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Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
Research methodology - Analysis of DataThe Stockker
Processing & Analysis of Data, Data editing, Benefits of data editing, Data coding, Classification of data, CLASSIFICATION ACCORDING THE ATTRIBUTES, CLASSIFICATION ON THE BASIS OF INTERVAL, TABULATION of data, Types of tables, Graphing of data, Bar chart, Pie chart, Line graph, histogram, Polygon / ogive, Analysis of Data, Descriptive Analysis, Uni-Variate Analysis, Bivariate Analysis, Multi-Variate Analysis, Causal Analysis, Inferential Analysis, PARAMETRIC TESTS, Non parametric Test,
In any single written message, one can count letters, words or sentences. One can categories phrases, describe the logical structure of expressions, ascertain associations, connotations, denotations, elocutionary forces, and one can also offer psychiatric, sociological, or political interpretations. All of these may be simultaneously valid. In short a message may convey a multitude of contents even to a single receiver.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...Stats Statswork
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
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Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
Research methodology - Analysis of DataThe Stockker
Processing & Analysis of Data, Data editing, Benefits of data editing, Data coding, Classification of data, CLASSIFICATION ACCORDING THE ATTRIBUTES, CLASSIFICATION ON THE BASIS OF INTERVAL, TABULATION of data, Types of tables, Graphing of data, Bar chart, Pie chart, Line graph, histogram, Polygon / ogive, Analysis of Data, Descriptive Analysis, Uni-Variate Analysis, Bivariate Analysis, Multi-Variate Analysis, Causal Analysis, Inferential Analysis, PARAMETRIC TESTS, Non parametric Test,
In any single written message, one can count letters, words or sentences. One can categories phrases, describe the logical structure of expressions, ascertain associations, connotations, denotations, elocutionary forces, and one can also offer psychiatric, sociological, or political interpretations. All of these may be simultaneously valid. In short a message may convey a multitude of contents even to a single receiver.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
The growth in the use of technology has led organizations to generate data for which they need Data Analytics to analyze the data to make business decisions.
The presentation includes the following topics:
- Introduction to Data Analytics
- Data Analytics Process
- Data Analytics Skills
- Certifications Information for Data Analytics
CRM 101: What is CRM?
This is a simple definition of CRM.
Customer relationship management (CRM) is a technology for managing all your company’s relationships and interactions with customers and potential customers. The goal is simple: Improve business relationships to grow your business. A CRM system helps companies stay connected to customers, streamline processes, and improve profitability.
When people talk about CRM, they are usually referring to a CRM system, a tool that helps with contact management, sales management, agent productivity, and more. CRM tools can now be used to manage customer relationships across the entire customer lifecycle, spanning marketing, sales, digital commerce, and customer service interactions.
A CRM solution helps you focus on your organization’s relationships with individual people — including customers, service users, colleagues, or suppliers — throughout your lifecycle with them, including finding new customers, winning their business, and providing support and additional services throughout the relationship.
Who is CRM for?
A CRM system gives everyone — from sales, customer service, business development, recruiting, marketing, or any other line of business — a better way to manage the external interactions and relationships that drive success. A CRM tool lets you store customer and prospect contact information, identify sales opportunities, record service issues, and manage marketing campaigns, all in one central location — and make information about every customer interaction available to anyone at your company who might need it.
With visibility and easy access to data, it's easier to collaborate and increase productivity. Everyone in your company can see how customers have been communicated with, what they’ve bought, when they last purchased, what they paid, and so much more. CRM can help companies of all sizes drive business growth, and it can be especially beneficial to a small business, where teams often need to find ways to do more with less.
Here’s why CRM matters to your business.
CRM is the largest and fastest-growing enterprise application software category, and worldwide spending on CRM is expected to reach USD $114.4 billion by the year 2027. If your business is going to last, you need a strategy for the future that’s centered around your customers, and enabled by the right technology. You have targets for sales, business objectives, and profitability. But getting up-to-date, reliable information on your progress can be tricky. How do you translate the many streams of data coming in from sales, customer service, marketing, and social media monitoring into useful business information?
A CRM system can give you a clear overview of your customers. You can see everything in one place — a simple, customizable dashboard that can tell you a customer’s previous history with you, the status of their orders, any outstanding customer service issues, and more. You can even choose to include information
Kompetensi Data analitik merupakan kompetensi yang sangat dibutuhkan pada era industri 4.0. Kami siap memberikan pelatihan kepada karyawan perusahaan yang membutuhkan. Silahkan kontak kami di jhotank@yahoo.com atau di website http://corporaeuniversity-digital.com.
Research design decisions and be competent in the process of reliable data co...Stats Statswork
Research Design may be described as the researchers scheme of outlining the flow of his project. It is based on research design, that the researcher goes about gathering data to answer his research question. It enables the researcher to prioritize his work, create better questionnaires and arrive at conclusions with greater clarity. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following – Always on Time, outstanding customer support, and High-quality Subject Matter Experts.
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As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
2. OVERVIEW
Qualitative and quantitative
Simple quantitative analysis
Simple qualitative analysis
Tools to support data analysis
Theoretical frameworks: grounded theory,
distributed cognition, activity theory
Presenting the findings: rigorous notations,
stories, summaries
3. WHY DO WE ANALYZE DATA
The purpose of analysing data is to obtain usable and useful
information. The analysis, irrespective of whether the data is
qualitative or quantitative, may:
• describe and summarise the data
• identify relationships between variables
• compare variables
• identify the difference between variables
• forecast outcomes
4.
