1. Research Topic Super Computer Data MiningThe aim of this.docxketurahhazelhurst
1. Research Topic: Super Computer Data Mining
The aim of this project is to produce a super-computing data mining resource for use by the UK academic community which utilizes a number of advanced machine learning and statistical algorithms for large datasets. In particular, a number of evolutionary computing-based algorithms and the ensemble machine approach will be used to exploit the large-scale parallelism possible in super-computing. This purpose is embodied in the following objectives:
1. to develop a massively parallel approach for commonly used statistical and machine learning techniques for exploratory data analysis
1. to develop a massively parallel approach to the use of evolutionary computing techniques for feature creation and selection
1. to develop a massively parallel approach to the use of evolutionary computing techniques for data modelling
1. to develop a massively parallel approach to the use of ensemble machines for data modelling consisting of many well-known machine learning algorithms;
1. to develop an appropriate super-computing infra-structure to support the use of such advanced machine learning techniques with large datasets.
Research Needs:
Problem definition – In the first phase problem definition is listed i.e. business aims and objectives are determined taking into consideration certain factors like the current background and future prospective.
Data exploration – Required data is collected and explored using various statistical methods along with identification of underlying problems.
Data preparation – The data is prepared for modeling by cleansing and formatting the raw data in the desired way. The meaning of data is not changed while preparing.
Modeling – In this phase the data model is created by applying certain mathematical functions and modeling techniques. After the model is created it goes through validation and verification.
Evaluation – After the model is created, it is evaluated by a team of experts to check whether it satisfies business objectives or not.
Deployment – After evaluation, the model is deployed and further plans are made for its maintenance. A properly organized report is prepared with the summary of the work done.
Research paper Policy
· APA format
. https://apastyle.apa.org/
. https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/general_format.html
· Min number of pages are 15 pages
· Must have
. Contents with page numbers
. Abstract
. Introduction
. The problem
4. Are there any sub-problems?
4. Is there any issue need to be present concerning the problem?
. The solutions
5. Steps of the solutions
. Compare the solution to other solution
. Any suggestion to improve the solution
. Conclusion
. References
· Missing one of the above will result -5/30 of the research paper
· Paper does not stick to the APA will result in 0 in the research paper
· Submission
. you have multiple submission to check you safe assignments
. The percentage accepted is 1%.
1. Research Topic Super Computer Data MiningThe aim of this.docxbraycarissa250
1. Research Topic: Super Computer Data Mining
The aim of this project is to produce a super-computing data mining resource for use by the UK academic community which utilizes a number of advanced machine learning and statistical algorithms for large datasets. In particular, a number of evolutionary computing-based algorithms and the ensemble machine approach will be used to exploit the large-scale parallelism possible in super-computing. This purpose is embodied in the following objectives:
1. to develop a massively parallel approach for commonly used statistical and machine learning techniques for exploratory data analysis
1. to develop a massively parallel approach to the use of evolutionary computing techniques for feature creation and selection
1. to develop a massively parallel approach to the use of evolutionary computing techniques for data modelling
1. to develop a massively parallel approach to the use of ensemble machines for data modelling consisting of many well-known machine learning algorithms;
1. to develop an appropriate super-computing infra-structure to support the use of such advanced machine learning techniques with large datasets.
Research Needs:
Problem definition – In the first phase problem definition is listed i.e. business aims and objectives are determined taking into consideration certain factors like the current background and future prospective.
Data exploration – Required data is collected and explored using various statistical methods along with identification of underlying problems.
Data preparation – The data is prepared for modeling by cleansing and formatting the raw data in the desired way. The meaning of data is not changed while preparing.
Modeling – In this phase the data model is created by applying certain mathematical functions and modeling techniques. After the model is created it goes through validation and verification.
Evaluation – After the model is created, it is evaluated by a team of experts to check whether it satisfies business objectives or not.
Deployment – After evaluation, the model is deployed and further plans are made for its maintenance. A properly organized report is prepared with the summary of the work done.
Research paper Policy
· APA format
. https://apastyle.apa.org/
. https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/general_format.html
· Min number of pages are 15 pages
· Must have
. Contents with page numbers
. Abstract
. Introduction
. The problem
4. Are there any sub-problems?
4. Is there any issue need to be present concerning the problem?
. The solutions
5. Steps of the solutions
. Compare the solution to other solution
. Any suggestion to improve the solution
. Conclusion
. References
· Missing one of the above will result -5/30 of the research paper
· Paper does not stick to the APA will result in 0 in the research paper
· Submission
. you have multiple submission to check you safe assignments
. The percentage accepted is 1%.
This document discusses concepts related to corporate and strategic planning for management information systems. It covers topics such as long and short range planning, dimensions of planning including time, entity and organization, characteristics of corporate plans, essentiality of strategic planning due to market forces and environmental factors, development of long range strategic planning including mission and goals, and types of strategies such as overall company strategy, growth strategy, product strategy, and market strategy. Short range planning deals with targets and objectives for one year. Tools for planning discussed include creativity, systems approach, sensitivity analysis, and modeling. Balance scorecards, dashboards, and scorecards are also covered. Finally, the role of MIS in strategic management is discussed.
Enterprise Architecture - An Introduction Daljit Banger
The Slides are from my session at "An Evening of Enterprise Architecture Awareness" held at theUniversity of Sussex Hosted by the BCS Local Chapter and facilitated by the BCS EA Specialist Group.
Traveling the Big Data Super Highway: Realizing Enterprise-wide Adoption and ...Cognizant
When it comes to big data, companies need to determine best fit with existing investments and incorporate proven best practices that enable them to run better and run differently.
