This document provides information on the course "Business Intelligence- Data Warehousing and Analytics" for the third semester of the B.E Computer Science program. The course aims to teach students about transaction processing vs analytical applications, the business intelligence framework, data warehousing methodology, and using metrics to achieve business goals. The course will cover topics such as digital data types, OLTP vs OLAP systems, data integration, dimensional modeling, and enterprise reporting. Assessment will be based on three levels of attainment and the course intends to help students understand and apply concepts of business intelligence.
This document provides an overview of a course on business intelligence and data visualization. The course objectives are to introduce data visualization theories, techniques, and tools for analyzing and presenting business data. Students will learn to design, develop, and evaluate effective visualizations and dashboards using tools like Tableau. The course focuses on how business intelligence uses analytics tools to combine data from multiple sources and help users make more informed decisions. Key topics covered include the fundamentals of business intelligence, Tableau functionality, data modeling, and creating dashboards and stories.
Pal gov.tutorial1.session9 10.bpmn-overview (mahmoud saheb's conflicted copy ...Mustafa Jarrar
This document provides an overview of business process modeling and the Business Process Modeling Notation (BPMN). It introduces key concepts related to enterprise modeling, business process management, and service-oriented architecture. The document outlines a session on BPMN that will demonstrate basic knowledge of BPMN notations and concepts, and how to use business process modeling tools like BizAgi and Bonita. The session will include examples, videos, and hands-on activities with modeling tools.
The Lean IT training provides the stimulus which is required for companies to stand out in the competitive world economy. Adoption of Lean in an organization will enable the smart usage of Information technology to enhance the business performance and improve the service levels.
Lean IT training provides critical knowledge of the principles of Lean philosophy, application of this philosophy in an IT-environment, validate their leadership of Lean methodology, and making continuous improvement using small incremental change using Kaizen.
To know more about Lean IT training worldwide,
please contact us at -
Email: support@invensislearning.com
Phone - US +1-910-726-3695,
Website: https://www.invensislearning.com
theroom will build a website that goes above and beyond to create brand recognition for your business. By considering your customers every step of the way theroom can deliver a strategic information architecture and an intuitive user experience.
This document provides an overview of a course on Predictive Modeling using IBM SPSS Statistics. The course is divided into 5 units that cover topics such as reading, organizing, and transforming data in SPSS; conducting descriptive and inferential statistics; creating graphical displays; and performing statistical analyses like t-tests, ANOVA, correlation, regression, and predictive analysis. Students will learn how to import, manage, and analyze data in SPSS through illustrative problems and projects involving both parametric and non-parametric statistical tests. The goal is for students to gain experience in using SPSS to conduct statistical analyses and predictive modeling on data.
This portfolio document contains information about Michael Hanaway including:
- A table of contents outlining the different sections
- A statement of authenticity, mission statement, and elevator speech about his background and goals
- Details of his education including his plan of study, course descriptions, and academic history
- His resume highlighting his experience in graphic design, sales, and customer service
- A section on his awards and accomplishments as well as his career goals and path
- Examples of his work including pseudocode, flowcharts, and programming samples in C++
Data Warehousing and Business Intelligence Training in bangalore,Our Data Warehousing and BI training helps you learn data warehousing and data mining concepts.
The document provides information about a course on Big Data Analytics taught at Malla Reddy College of Engineering & Technology. It includes 5 units that will be covered: Introduction to Big Data and Analytics, Introduction to Technology Landscape, Introduction to MongoDB and MapReduce Programming, Introduction to Hive and Pig, and Introduction to Data Analytics with R. The course aims to introduce students to big data tools and information standard formats. It will cover topics such as structured and unstructured data, Hadoop, MongoDB, MapReduce, Hive, Pig, and machine learning algorithms.
This document provides an overview of a course on business intelligence and data visualization. The course objectives are to introduce data visualization theories, techniques, and tools for analyzing and presenting business data. Students will learn to design, develop, and evaluate effective visualizations and dashboards using tools like Tableau. The course focuses on how business intelligence uses analytics tools to combine data from multiple sources and help users make more informed decisions. Key topics covered include the fundamentals of business intelligence, Tableau functionality, data modeling, and creating dashboards and stories.
