Question Bank On Research Methodology
| Institution | University |
| Course | Research Methodology |
| Year | 1st Year |
| Semester | Unknown |
| Posted By | Rose Oloo |
| File Type | |
| Pages | 16 Pages |
| File Size | 54.66 KB |
| Views | 769 |
| Downloads | 0 |
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Description
This document contains a question bank on research methodology covering 6 units: 1. Introduction to research - defining research, differences between methods and methodology, objectives of research, research process, criteria for good research. 2. Research design - meaning and significance, differences from problem approach, exploratory vs descriptive design, research hypotheses, experimental designs. 3. Sampling methods - sample design, probability vs non-probability sampling, random sampling, stratified sampling, sampling bias, sample size. 4. Data collection methods - primary vs secondary data, surveys, questionnaires, interviews, observation, case studies, attitudes measurement techniques. 5. Attitude measurement and scaling - types of scales, sources of error,
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Ethical Hacking QUESTION & ANSWERS
Download questions and answers to the unit Ethical Hacking and be a head of others with elaborative answers .
Q1. a) Define the following terms as used in Ethical Hacking (5 Marks)
i) Hacking
- Hacking refers to the unauthorized access or manipulation of computer systems, networks,
or data.
ii) Ethical Hacking
- Ethical Hacking involves the authorized use of hacking techniques to identify and fix
vulnerabilities in systems to prevent unauthorized access or data breaches.
iii) Ping Sweep
- Ping Sweep is a technique used to identify active devices on a network by sending ICMP
echo requests (pings) to a range of IP addresses and waiting for responses.
iv) Inverse TCP Flag Scanning
- Inverse TCP Flag Scanning is a technique used to identify systems that are not vulnerable to
certain types of attacks by exploiting the TCP protocol's behavior.
8 Pages
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70.86 KB
CMT 429: Introduction to Data Science
A Data Scientist must find patterns within data. This data must be organized in a standard format in order to find patterns.
A Data Scientist analyzes the data, finds patterns, makes future predictions and presents the result with useful insights in a way the company can understand
This is done through a Data Science Life Cycle.
71 Pages
1631 Views
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CMT 314 :MOBILE APPLICATIONS DEVELOPMENT
Mobile Computing is a technology that allows transmission of data, voice and video via a computer or any other wireless enabled device without having to be connected to a fixed physical link.
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PROPOSAL WRITING GUIDELINES
A secondary purpose of writing a proposal is to train you in the art of proposal writing as these skills will be useful not only in the world of academia but in all fields.
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The Rehabilitation of offenders
Rehabilitation is “the idea that punishment can reduce the incidence of crime by taking a form which will improve the individual offender’s character or behaviour and make him or her less likely to reoffend in future”
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Web Application Security
1.1 Definition of terms
There are several terms related to computer Security and Cryptography. In this section, we
briefly explain five major terms.
1.11 Computer security
Computer Security is the degree to which information systems are protected against
destruction, disruption, deletion, unauthorized access and unauthorized changes. The term
Cyber Security, Information System Security and System Security can be used interchangeably
for purposes of this unit.
88 Pages
1727 Views
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12.14 MB
SOCIAL INFORMATICS QUESTIONS AND ANSWERS
Download questions and answers to social informatics and get yourself ahead of others.
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Python for Data Science
The volume of data that one has to deal has exploded to unimaginable levels in the past decade, and at the same time, the price of data storage has systematically reduced. Private companies and research institutions capture terabytes of data about their users’ interactions, business, social media, and also sensors from devices such as mobile phones and automobiles. The challenge of this era is to make sense of this sea of data. This is where big data analytics comes into picture.
Big Data Analytics largely involves collecting data from different sources, merge it in a way that it becomes available to be consumed by analysts and finally deliver data products useful to the organization business. The process of converting large amounts of unstructured raw data, retrieved from different sources to a data product useful for organizations forms the core of Big Data Analytics.
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Data Science Project (Car Price Predicition)
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Data science Project (Credit Card Fraud Detection)
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