STOCK MARKET ANALYZING AND PREDICTION USING MACHINE LEARNING TECHNIQUESIRJET Journal
This document discusses predicting stock market movements using machine learning techniques. It begins by reviewing previous research on fundamental analysis, technical analysis and applying machine learning to stock prediction. It then proposes a methodology using machine learning algorithms like support vector machine, decision trees and classification to analyze stock market data, extract features, segment data and build a mathematical model to forecast stock prices. The goal is to help investors make better decisions by predicting stock behavior.
Visual Analytics combines human intuition and data science to derive knowledge from the data in a very efficient, effective and easy way. Visual Analytics empowers your people to interact with the data and generate new insights.
1. Research Topic Super Computer Data MiningThe aim of this.docxketurahhazelhurst
1. Research Topic: Super Computer Data Mining
The aim of this project is to produce a super-computing data mining resource for use by the UK academic community which utilizes a number of advanced machine learning and statistical algorithms for large datasets. In particular, a number of evolutionary computing-based algorithms and the ensemble machine approach will be used to exploit the large-scale parallelism possible in super-computing. This purpose is embodied in the following objectives:
1. to develop a massively parallel approach for commonly used statistical and machine learning techniques for exploratory data analysis
1. to develop a massively parallel approach to the use of evolutionary computing techniques for feature creation and selection
1. to develop a massively parallel approach to the use of evolutionary computing techniques for data modelling
1. to develop a massively parallel approach to the use of ensemble machines for data modelling consisting of many well-known machine learning algorithms;
1. to develop an appropriate super-computing infra-structure to support the use of such advanced machine learning techniques with large datasets.
Research Needs:
Problem definition – In the first phase problem definition is listed i.e. business aims and objectives are determined taking into consideration certain factors like the current background and future prospective.
Data exploration – Required data is collected and explored using various statistical methods along with identification of underlying problems.
Data preparation – The data is prepared for modeling by cleansing and formatting the raw data in the desired way. The meaning of data is not changed while preparing.
Modeling – In this phase the data model is created by applying certain mathematical functions and modeling techniques. After the model is created it goes through validation and verification.
Evaluation – After the model is created, it is evaluated by a team of experts to check whether it satisfies business objectives or not.
Deployment – After evaluation, the model is deployed and further plans are made for its maintenance. A properly organized report is prepared with the summary of the work done.
Research paper Policy
· APA format
. https://apastyle.apa.org/
. https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/general_format.html
· Min number of pages are 15 pages
· Must have
. Contents with page numbers
. Abstract
. Introduction
. The problem
4. Are there any sub-problems?
4. Is there any issue need to be present concerning the problem?
. The solutions
5. Steps of the solutions
. Compare the solution to other solution
. Any suggestion to improve the solution
. Conclusion
. References
· Missing one of the above will result -5/30 of the research paper
· Paper does not stick to the APA will result in 0 in the research paper
· Submission
. you have multiple submission to check you safe assignments
. The percentage accepted is 1%.
1. Research Topic Super Computer Data MiningThe aim of this.docxbraycarissa250
1. Research Topic: Super Computer Data Mining
The aim of this project is to produce a super-computing data mining resource for use by the UK academic community which utilizes a number of advanced machine learning and statistical algorithms for large datasets. In particular, a number of evolutionary computing-based algorithms and the ensemble machine approach will be used to exploit the large-scale parallelism possible in super-computing. This purpose is embodied in the following objectives:
1. to develop a massively parallel approach for commonly used statistical and machine learning techniques for exploratory data analysis
1. to develop a massively parallel approach to the use of evolutionary computing techniques for feature creation and selection
1. to develop a massively parallel approach to the use of evolutionary computing techniques for data modelling
1. to develop a massively parallel approach to the use of ensemble machines for data modelling consisting of many well-known machine learning algorithms;
1. to develop an appropriate super-computing infra-structure to support the use of such advanced machine learning techniques with large datasets.
Research Needs:
Problem definition – In the first phase problem definition is listed i.e. business aims and objectives are determined taking into consideration certain factors like the current background and future prospective.
Data exploration – Required data is collected and explored using various statistical methods along with identification of underlying problems.
Data preparation – The data is prepared for modeling by cleansing and formatting the raw data in the desired way. The meaning of data is not changed while preparing.
Modeling – In this phase the data model is created by applying certain mathematical functions and modeling techniques. After the model is created it goes through validation and verification.
Evaluation – After the model is created, it is evaluated by a team of experts to check whether it satisfies business objectives or not.
Deployment – After evaluation, the model is deployed and further plans are made for its maintenance. A properly organized report is prepared with the summary of the work done.
Research paper Policy
· APA format
. https://apastyle.apa.org/
. https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/general_format.html
· Min number of pages are 15 pages
· Must have
. Contents with page numbers
. Abstract
. Introduction
. The problem
4. Are there any sub-problems?
4. Is there any issue need to be present concerning the problem?
. The solutions
5. Steps of the solutions
. Compare the solution to other solution
. Any suggestion to improve the solution
. Conclusion
. References
· Missing one of the above will result -5/30 of the research paper
· Paper does not stick to the APA will result in 0 in the research paper
· Submission
. you have multiple submission to check you safe assignments
. The percentage accepted is 1%.