Pal gov.tutorial1.session9 10.bpmn-overview (mahmoud saheb's conflicted copy ...Mustafa Jarrar
This document provides an overview of business process modeling and the Business Process Modeling Notation (BPMN). It introduces key concepts related to enterprise modeling, business process management, and service-oriented architecture. The document outlines a session on BPMN that will demonstrate basic knowledge of BPMN notations and concepts, and how to use business process modeling tools like BizAgi and Bonita. The session will include examples, videos, and hands-on activities with modeling tools.
The Lean IT training provides the stimulus which is required for companies to stand out in the competitive world economy. Adoption of Lean in an organization will enable the smart usage of Information technology to enhance the business performance and improve the service levels.
Lean IT training provides critical knowledge of the principles of Lean philosophy, application of this philosophy in an IT-environment, validate their leadership of Lean methodology, and making continuous improvement using small incremental change using Kaizen.
To know more about Lean IT training worldwide,
please contact us at -
Email: support@invensislearning.com
Phone - US +1-910-726-3695,
Website: https://www.invensislearning.com
theroom will build a website that goes above and beyond to create brand recognition for your business. By considering your customers every step of the way theroom can deliver a strategic information architecture and an intuitive user experience.
This document provides an overview of a course on Predictive Modeling using IBM SPSS Statistics. The course is divided into 5 units that cover topics such as reading, organizing, and transforming data in SPSS; conducting descriptive and inferential statistics; creating graphical displays; and performing statistical analyses like t-tests, ANOVA, correlation, regression, and predictive analysis. Students will learn how to import, manage, and analyze data in SPSS through illustrative problems and projects involving both parametric and non-parametric statistical tests. The goal is for students to gain experience in using SPSS to conduct statistical analyses and predictive modeling on data.
This portfolio document contains information about Michael Hanaway including:
- A table of contents outlining the different sections
- A statement of authenticity, mission statement, and elevator speech about his background and goals
- Details of his education including his plan of study, course descriptions, and academic history
- His resume highlighting his experience in graphic design, sales, and customer service
- A section on his awards and accomplishments as well as his career goals and path
- Examples of his work including pseudocode, flowcharts, and programming samples in C++
Data Warehousing and Business Intelligence Training in bangalore,Our Data Warehousing and BI training helps you learn data warehousing and data mining concepts.
The document provides information about a course on Big Data Analytics taught at Malla Reddy College of Engineering & Technology. It includes 5 units that will be covered: Introduction to Big Data and Analytics, Introduction to Technology Landscape, Introduction to MongoDB and MapReduce Programming, Introduction to Hive and Pig, and Introduction to Data Analytics with R. The course aims to introduce students to big data tools and information standard formats. It will cover topics such as structured and unstructured data, Hadoop, MongoDB, MapReduce, Hive, Pig, and machine learning algorithms.
The document provides information about a course on Big Data Analytics taught at Malla Reddy College of Engineering & Technology. It includes 5 units that will be covered: Introduction to Big Data and Analytics, Introduction to Technology Landscape, Introduction to MongoDB and MapReduce Programming, Introduction to Hive and Pig, and Introduction to Data Analytics with R. The course aims to introduce students to big data tools and information standard formats to help them design data for analytics and work with tools like Hadoop, Scala, and machine learning algorithms.
The document discusses the development of IT as a profession and the challenges faced in establishing it as such. It proposes using a model of a profession that includes elements like a code of ethics, standards of practice, body of knowledge, preparatory education, certification, and professional development. The IEEE is working to develop an Enterprise IT Body of Knowledge (ITBOK) to help address the challenges by providing a common framework and bringing together existing efforts, with input from various organizations. The goal is to better define IT job roles and career paths, establish competency standards, and gain recognition of IT professionals.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
This document outlines the fundamentals of a data science course, including its objectives, outcomes, and syllabus. The course aims to introduce students to common data science tools and teach programming for data analytics. It covers topics like data analysis with Excel, NumPy, Pandas, and Matplotlib. The syllabus includes 6 units covering data science basics, the data science process, tools for analysis and visualization, and content beyond the core topics like R and Power BI. Online resources are also provided for additional learning.
