freesans" , sans-serif;"><span style="font-family: "arial" , sans-serif;"><b><span style="color: #38761d;"><br /></span></b></span></span></div>
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<span style="background-color: white; font-family: "arial" , "tahoma" , "helvetica" , "freesans" , sans-serif;"><span style="font-family: "arial" , sans-serif;"><b><span style="color: red;">Ans: </span></b></span></span></div>
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<span style="background-color: white; font-family: "arial" , "tahoma" , "helvetica" , "freesans" , sans-serif;"><span style="font-family: "arial" , sans-serif;"><b><span style="color: red;">         </span></b></span></span></div>
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<span style="font-family: "arial" , sans-serif; font-size: 12pt;"><span style="color: #38761d;">Q9 What do we mean by a data protection policy?</span></span></h3>
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<span style="color: red; font-family: "arial" , sans-serif;"><b>Ans:</b></span></div>
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<span style="color: red; font-family: "arial" , sans-serif;"><b><br /></b></span></div>
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<b style="background-color: white; font-family: Arial, Tahoma, Helvetica, FreeSans, sans-serif;"><span style="font-family: "arial" , sans-serif; line-height: 22.8267px;"><span style="color: #38761d;">Q10 Why  data privacy too important?</span></span></b></div>
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<b style="background-color: white; font-family: Arial, Tahoma, Helvetica, FreeSans, sans-serif;"><span style="font-family: "arial" , sans-serif; line-height: 22.8267px;"><span style="color: red;">Ans:</span></span></b></div>
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Data Privacy Questions And Answers

Data Privacy Questions And Answers Q1-What is Data Privacy? Ans: Data privacy , also called information privacy , is ...

Data Mining ppt

Data Mining in simple way with simple Examples<br />
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 To read online <a href="https://drive.google.com/open?id=0B4Dvt6TbULIrTWhKaU1HcV9vVU0">click here</a><br />
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Data Mining ppt

Data Mining in simple way with simple Examples To read online click here

Data Mining

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                    BsCs    3rd Semester                    DBMS</h2>
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       <span style="font-size: x-large;"> Data Mining Questions  and their  Answers</span></h2>
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<span style="font-size: 14px;"><b><span style="color: green;">Q # 1 What is Data mining ?</span></b><br />
 A Data mining is  knowledge discovery in databases. It is  extraction 
of interesting (non-trivial, implicit, previously unknown and 
potentially useful) information or patterns from data in large 
databases.</span><br />
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<span style="font-size: 14px;"><b><span style="color: green;">Q# 2 What is difference between OLAP and data mining ?</span></b><br />
 A  OLAP - (On-line Analytical Processing )provides you with a very good
 view of what is happening, but can not predict what will happen in the 
future or why it is happening where as data mining is group of 
techniques that find relationships that have not previously been 
discovered.</span><br />
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<span style="font-size: 14px;"><b><span style="color: green;">Q# 3 What are the functionalities of Data Mining?</span></b><br />
 •<b>Knowledge Prediction  </b></span><span style="font-size: 14px;"> Data mining uses known data for prediction/forecasting. It is also known as predictive data.</span></div>
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<span style="font-size: 14px;">•<b>Knowledge Description - </b></span><span style="font-size: 14px;">Concrete information gleaned from data is known as data description. It is also known as descriptive data. </span><br />
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<b><span style="color: green;"><span style="font-size: 14px;">Q# 4 What are  some of the tasks of data mining?</span></span></b></div>
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<span style="font-size: 14px;">A Following activities are carried out during data mining</span></div>
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<ul>
<li style="margin-left: 40px;">
    <span style="font-size: 14px;">Classification [Predictive]</span></li>
<li style="margin-left: 40px;">
    <span style="font-size: 14px;">Clustering [Descriptive]</span></li>
<li style="margin-left: 40px;">
    <span style="font-size: 14px;">Association Rule Discovery [Descriptive]</span></li>
<li style="margin-left: 40px;">
    <span style="font-size: 14px;">Sequential Pattern Discovery [Descriptive]</span></li>
<li style="margin-left: 40px;">
    <span style="font-size: 14px;">Regression [Predictive]</span></li>
<li style="margin-left: 40px;">
    <span style="font-size: 14px;">Deviation Detection [Predictive]</span></li>
</ul>
<div style="margin-left: 40px;">
<span style="font-size: 14px;"><b><span style="color: green;">Q# 5  What do you mean by preprocessing of data in data mining ? </span></b><br />
   A Before data is mined it has to be preprocessed. It consists of following three stages   </span></div>
<ul style="margin-left: 40px;">
<li>
    