This document discusses concepts related to corporate and strategic planning for management information systems. It covers topics such as long and short range planning, dimensions of planning including time, entity and organization, characteristics of corporate plans, essentiality of strategic planning due to market forces and environmental factors, development of long range strategic planning including mission and goals, and types of strategies such as overall company strategy, growth strategy, product strategy, and market strategy. Short range planning deals with targets and objectives for one year. Tools for planning discussed include creativity, systems approach, sensitivity analysis, and modeling. Balance scorecards, dashboards, and scorecards are also covered. Finally, the role of MIS in strategic management is discussed.
Enterprise Architecture - An Introduction Daljit Banger
The Slides are from my session at "An Evening of Enterprise Architecture Awareness" held at theUniversity of Sussex Hosted by the BCS Local Chapter and facilitated by the BCS EA Specialist Group.
Traveling the Big Data Super Highway: Realizing Enterprise-wide Adoption and ...Cognizant
When it comes to big data, companies need to determine best fit with existing investments and incorporate proven best practices that enable them to run better and run differently.
STOCK MARKET ANALYZING AND PREDICTION USING MACHINE LEARNING TECHNIQUESIRJET Journal
This document discusses predicting stock market movements using machine learning techniques. It begins by reviewing previous research on fundamental analysis, technical analysis and applying machine learning to stock prediction. It then proposes a methodology using machine learning algorithms like support vector machine, decision trees and classification to analyze stock market data, extract features, segment data and build a mathematical model to forecast stock prices. The goal is to help investors make better decisions by predicting stock behavior.
Visual Analytics combines human intuition and data science to derive knowledge from the data in a very efficient, effective and easy way. Visual Analytics empowers your people to interact with the data and generate new insights.
This document discusses the process of constructing, delivering, and maintaining accounting information systems projects. It covers the in-house development phase of the systems development life cycle, including analyzing user needs, designing processes and databases, programming applications, and testing and implementation. It also discusses using commercial software packages, and the maintenance and support phase, which involves acquiring software updates and modifying existing systems. Key aspects of project development like prototyping, project management tools, structured and object-oriented design approaches, and data modeling are explained at a high level.
The document summarizes Chapter 13 of the textbook "Accounting Information Systems, 8e" by James A. Hall. It discusses the systems development life cycle (SDLC) process, which includes 5 stages: systems strategy, project initiation, systems analysis, systems design, and systems implementation. The chapter focuses on the first two stages - systems strategy and project initiation. Systems strategy involves understanding business needs, legacy systems, and user feedback to create a strategic plan. Project initiation assesses proposals for consistency with strategy and evaluates feasibility.
This document contains slides from a course on new product development and innovation. It discusses topics such as why companies introduce new products, approaches to developing new products, testing new product concepts, launching new products, and strategies for product growth. Copyright notices are included at the bottom of each slide.
Report on strategic rules of Information System for changing the bases of com...Md. Khukan Miah
Achieving advantages requires broad IS management and user dialogue plus imagination. The process is complicated by the fact that many IS products are strategic though the potential benefits are very subjective and not easily verified. Often a strict ROI focus by senior management may turn attention toward narrow, well-defined targets as opposed to broader strategic opportunities that are harder to analyze.
The document discusses project selection and governance for the Hilti Group as it implements a new cloud-based CRM software called Salesforce. It recommends that Hilti use the Analytical Hierarchy Process to evaluate project alternatives according to criteria like costs, strategic goals, and value creation. It also suggests a network organizational structure to allow partnerships and outsourcing for skills and flexibility. For project governance, it compares the P3M3 and stage-gate methods, noting that P3M3 provides a detailed process for efficiency while stage-gates allow consistent project reviews at important milestones.
The Data-To-Business bridge model for business development organizationsMathieu Rioult
This is a methodology to extract, justify, apply and track actionable insights from a structured dataset and a cross-departments exchange. Please find its summarized mechanism below.
A simple “coordination & action” concept based on four pillars, their related key actions and two strong principles that feed your organization everyday: Growing Cells & “comestible” Fuel. Human Beings interacting and learning collaboratively & Structured data understandable and exploitable by them.
Thanks to both and the D2B bridge, your organization will be able to identify and implement actionable insights that will positively impact your overall business growth and organizational processes. Moreover, your organization will build by itself a learning machine that each individual will beneficiate from. Actions run by someone’s God Feeling appear now as obsolete. For sure, Alone, we go faster. But together, we go farer.
The document discusses the importance of developing a big data plan. It states that while exploiting big data is an important source of competitive advantage, many companies struggle due to technical and organizational challenges. It recommends that companies craft a big data plan that focuses on three elements: assembling and integrating data from various sources, selecting analytic models that can optimize operations and predict business outcomes, and creating intuitive tools that help employees make use of the analytic outputs. Developing such a plan will help companies prioritize investments and initiatives to harness big data effectively.
-Developed Enterprise software architecture model for startup company as consultants
-Identified and developed all part of enterprise software middleware such as business motivation model, business capability model, Information architecture model, application architecture and much more.
- Developed the project planning, roadmap and governance for the enterprise.
This document summarizes key concepts from the first module of an MIS textbook. It defines transaction processing systems and management information systems, and describes the four major components of an information system as data, database, process, and information. It also explains Porter's Five Forces model for analyzing competitive environments in business.
The document provides 8 guidelines for choosing the right data science platform for business analytics needs. It discusses factors such as whether the platform can handle all aspects of business analytics, large volumes of data, both structured and unstructured data, and real-time scoring problems. It also addresses whether the platform supports easy-to-use workflows, optimization functions, model management, and communicating insights. The document uses Angoss as an example and describes how its platform meets the guidelines.
1. The document discusses rational decision making and business intelligence. It defines rational decision making as selecting the optimal alternative based on analyzing past data and considering various performance criteria.