Business Process Modeling Notation FundamentalsMustafa Jarrar
The document provides an overview of Business Process Modeling Notation (BPMN) fundamentals. It discusses the history and development of BPMN, including its creation in 2001 and release of version 1.0 in 2004. It then defines BPMN as a standard for defining and communicating business processes. The document also notes that while BPMN 1.0 allowed modeling of business processes, it did not formally define the semantics of the diagrams. Later work by the Object Management Group aimed to address this by developing a Business Process Definition Metamodel to serve as the metamodel for BPMN.
Enabling a Bimodal IT Framework for Advanced Analytics with Data VirtualizationDenodo
Watch: https://bit.ly/2FLc5I2
Being able to maintain a well managed and curated Data Warehouse, along with keeping up with all of the demands of a very sophisticated consumer group can be a challenge. The new user wants access to data, they want to experiment, fail fast and if they do find usable insights/algorithms they want them productionized. This puts pressure on an IT organization and pushes them closer to a Bimodal operation where the regular IT processes that are highly curated, well defined and managed contrast sharply with the demands of the more sophisticated user.
In the recently published TDWI Best Practices Report ,“Data Management for Advanced Analytics”, Philip Russom, DM for Advanced Analytics some of these newer requirements for the more sophisticated user are discussed in some length. How can IT support traditional demands around traditional BI and Reporting, whilst enabling the business with more demand for data and Advanced Analytics in mind?
Attend and learn:
- How data virtualization enables this Bi-Modal approach to Data Management.
- How data virtualization enables compelling use cases for data management and advanced analytics
- How we can achieve this important balance with process and technology.
Business Intelligence, Analytics, and Data Science A ManagerialTawnaDelatorrejs
This document summarizes key points from Chapter 3 of a textbook on business intelligence, analytics, and data science. It discusses data warehousing concepts like architectures, data integration processes, dimensional modeling, and online analytical processing. It also covers business performance management, describing it as a real-time system that alerts managers to opportunities and problems and empowers them to react through models and collaboration. The chapter examines topics like data warehousing development approaches, administration, security, and emerging technologies in the field.
BDV Skills Accreditation - Definition and ensuring of digital roles and compe...Big Data Value Association
The objective of the workshop is to highlight the need for a pan European level skill recognition for Big Data that stimulates mobility and fulfils the definition of overarching Learning Objectives & Overarching Learning Impacts. It is also meant to get feedback on the formats that are being prepared namely, usage of Badges, Label and EIT Label for professionals.
This document provides an overview of a tutorial on business process management. It discusses best practices for BPM including having strong partnerships with IT, using industry standard tools, selecting the right initial project, and managing expectations. It also discusses frameworks for assessing business process reengineering and outlines characteristics of successful BPM projects such as defining clear metrics and fostering cultural change.
IT and Business Process Modelling course at IT University of Copenhagen (Lect...Thomas Hildebrandt
First and second lecture for the IT and Business Process Modelling course at IT University of Copenhagen.
The course has focus on flexibility in business processes and introduces to DCR Graphs business process constraint mapping (using www.dcrgraphs.net) and BPMN modelling (using www.academic.signavio.com).
It is based on the book "Enabling Flexibility in Process-Aware Information Systems - Challenges, Methods, Technologies" by Manfred Reichert and Barbara Weber. (http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-3-642-30408-8)
This document provides information about courses for a Bachelor of Technology in Computer Science and Engineering for Semester VIII. It lists 5 required courses covering topics like project work, electives in professional and open electives, and corresponding labs. Details are provided for each course including credit hours, examination scheme, topics covered and suggested reading materials. The document also outlines the eligibility criteria for elective courses.
This document contains contact and background information for Foo Long Jong, including his 17 years of experience in IT planning, management, and implementing ERP systems. It details his employment history managing IT sections and implementing various ERP systems and projects. It also lists his educational qualifications in information technology and computer skills.
The document describes different training delivery methods:
- ILT (Instructor-Led Training) sessions are conducted in a physical classroom.
- ILO (Instructor-Led Online Training) sessions are conducted via WebEx.
- FLEX Classroom combines ILT and ILO for flexibility.
Fhyzics is a Best Business Analytics training institute.