     <b>Data cleaning </b>- Real world data is dirty so need to be cleaned<br />

</li>
<li>
    
     <b>Data reduction</b>- Remove data not useful for mining<br />

</li>
<li>
    
     <b>Data transformation</b> - Syntactic transformation<br />

</li>
</ul>
<div style="margin-left: 40px;">
<span style="font-size: 14px;"><b><span style="color: green;">Q# 6 Describe causes and tasks of data cleaing?</span></b><br />
   A <b>Causes of Dirty Data</b></span></div>
<ul>
<li style="margin-left: 40px;">
    <span style="font-size: 14px;">Missing values</span></li>
<li style="margin-left: 40px;">
    <span style="font-size: 14px;">Noisy data (Human/Machine Errors)</span></li>
<li style="margin-left: 40px;">
    <span style="font-size: 14px;">Inconsistent data</span></li>
</ul>
<span style="font-size: 14px;"><b>            Data cleaning tasks</b></span></div>
<ul>
<li style="margin-left: 40px;">
   <span style="font-size: 14px;">Handling missing values</span></li>
<li style="margin-left: 40px;">
   <span style="font-size: 14px;">Identify outliers and smooth out noisy data</span></li>
<li style="margin-left: 40px;">
   <span style="font-size: 14px;">Correct inconsistent data</span></li>
</ul>
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<br />
<b><span style="color: green;"><span style="font-size: 14px;">     Q# 7 Jot down the sources of data mining?</span></span></b><br />
<br />
<b><span style="color: green;"><span style="font-size: 14px;"> </span></span></b>   <span style="font-size: 14px;">1_ Data Base</span><br />
<span style="font-size: 14px;">    2_Text Documents</span><br />
<span style="font-size: 14px;">    3_Pictures(images)</span><br />
<span style="font-size: 14px;">    4_Web. </span> <br />
<b><span style="color: green;"><span style="font-size: 14px;">    Q# 8 How Data Mining helps in prediction ?</span></span></b><br />
                   <b> </b><span style="font-size: 14px;">D</span><span style="font-size: 14px;">ata Mining visits and analyzes the previous recorded data on the basis of which it predicts about future.</span>
 <br />
<span style="color: #38761d;"><span style="color: #38761d;"><span style="color: #38761d;"><b><span style="color: #6aa84f;"><span style="font-size: 14px;">Q Discuss about Web Mining? </span></span></b></span></span></span></div>
<div>
<span style="color: #6aa84f;"><span style="font-size: 14px;">            </span></span><br />
<span class="_Tgc"><b>           Web mining</b> is the integration of information gathered by traditional data <b>mining</b> methodologies and techniques with information gathered over the World Wide <b>Web.</b></span></div>
<div>
<span class="_Tgc"><b>Web mining is used to understand customer behavior, evaluate the effectiveness.</b></span></div>
<div>
<span style="color: #38761d;"><span style="color: #38761d;"><b><span class="_Tgc"><br /></span></b></span></span></div>
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<span style="color: #38761d;"><span style="color: #38761d;"><span style="color: #38761d;"><span style="background-color: white;"><b><span style="color: #6aa84f;"><span style="font-size: 14px;">Q Discuss about Data Mining Functionalities? </span></span></b></span></span></span></span></div>
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<div>
<span style="font-size: x-small;">.</span><span style="color: #6aa84f;"><span style="font-size: 14px;"><span style="font-size: small;">       </span></span></span><span style="font-size: small;">Data mining functionalities are used to specify the</span><span style="color: #6aa84f;"><span style="font-size: 14px;"></span></span><span style="font-size: small;"> kind of patterns to be found in data mining tasks. </span></div>
<div>
<span style="font-size: small;">   <b>Data mining tasks:</b></span></div>
<div>
<span style="font-size: small;"><b>     </b></span><span style="font-size: small;"><u>Descriptive data mining</u> : characterize the general </span><span style="font-size: small;">properties of the data in the database.</span><br />
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<span style="font-size: small;"><u>   Predictive data mining</u>: perform inference on the current data in order to make predictions.</span></div>
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