2. It describes the typical cycle of a business intelligence analysis as involving defining objectives, generating insights from data analysis, making decisions based on insights, and evaluating performance.
3. Key components of business intelligence architectures are data sources, data warehouses/marts for storing and processing data, and business intelligence tools for generating insights and supporting decision making.
Tips --Break Down the Barriers to Better Data AnalyticsAbhishek Sood
1) Analytics executives face challenges in collecting, analyzing, and delivering insights from data due to a lack of skills, cultural barriers, IT backlogs, and productivity drains.
2) Legacy systems and complex analytics platforms also impede effective data use. Modular solutions that integrate with existing systems and empower self-service are recommended.
3) The document promotes the Statistica software as addressing these challenges through its ease of use, integration capabilities, and support for big data analytics.
The document discusses several strategic challenges and changes facing supply chains, including game-changing technologies like mobility, digitization and automation; supply chain analytics and how it can move from data to information to understanding; supply chain talent management and developing effective talent strategies; and sustainability considerations for supply chains. The key topics covered are supply chain principles, emerging technologies, analytics, talent management, and sustainability.
The document provides an overview of building an enterprise data management strategy using the MIKE2.0 methodology. It defines enterprise data management and discusses business drivers. It also outlines challenges in defining a strategy, benefits, and different techniques. The methodology involves 5 phases including business assessment, technology assessment, design, deployment and operations. Key activities and outputs are shown for defining the strategy and assessing current state.
Big data is delivering significant value to organizations that complete projects according to a survey. The vast majority (92%) of users are satisfied with business outcomes and feel their implementation meets needs. Larger companies see big data as more important and are more likely to benefit from initial implementations. While talent shortage poses challenges, successful users leverage external resources. Users see big data as disruptive and potentially transformational, with 89% believing it will revolutionize business as the internet did.
Chapter 3 • Nature of Data, Statistical Modeling, and Visuali.docxpoulterbarbara
Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 185
of thousands of BI dashboards, scorecards, and BI interfaces used by businesses of all
sizes and industries, nonprofits, and government agencies.
According to Eckerson (2006), a well-known expert on BI in general and dash-
boards in particular, the most distinctive feature of a dashboard is its three layers of
information:
1. Monitoring: Graphical, abstracted data to monitor key performance metrics.
2. Analysis: Summarized dimensional data to analyze the root cause of problems.
3. Management: Detailed operational data that identify what actions to take to re-
solve a problem.
Because of these layers, dashboards pack a large amount of information into a sin-
gle screen. According to Few (2005), “The fundamental challenge of dashboard design is
to display all the required information on a single screen, clearly and without distraction,
in a manner that can be assimilated quickly.” To speed assimilation of the numbers, they
need to be placed in context. This can be done by comparing the numbers of interest to
other baseline or target numbers, by indicating whether the numbers are good or bad,
by denoting whether a trend is better or worse, and by using specialized display widgets
or components to set the comparative and evaluative context. Some of the common
comparisons that are typically made in BI systems include comparisons against past val-
ues, forecasted values, targeted values, benchmark or average values, multiple instances
of the same measure, and the values of other measures (e.g., revenues versus costs).
Even with comparative measures, it is important to specifically point out whether a
particular number is good or bad and whether it is trending in the right direction. Without
these types of evaluative designations, it can be time consuming to determine the status
of a particular number or result. Typically, either specialized visual objects (e.g., traffic
lights, dials, and gauges) or visual attributes (e.g., color coding) are used to set the evalu-
ative context. An interactive dashboard-driven reporting data exploration solution built by
an energy company is featured in Application Case 3.8.
Energy markets all around the world are going
through a significant change and transformation,
creating ample opportunities along with significant
challenges. As is the case in any industry, oppor-
tunities are attracting more players in the market-
place, increasing the competition, and reducing the
tolerances for less-than-optimal business decision
making. Success requires creating and disseminat-
ing accurate and timely information to whomever
whenever it is needed. For instance, if you need to
easily track marketing budgets, balance employee
workloads, and target customers with tailored mar-
keting messages, you would need three different
reporting solutions. Electrabel GDF SUEZ is doing
all of that for its marketing and sales business .
The purpose of strategic plan is to give a business a roadmap to its future. It answers three questions:
1. What is the mission of the business?
2. What goals should be met to accomplish
this mission?
3. What strategies should be employed to achieve these goals?
Our global logistics client was challenged by its inability to support their communications initiatives leveraging established campaign systems. Their existing marketing infrastructure was not well designed to support targeted campaigns, and due to the complex nature of their data and systems our client had limited visibility into their campaign performance tracking and advertising spend.
Although Big Data is changing enterprise data architecture models, support for Big Data extends beyond the walls of IT. The most successful companies are focused on building strong business cases for Big Data to drive support, adoption and funding though the enterprise.
This webinar investigated the two perspectives in constructing a business case for Big Data as well as how to create a compelling business case for Big Data success.
During this webinar, we covered:
-Challenges Creating Business Cases for Big Data
-Two perspectives for building Big Data business-cases
-Building the business-focused case and getting to monetized benefits
-Fortifying your business case with IT-benefits
Topic that identifies characteristics of Native American Culture and.docxVannaSchrader3
Topic that identifies characteristics of Native American Culture and how it influences/contributes to contemporary cultures and/or what factors have changed perspective regarding Native American cultural practices.
resources cited, at least 3 of any format.
Cover Page.
Minimun 4 page (excluding reference and cover).
MLA formet with proper work cited on the last page
12/ Times/ Double Spacing.