It's provides the business analytics training & Online business
analytics course.If You know more details Please visit
www.businessanalyticstraining.net
Fhyzics, global leader in business analytics. We provide Business Analytics Training & Certification. For know more about course offering please reachout to us at +91-900-305-9000 / www.businessanalyticstraining.net
The document discusses IT/IS architecture at three companies - Chubb Insurance, Nike, and United Way of Central Ohio. It provides details on the challenges each company faced with their IT architecture, and reasons for success or failure in addressing those challenges. Specifically, it notes that Chubb succeeded by establishing an enterprise architecture plan, chief architect, and IT staff integrated with the business. Nike failed because their issue was closely tied to a core process and they didn't devote enough resources to their new system. The document recommends United Way develop a system map and database integration plan.
This document describes Module 4 of an Industry 4.0 Specialist training programme on work and organization design in the age of digital transformation. The module objectives are to teach how work changes with Industry 4.0 technologies and the challenges and opportunities this presents for employees. Topics covered include computer integrated manufacturing systems, case studies on the interdependence of humans and technology, and organizational challenges. The module contains exercises on personality traits, scheduling with different dispatch rules, and combining critical ratio and starvation avoidance objectives in dispatching.
Confirming PagesLess managing. More teaching. Greater AlleneMcclendon878
Confirming Pages
Less managing. More teaching. Greater learning.
INSTRUCTORS GET:
• Interactive Applications – book-specific interactive
assignments that require students to APPLY what
they’ve learned.
• Simple assignment management, allowing you to
spend more time teaching.
• Auto-graded assignments, quizzes, and tests.
• Detailed Visual Reporting where student and
section results can be viewed and analyzed.
• Sophisticated online testing capability.
• A filtering and reporting function
that allows you to easily assign and
report on materials that are correlated
to accreditation standards, learning
outcomes, and Bloom’s taxonomy.
• An easy-to-use lecture capture tool.
Would you like your students to show up for class more prepared? (Let’s face it, class
is much more fun if everyone is engaged and prepared…)
Want ready-made application-level interactive assignments, student progress
reporting, and auto-assignment grading? (Less time grading means more time teaching…)
Want an instant view of student or class performance relative to learning
objectives? (No more wondering if students understand…)
Need to collect data and generate reports required for administration or
accreditation? (Say goodbye to manually tracking student learning outcomes…)
Want to record and post your lectures for students to view online?
INSTRUCTORS...
With McGraw-Hill's Connect® MIS,
haa7685X_fm_i-xxxv.indd ihaa7685X_fm_i-xxxv.indd i 12/20/11 9:29 PM12/20/11 9:29 PM
Confirming Pages
Want an online, searchable version of your textbook?
Wish you could reference your textbook online while you’re doing
your assignments?
Want to get more value from your textbook purchase?
Think learning MIS should be a bit more interesting?
Connect® Plus MIS eBook
If you choose to use Connect™ Plus MIS, you have an affordable and
searchable online version of your book integrated with your other
online tools.
Connect® Plus MIS eBook offers features like:
• Topic search
• Direct links from assignments
• Adjustable text size
• Jump to page number
• Print by section
Check out the STUDENT RESOURCES
section under the Connect® Library tab.
Here you’ll find a wealth of resources designed to help you
achieve your goals in the course. You’ll find things like quizzes,
PowerPoints, and Internet activities to help you study.
Every student has different needs, so explore the STUDENT
RESOURCES to find the materials best suited to you.
haa7685X_fm_i-xxxv.indd iihaa7685X_fm_i-xxxv.indd ii 12/20/11 9:29 PM12/20/11 9:29 PM
Confirming Pages
Management Information Systems
FOR THE INFORMATION AGE
NINTH EDITION
Stephen Haag
DANIELS COLLEGE OF BUSINESS
UNIVERSITY OF DENVER
Maeve Cummings
KELCE COLLEGE OF BUSINESS
PITTSBURG STATE UNIVERSITY
haa7685X_fm_i-xxxv.indd iiihaa7685X_fm_i-xxxv.indd iii 12/26/11 5:37 PM12/26/11 5:37 PM
Confirming Pages
MANAGEMENT INFORMATION SYSTEMS FOR THE INF ...
This document provides an introduction to machine learning and neural networks. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It also describes the main types of machine learning as supervised learning, unsupervised learning, and reinforcement learning. The document then discusses neural networks and their biological inspiration from the human brain. It provides examples of neural network applications and describes the basic structure and functioning of neural networks.