5. SCALES OF MEASUREMENT
Many people are confused about what type of
analysis to use on a set of data and the
relevant forms of pictorial presentation or
data display. The decision is based on the
scale of measurement of the data. These
scales are nominal, ordinal and numerical.
Nominal scale
A nominal scale is where:
the data can be classified into a non-
numerical or named categories, and
the order in which these categories can be
written or asked is arbitrary.
Ordinal scale
An ordinal scale is where:
the data can be classified into non-numerical or named
categories
an inherent order exists among the response categories.
Ordinal scales are seen in questions that call for
ratings of quality (for example, very good, good, fair,
poor, very poor) and agreement (for example, strongly
agree, agree, disagree, strongly disagree).
Numerical scale
A numerical scale is:
where numbers represent the possible response
categories
there is a natural ranking of the categories
zero on the scale has meaning
there is a quantifiable difference within categories and
between consecutive categories.
6. When using a quantitative methodology, you are normally testing theory through the testing
of a hypothesis.
In qualitative research, you are either exploring the application of a theory or model in a different
context or are hoping for a theory or a model to emerge from the data. In other words,
although you may have some ideas about your topic, you are also looking for ideas,
concepts and attitudes often from experts or practitioners in the field.
7.
8.
9.
10.
11.
12.
13.
14.
15. GRAPHICAL REPRESENTATIONS
give overview of data
Number of errors made
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1 3 5 7 9 11 13 15 17
User
Number
of
errors
made
Internet use
< once a day
once a day
once a week
2 or 3 times a week
once a month
Number of errors made
0
2
4
6
8
10
0 5 10 15 20
User
Number
of
errors
made
17. QUALITATIVE ANALYSIS
"Data analysis is the process
of bringing order, structure
and meaning to the mass of
collected data. It is a
messy, ambiguous, time-
consuming, creative, and
fascinating process. It does
not proceed in a linear
fashion; it is not neat.
Qualitative data analysis is
a search for general
statements about
relationships among
categories of data."
Marshall and Rossman, 1990:111
Hitchcock and Hughes take
this one step further:
"…the ways in which the
researcher moves from a
description of what is the
case to an explanation of
why what is the case is the
case."
Hitchcock and Hughes 1995:295
18. Simple qualitative analysis
• Unstructured - are not directed by a script. Rich but not
replicable.
• Structured - are tightly scripted, often like a questionnaire.
Replicable but may lack richness.
• Semi-structured - guided by a script but interesting issues can
be explored in more depth. Can provide a good balance
between richness and replicability.
19. Simple qualitative analysis
• Recurring patterns or themes
– Emergent from data, dependent on observation
framework if used
• Categorizing data
– Categorization scheme may be emergent or pre-specified
• Looking for critical incidents
– Helps to focus in on key events
20. TOOLS TO SUPPORT DATA
ANALYSIS
• Spreadsheet – simple to use, basic graphs
• Statistical packages, e.g. SPSS
• Qualitative data analysis tools
– Categorization and theme-based analysis, e.g. N6
– Quantitative analysis of text-based data
• CAQDAS Networking Project, based at the University of Surrey
(http://caqdas.soc.surrey.ac.uk/)
21. Theoretical frameworks for
qualitative analysis
• Basing data analysis around theoretical frameworks provides
further insight
• Three such frameworks are:
– Grounded Theory
– Distributed Cognition
– Activity Theory
22. Grounded Theory
• Aims to derive theory from systematic analysis of data
• Based on categorization approach (called here ‘coding’)
• Three levels of ‘coding’
– Open: identify categories
– Axial: flesh out and link to subcategories
– Selective: form theoretical scheme
• Researchers are encouraged to draw on own theoretical
backgrounds to inform analysis
23. Distributed Cognition
• The people, environment & artefacts are regarded as one
cognitive system
• Used for analyzing collaborative work
• Focuses on information propagation & transformation
24. Activity Theory
• Explains human behavior in terms of our practical activity with
the world
• Provides a framework that focuses analysis around the concept of
an ‘activity’ and helps to identify tensions between the different
elements of the system
• Two key models: one outlines what constitutes an ‘activity’; one
models the mediating role of artifacts
27. Presenting the findings
• Only make claims that your data can support
• The best way to present your findings depends on the audience,
the purpose, and the data gathering and analysis undertaken
• Graphical representations (as discussed above) may be
appropriate for presentation
• Other techniques are:
– Rigorous notations, e.g. UML
– Using stories, e.g. to create scenarios
– Summarizing the findings
28. SUMMARY
• The data analysis that can be done depends on the
data gathering that was done
• Qualitative and quantitative data may be gathered
from any of the three main data gathering approaches
• Percentages and averages are commonly used in
Interaction Design
• Mean, median and mode are different kinds of
‘average’ and can have very different answers for the
same set of data
• Grounded Theory, Distributed Cognition and Activity
Theory are theoretical frameworks to support data
analysis
• Presentation of the findings should not overstate the
evidence