.
Topic Stem Cell ResearchAPA Format I need these topics. don.docxVannaSchrader3
Topic: Stem Cell Research
APA Format
I need these topics. don't add other contents
Table of contents:
1. Overview of stem cell research -
1 Page
2. Embryonic Stem Cells -
2 Pages
3. Adult Stem Cells -
2 Pages
4. Legal issues - 1 Page
5. Conclusion- It should be a strong conclusion
References:
Use 3 Journal Articles or newspaper articles and 2 Internet site. for example .edu, .org, .gov.
another 2 references from the academically approved books.
see for more info and references in the document
.
This document discusses the process of constructing, delivering, and maintaining accounting information systems projects. It covers the in-house development phase of the systems development life cycle, including analyzing user needs, designing processes and databases, programming applications, and testing and implementation. It also discusses using commercial software packages, and the maintenance and support phase, which involves acquiring software updates and modifying existing systems. Key aspects of project development like prototyping, project management tools, structured and object-oriented design approaches, and data modeling are explained at a high level.
The document summarizes Chapter 13 of the textbook "Accounting Information Systems, 8e" by James A. Hall. It discusses the systems development life cycle (SDLC) process, which includes 5 stages: systems strategy, project initiation, systems analysis, systems design, and systems implementation. The chapter focuses on the first two stages - systems strategy and project initiation. Systems strategy involves understanding business needs, legacy systems, and user feedback to create a strategic plan. Project initiation assesses proposals for consistency with strategy and evaluates feasibility.
This document contains slides from a course on new product development and innovation. It discusses topics such as why companies introduce new products, approaches to developing new products, testing new product concepts, launching new products, and strategies for product growth. Copyright notices are included at the bottom of each slide.
Report on strategic rules of Information System for changing the bases of com...Md. Khukan Miah
Achieving advantages requires broad IS management and user dialogue plus imagination. The process is complicated by the fact that many IS products are strategic though the potential benefits are very subjective and not easily verified. Often a strict ROI focus by senior management may turn attention toward narrow, well-defined targets as opposed to broader strategic opportunities that are harder to analyze.
The document discusses project selection and governance for the Hilti Group as it implements a new cloud-based CRM software called Salesforce. It recommends that Hilti use the Analytical Hierarchy Process to evaluate project alternatives according to criteria like costs, strategic goals, and value creation. It also suggests a network organizational structure to allow partnerships and outsourcing for skills and flexibility. For project governance, it compares the P3M3 and stage-gate methods, noting that P3M3 provides a detailed process for efficiency while stage-gates allow consistent project reviews at important milestones.
The Data-To-Business bridge model for business development organizationsMathieu Rioult
This is a methodology to extract, justify, apply and track actionable insights from a structured dataset and a cross-departments exchange. Please find its summarized mechanism below.
A simple “coordination & action” concept based on four pillars, their related key actions and two strong principles that feed your organization everyday: Growing Cells & “comestible” Fuel. Human Beings interacting and learning collaboratively & Structured data understandable and exploitable by them.
Thanks to both and the D2B bridge, your organization will be able to identify and implement actionable insights that will positively impact your overall business growth and organizational processes. Moreover, your organization will build by itself a learning machine that each individual will beneficiate from. Actions run by someone’s God Feeling appear now as obsolete. For sure, Alone, we go faster. But together, we go farer.
The document discusses the importance of developing a big data plan. It states that while exploiting big data is an important source of competitive advantage, many companies struggle due to technical and organizational challenges. It recommends that companies craft a big data plan that focuses on three elements: assembling and integrating data from various sources, selecting analytic models that can optimize operations and predict business outcomes, and creating intuitive tools that help employees make use of the analytic outputs. Developing such a plan will help companies prioritize investments and initiatives to harness big data effectively.
-Developed Enterprise software architecture model for startup company as consultants
-Identified and developed all part of enterprise software middleware such as business motivation model, business capability model, Information architecture model, application architecture and much more.
- Developed the project planning, roadmap and governance for the enterprise.
This document summarizes key concepts from the first module of an MIS textbook. It defines transaction processing systems and management information systems, and describes the four major components of an information system as data, database, process, and information. It also explains Porter's Five Forces model for analyzing competitive environments in business.
The document provides 8 guidelines for choosing the right data science platform for business analytics needs. It discusses factors such as whether the platform can handle all aspects of business analytics, large volumes of data, both structured and unstructured data, and real-time scoring problems. It also addresses whether the platform supports easy-to-use workflows, optimization functions, model management, and communicating insights. The document uses Angoss as an example and describes how its platform meets the guidelines.
1. The document discusses rational decision making and business intelligence. It defines rational decision making as selecting the optimal alternative based on analyzing past data and considering various performance criteria.
2. It describes the typical cycle of a business intelligence analysis as involving defining objectives, generating insights from data analysis, making decisions based on insights, and evaluating performance.
3. Key components of business intelligence architectures are data sources, data warehouses/marts for storing and processing data, and business intelligence tools for generating insights and supporting decision making.
Tips --Break Down the Barriers to Better Data AnalyticsAbhishek Sood
1) Analytics executives face challenges in collecting, analyzing, and delivering insights from data due to a lack of skills, cultural barriers, IT backlogs, and productivity drains.
2) Legacy systems and complex analytics platforms also impede effective data use. Modular solutions that integrate with existing systems and empower self-service are recommended.
3) The document promotes the Statistica software as addressing these challenges through its ease of use, integration capabilities, and support for big data analytics.