The document discusses input and output streams in Java. It provides code examples of drawing a string to the screen using graphics, defines what an applet is, and compares the differences between applications and applets. It also discusses streams, reading and writing objects from streams, and input/output stream classes. Methods for controlling an applet's appearance like paint() and repaint() are described, as well as how to set colors and pass parameters to an applet.
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The document provides information about a course on Big Data Analytics taught at Malla Reddy College of Engineering & Technology. It includes 5 units that will be covered: Introduction to Big Data and Analytics, Introduction to Technology Landscape, Introduction to MongoDB and MapReduce Programming, Introduction to Hive and Pig, and Introduction to Data Analytics with R. The course aims to introduce students to big data tools and information standard formats to help them design data for analytics and work with tools like Hadoop, Scala, and machine learning algorithms.
The document discusses the development of IT as a profession and the challenges faced in establishing it as such. It proposes using a model of a profession that includes elements like a code of ethics, standards of practice, body of knowledge, preparatory education, certification, and professional development. The IEEE is working to develop an Enterprise IT Body of Knowledge (ITBOK) to help address the challenges by providing a common framework and bringing together existing efforts, with input from various organizations. The goal is to better define IT job roles and career paths, establish competency standards, and gain recognition of IT professionals.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
This document outlines the fundamentals of a data science course, including its objectives, outcomes, and syllabus. The course aims to introduce students to common data science tools and teach programming for data analytics. It covers topics like data analysis with Excel, NumPy, Pandas, and Matplotlib. The syllabus includes 6 units covering data science basics, the data science process, tools for analysis and visualization, and content beyond the core topics like R and Power BI. Online resources are also provided for additional learning.
Business Process Modeling Notation FundamentalsMustafa Jarrar
The document provides an overview of Business Process Modeling Notation (BPMN) fundamentals. It discusses the history and development of BPMN, including its creation in 2001 and release of version 1.0 in 2004. It then defines BPMN as a standard for defining and communicating business processes. The document also notes that while BPMN 1.0 allowed modeling of business processes, it did not formally define the semantics of the diagrams. Later work by the Object Management Group aimed to address this by developing a Business Process Definition Metamodel to serve as the metamodel for BPMN.
Enabling a Bimodal IT Framework for Advanced Analytics with Data VirtualizationDenodo
Watch: https://bit.ly/2FLc5I2
Being able to maintain a well managed and curated Data Warehouse, along with keeping up with all of the demands of a very sophisticated consumer group can be a challenge. The new user wants access to data, they want to experiment, fail fast and if they do find usable insights/algorithms they want them productionized. This puts pressure on an IT organization and pushes them closer to a Bimodal operation where the regular IT processes that are highly curated, well defined and managed contrast sharply with the demands of the more sophisticated user.
In the recently published TDWI Best Practices Report ,“Data Management for Advanced Analytics”, Philip Russom, DM for Advanced Analytics some of these newer requirements for the more sophisticated user are discussed in some length. How can IT support traditional demands around traditional BI and Reporting, whilst enabling the business with more demand for data and Advanced Analytics in mind?
Attend and learn:
- How data virtualization enables this Bi-Modal approach to Data Management.
- How data virtualization enables compelling use cases for data management and advanced analytics
- How we can achieve this important balance with process and technology.
Business Intelligence, Analytics, and Data Science A ManagerialTawnaDelatorrejs
This document summarizes key points from Chapter 3 of a textbook on business intelligence, analytics, and data science. It discusses data warehousing concepts like architectures, data integration processes, dimensional modeling, and online analytical processing. It also covers business performance management, describing it as a real-time system that alerts managers to opportunities and problems and empowers them to react through models and collaboration. The chapter examines topics like data warehousing development approaches, administration, security, and emerging technologies in the field.
BDV Skills Accreditation - Definition and ensuring of digital roles and compe...Big Data Value Association
The objective of the workshop is to highlight the need for a pan European level skill recognition for Big Data that stimulates mobility and fulfils the definition of overarching Learning Objectives & Overarching Learning Impacts. It is also meant to get feedback on the formats that are being prepared namely, usage of Badges, Label and EIT Label for professionals.