The document discusses several strategic challenges and changes facing supply chains, including game-changing technologies like mobility, digitization and automation; supply chain analytics and how it can move from data to information to understanding; supply chain talent management and developing effective talent strategies; and sustainability considerations for supply chains. The key topics covered are supply chain principles, emerging technologies, analytics, talent management, and sustainability.
The document provides an overview of building an enterprise data management strategy using the MIKE2.0 methodology. It defines enterprise data management and discusses business drivers. It also outlines challenges in defining a strategy, benefits, and different techniques. The methodology involves 5 phases including business assessment, technology assessment, design, deployment and operations. Key activities and outputs are shown for defining the strategy and assessing current state.
Big data is delivering significant value to organizations that complete projects according to a survey. The vast majority (92%) of users are satisfied with business outcomes and feel their implementation meets needs. Larger companies see big data as more important and are more likely to benefit from initial implementations. While talent shortage poses challenges, successful users leverage external resources. Users see big data as disruptive and potentially transformational, with 89% believing it will revolutionize business as the internet did.
Chapter 3 • Nature of Data, Statistical Modeling, and Visuali.docxpoulterbarbara
Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 185
of thousands of BI dashboards, scorecards, and BI interfaces used by businesses of all
sizes and industries, nonprofits, and government agencies.
According to Eckerson (2006), a well-known expert on BI in general and dash-
boards in particular, the most distinctive feature of a dashboard is its three layers of
information:
1. Monitoring: Graphical, abstracted data to monitor key performance metrics.
2. Analysis: Summarized dimensional data to analyze the root cause of problems.
3. Management: Detailed operational data that identify what actions to take to re-
solve a problem.
Because of these layers, dashboards pack a large amount of information into a sin-
gle screen. According to Few (2005), “The fundamental challenge of dashboard design is
to display all the required information on a single screen, clearly and without distraction,
in a manner that can be assimilated quickly.” To speed assimilation of the numbers, they
need to be placed in context. This can be done by comparing the numbers of interest to
other baseline or target numbers, by indicating whether the numbers are good or bad,
by denoting whether a trend is better or worse, and by using specialized display widgets
or components to set the comparative and evaluative context. Some of the common
comparisons that are typically made in BI systems include comparisons against past val-
ues, forecasted values, targeted values, benchmark or average values, multiple instances
of the same measure, and the values of other measures (e.g., revenues versus costs).
Even with comparative measures, it is important to specifically point out whether a
particular number is good or bad and whether it is trending in the right direction. Without
these types of evaluative designations, it can be time consuming to determine the status
of a particular number or result. Typically, either specialized visual objects (e.g., traffic
lights, dials, and gauges) or visual attributes (e.g., color coding) are used to set the evalu-
ative context. An interactive dashboard-driven reporting data exploration solution built by
an energy company is featured in Application Case 3.8.
Energy markets all around the world are going
through a significant change and transformation,
creating ample opportunities along with significant
challenges. As is the case in any industry, oppor-
tunities are attracting more players in the market-
place, increasing the competition, and reducing the
tolerances for less-than-optimal business decision
making. Success requires creating and disseminat-
ing accurate and timely information to whomever
whenever it is needed. For instance, if you need to
easily track marketing budgets, balance employee
workloads, and target customers with tailored mar-
keting messages, you would need three different
reporting solutions. Electrabel GDF SUEZ is doing
all of that for its marketing and sales business .
The purpose of strategic plan is to give a business a roadmap to its future. It answers three questions:
1. What is the mission of the business?
2. What goals should be met to accomplish
this mission?
3. What strategies should be employed to achieve these goals?
Our global logistics client was challenged by its inability to support their communications initiatives leveraging established campaign systems. Their existing marketing infrastructure was not well designed to support targeted campaigns, and due to the complex nature of their data and systems our client had limited visibility into their campaign performance tracking and advertising spend.
Although Big Data is changing enterprise data architecture models, support for Big Data extends beyond the walls of IT. The most successful companies are focused on building strong business cases for Big Data to drive support, adoption and funding though the enterprise.
This webinar investigated the two perspectives in constructing a business case for Big Data as well as how to create a compelling business case for Big Data success.
During this webinar, we covered:
-Challenges Creating Business Cases for Big Data
-Two perspectives for building Big Data business-cases
-Building the business-focused case and getting to monetized benefits
-Fortifying your business case with IT-benefits
Topic that identifies characteristics of Native American Culture and.docxVannaSchrader3
Topic that identifies characteristics of Native American Culture and how it influences/contributes to contemporary cultures and/or what factors have changed perspective regarding Native American cultural practices.
resources cited, at least 3 of any format.
Cover Page.
Minimun 4 page (excluding reference and cover).
MLA formet with proper work cited on the last page
12/ Times/ Double Spacing.
.
Topic Stem Cell ResearchAPA Format I need these topics. don.docxVannaSchrader3
Topic: Stem Cell Research
APA Format
I need these topics. don't add other contents
Table of contents:
1. Overview of stem cell research -
1 Page
2. Embryonic Stem Cells -
2 Pages
3. Adult Stem Cells -
2 Pages
4. Legal issues - 1 Page
5. Conclusion- It should be a strong conclusion
References:
Use 3 Journal Articles or newspaper articles and 2 Internet site. for example .edu, .org, .gov.
another 2 references from the academically approved books.
see for more info and references in the document
.
Topic Styles of PolicingYou are a patrol officer in a middle- to .docxVannaSchrader3
Topic: Styles of Policing
You are a patrol officer in a middle- to lower-class community, which is a suburb of a much larger metropolitan city. During the past 6 months, you have noticed an increase in what might be the beginning of gang activity in your community. You have begun to see gang-style graffiti painted on walls, buildings, and street signs. You have noticed that more young adults are gathering on street corners and appear to be dressing in clothing often associated with gang involvement. While no gang violence has occurred yet, you suspect it is not far away.
As discussed in your text, there are three distinct styles of policing. They are the watchman style, the legalistic style, and the service style.
In a single posting, describe in detail how you would address this growing problem using
each
of the policing styles listed above. Explain which approach is best, using research to substantiate your postings, citing your sources following APA format
.
Topic the legalization of same sex adoptionThese same sex adopti.docxVannaSchrader3
Topic: the legalization of same sex adoption
These: same sex adoption should be legalized and be accepted by the public
attrachments: draft and suggestions from the professor
Develop this 8 pages draft to be a 15 pages final paper
APA style, double spaced, use 10 peer-review journals as sources
.
TOPIC The Truth About Caffeine3 pages,give some statistics of neg.docxVannaSchrader3
TOPIC/ The Truth About Caffeine
3 pages,give some statistics of negative effects of caffeine
the guides to follow:
topic:
Specific Purpose:to inform ....
Introduction:(discovering +history)
Body:
I like here to give some general info about caffeine and explain the negetive effects.
conclusion:
.
Topic Media Example (article)1) as usual, do an analysis of the.docxVannaSchrader3
Topic: Media Example (article)
1) as usual, do an analysis of the logic of the article on Religion which you choose:What is the : claim, premises, whether the argument in the article is valid or sound.
2) THEN, construct FOUR valid Formal Logic argument, using information from the article. One of each of the following forms must be included:
a) Modus Ponens
b) Modus Tollens
c) Chain Argument
d) Disjunctive Argument
please link me to the essay
Pages:
1, Double spaced
.
Topic Servant LeadershipThread In our reading we explored th.docxVannaSchrader3
Topic: Servant Leadership
Thread:
In our reading we explored the concept of servant leadership. Blanchard and Hodges present Jesus Christ as the ultimate example of the servant leader, and with good cause. But consider other servant leaders found in Scripture, too, and then answer the following questions: What biblical leader would you select as another good example of a servant leader? Why? How did this leader reflect principles from both Northouse’s description and Blanchard & Hodge’s description of a servant leader?
300-500 word discussion board with APA in text citation using at least three professional sources. class text leadership theory and practice by peter g. northhouse and lead like jesus by ken blanchard and phil hodges
.
Topic Organization of Law Enforcement AgenciesDo you agree or d.docxVannaSchrader3
Topic:
Organization of Law Enforcement Agencies
Do you agree or disagree with the paramilitary style of organization of most law enforcement agencies? Defend your position. You must use current APA style. You must cite 1 scholarly-quality internet-based source/reference and 1 biblical source/reference to support your answer. Both sources must offer a specific connection to the discussion topic.
.
Topic Parents Should have a license to have childrenaprox. 500 wo.docxVannaSchrader3
Topic: Parents Should have a license to have children
aprox. 500 words
Focus on these three points
1. Childrens safety, health and happines
2. What makes a responsible parent
3.What determines a competent parent from an incompetent parent
-Include a citation page if using statistical data
.
Topic PATIENT DATA PRIVACYPerformance Improvement plan Proper an.docxVannaSchrader3
Topic: PATIENT DATA PRIVACY
Performance Improvement plan: Proper and Intense training of employees
Success of the Performance Improvement Plan
A. If this initiative is successful, what would be the financial implications for the healthcare organization?
B. How would the existing information management systems contribute to the success of your proposal?
C. What organizational processes will permit continued viability of the performance improvement initiative, if it is successful?
D. Analyze interdepartmental communication that would be necessary for continued engagement in the proposed initiative.
1.5-2 pages. APA format with references please
thank you
.
Topic Kelly’s Personal ConstructsQuestionPrompt Analyze th.docxVannaSchrader3
Topic:
Kelly’s Personal Constructs
Question/Prompt:
Analyze the 4 common elements in most human disturbance according to Kelly (threat, fear, anxiety, and guilt). Compare each of these constructs with what Scripture says regarding these particular elements.
Answer must be 300+ words and contain 2 references.
.
Topic Fingerprints.Study fingerprinting in the textbook and res.docxVannaSchrader3
Topic: Fingerprints.
Study fingerprinting in the textbook and research and discuss the topic including
–but not limited to–
fingerprint history, types and different methods used to develop and preserve prints.
In addition, research and discuss Integrated Automated Fingerprint Identification System (IAFIS).
Due Sunday
.
Topic is Domestic Violence, Both men and women being the abus.docxVannaSchrader3
Topic is:
Domestic Viole
nce
, Both men and women being the abuser
Ask a question: Identify an issue of concern or personal curiosity relating to your profession.
Identify two bodies of knowledge: Which two disciplines will be used to help answer the question?
Example: History and Sociology
Conduct a literature review: What research has been done to help answer this question?
Hint #1: Make notes in the center column (see below) as resources are identified and read.
Hint #2: Compile an annotated bibliography as you find information as this will help you keep your sources organized and references correct.
Bringing It Together: Discuss the question extensively using information from the middle column above.
Conclusion: End the discussion with a conclusion—answer the question! Please note, there are two parts to the conclusion:
Part #1: Answer your question and discuss how (if) your personal views have changed based on what you’ve learned.
Part #2: Discuss how you plan to build on this knowledge going forward.
.
Topic is regional integration .First You need to find article and re.docxVannaSchrader3
Topic is regional integration .First You need to find article and resources which is related with this topic. you should write three pages about this article, resources and topic
I told assignment's structure in link that is why please check the link(file)
my native language is not English that is why if you use more simple words in assignment, it will be better
.
Topic Human Trafficking in relation to US Border and Coastal securi.docxVannaSchrader3
Topic: Human Trafficking in relation to US Border and Coastal security.
You are to prepare your paper in a word document (Times New Roman, Font 12-double space) using APA style format ("Resources" and APA info attached below). Your research paper should be
10-12 pages of content excluding your title page and reference page
. A minimum of 1
0 outside references required.
.
Topic is AutonomyShort papers should use double spacing, 12-point .docxVannaSchrader3
Topic is Autonomy
Short papers should use double spacing, 12-point Times New Roman font, and one-inch margins. Sources should be cited according to a discipline-appropriate citation method. Page-length requirements: 1–2 pages,
APA format and properly cited.
Will be cheched for originality through Turn it in.
.
Topic Genetic connection of hypertension to cardiovascular disease .docxVannaSchrader3
Topic: Genetic connection of hypertension to cardiovascular disease in african americans?
Needs to be specific and to address better current health disparities in specific population groups as well as the prevention of selected public health issue. Clearly and properly present the material by using relevant scientific information, statistical data, and research-based evidence from identified credible external sources.
Length: The written component of this assignment should be a minimum of 8 double-spaced pages.
References: At least
eight
references
must be included from
scholarly sources
. Quoted materialsshould not exceed 10% of the total paper (since the focus of these assignments is critical thinking). Use your own words and build on the ideas of others. Materials copied verbatim from external sources must be enclosed in quotation marks. In-text reference citations are required as well as a list of references at the end of the assignment. (APA format is required.)
Organization: Subheadings should be used to organize your paper according to the questions.
Format: APA format is required for this assignment.
I have attached the annotated bibliography with sources to be used as well as an outline for reference on how to structure the paper.
.
topic Errors (medication or patient injury)in particular stra.docxVannaSchrader3
topic: Errors (medication or patient injury)
in particular strategies for reducing medication errors
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Topic differences between folk guitar and classic guitar.Minimu.docxVannaSchrader3
Topic: differences between folk guitar and classic guitar.
Minimum of 1500 words. Double-spaced. Cite ALL sources appropriately. Use MLA or APA (or any other accepted publication) for citation standards.
This is a research paper. Do not plagiarize materials. Use quotes and cite other people's work whenever it is appropriate. Do your best to be creative and original with your writing style rather than "regurgitate" information to me. You may be as creative as you like (graphics, photos, audio) as long as your paper is concise, has proper flow and informs me of something about the guitar.
.
Topic Death Investigations. Review homicide investigation as de.docxVannaSchrader3
Topic: Death Investigations.
Review homicide investigation as described in the textbook and through research including
–but not limited to–
types of wrongful deaths, the preliminary investigation, dying declaration, estimating time of death, gunshot wounds, autoerotic death investigation, and suicide investigation.
Submit to the Dropbox before midnight
Sunday
.
.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
62. · Types of Data
· Modifying Data in Excel
· Creating Distributions from Data
· Measures of Location
· Measures of Variability
· Analyzing Distributions
· Measures of Association Between Two Variables
· Data Cleansing
Learning Objectives
By the end of this module, students should be able to:
· Understand the categorization of analytical methods and
models
· Define the Four Vs of Big Data
· Discuss business analytics in practice for multiple industries
· Understand the legal and ethical issues in the use of data and
analytics
· Identify the different types of data
· Create and analyze distributions with data
· Sort and filter data in Excel
· Report measures of variability
· Measure association between two variables
· Cleanse data
Running head: ETHICS
1
ETHICS
2
Ethics
Shakitha Reed
Ethical considerations in development and application of
artificial Intelligences, data management, or technology
When developing artificial intelligence, data management or
63. technology application, ethical considerations are important.
Ethical considerations do not only focus on morally bag or good
things but also revolve around morally problematic issues that
should be addressed. The application of data management,
artificial intelligence and technology are accompanied by
promises of numerous benefits. The designers and developers of
this technology should ensure that the promises are achieved by
promoting high level of productivity and efficiency (Stahl.B.C.,
2021). The moral considerations should also ensure that the
well-being of the people is promoted by allowing them to live
better and promote human flourishing.
The developers of new technology system should ensure that
they uphold privacy dignity and human rights in the application
of the system The systems are profound to impose risks to
privacy and dignity of user. They should be designed in a way
that it protect the identity and personal information of the user
(European Parliamentary Research Service, 2020). Users should
be enlightened on ways to ensure their identity and privacy is
protected when using these technology systems.
The application of new technology should ensure that it
achieves equality in its benefits. It should not benefit one party
over others such as the rich over the poor. They should ensure
that they provide significant and diverse benefits to the entire
society by facilitating greater productivity and efficiency at a
lower cost (European Parliamentary Research Service, 2020).
These technologies should have the ability to tackle numerous
global issues such as conflicts, poverty and diseases among
others so that the lives of countless people are improved.
Therefore, it is important for the developers of AI, data
management and technology to ensure moral consideration.
Moral consideration will help in ensuring that the developed
technology system is effective in promoting the lives of people
without discrimination or violation of human rights.
64. References
European Parliamentary Research Service. (2020). The Ethics of
Artificial Intelligence: Issues and Initiatives. London: European
Parliamentary Research Service.
Stahl.B.C. (2021). Ethical Issues of AI. Artificial Intelligence
for a Better Future , 35-53.