This document provides an overview of a tutorial on business process management. It discusses best practices for BPM including having strong partnerships with IT, using industry standard tools, selecting the right initial project, and managing expectations. It also discusses frameworks for assessing business process reengineering and outlines characteristics of successful BPM projects such as defining clear metrics and fostering cultural change.
IT and Business Process Modelling course at IT University of Copenhagen (Lect...Thomas Hildebrandt
First and second lecture for the IT and Business Process Modelling course at IT University of Copenhagen.
The course has focus on flexibility in business processes and introduces to DCR Graphs business process constraint mapping (using www.dcrgraphs.net) and BPMN modelling (using www.academic.signavio.com).
It is based on the book "Enabling Flexibility in Process-Aware Information Systems - Challenges, Methods, Technologies" by Manfred Reichert and Barbara Weber. (http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-3-642-30408-8)
This document provides information about courses for a Bachelor of Technology in Computer Science and Engineering for Semester VIII. It lists 5 required courses covering topics like project work, electives in professional and open electives, and corresponding labs. Details are provided for each course including credit hours, examination scheme, topics covered and suggested reading materials. The document also outlines the eligibility criteria for elective courses.
This document contains contact and background information for Foo Long Jong, including his 17 years of experience in IT planning, management, and implementing ERP systems. It details his employment history managing IT sections and implementing various ERP systems and projects. It also lists his educational qualifications in information technology and computer skills.
The document describes different training delivery methods:
- ILT (Instructor-Led Training) sessions are conducted in a physical classroom.
- ILO (Instructor-Led Online Training) sessions are conducted via WebEx.
- FLEX Classroom combines ILT and ILO for flexibility.
Fhyzics is a Best Business Analytics training institute.
It's provides the business analytics training & Online business
analytics course.If You know more details Please visit
www.businessanalyticstraining.net
Fhyzics, global leader in business analytics. We provide Business Analytics Training & Certification. For know more about course offering please reachout to us at +91-900-305-9000 / www.businessanalyticstraining.net
The document discusses IT/IS architecture at three companies - Chubb Insurance, Nike, and United Way of Central Ohio. It provides details on the challenges each company faced with their IT architecture, and reasons for success or failure in addressing those challenges. Specifically, it notes that Chubb succeeded by establishing an enterprise architecture plan, chief architect, and IT staff integrated with the business. Nike failed because their issue was closely tied to a core process and they didn't devote enough resources to their new system. The document recommends United Way develop a system map and database integration plan.
This document describes Module 4 of an Industry 4.0 Specialist training programme on work and organization design in the age of digital transformation. The module objectives are to teach how work changes with Industry 4.0 technologies and the challenges and opportunities this presents for employees. Topics covered include computer integrated manufacturing systems, case studies on the interdependence of humans and technology, and organizational challenges. The module contains exercises on personality traits, scheduling with different dispatch rules, and combining critical ratio and starvation avoidance objectives in dispatching.
Confirming PagesLess managing. More teaching. Greater AlleneMcclendon878
Confirming Pages
Less managing. More teaching. Greater learning.
INSTRUCTORS GET:
• Interactive Applications – book-specific interactive
assignments that require students to APPLY what
they’ve learned.
• Simple assignment management, allowing you to
spend more time teaching.
• Auto-graded assignments, quizzes, and tests.
• Detailed Visual Reporting where student and
section results can be viewed and analyzed.
• Sophisticated online testing capability.
• A filtering and reporting function
that allows you to easily assign and
report on materials that are correlated
to accreditation standards, learning
outcomes, and Bloom’s taxonomy.
• An easy-to-use lecture capture tool.
Would you like your students to show up for class more prepared? (Let’s face it, class
is much more fun if everyone is engaged and prepared…)
Want ready-made application-level interactive assignments, student progress
reporting, and auto-assignment grading? (Less time grading means more time teaching…)
Want an instant view of student or class performance relative to learning
objectives? (No more wondering if students understand…)
Need to collect data and generate reports required for administration or
accreditation? (Say goodbye to manually tracking student learning outcomes…)
Want to record and post your lectures for students to view online?
INSTRUCTORS...
With McGraw-Hill's Connect® MIS,
haa7685X_fm_i-xxxv.indd ihaa7685X_fm_i-xxxv.indd i 12/20/11 9:29 PM12/20/11 9:29 PM
Confirming Pages
Want an online, searchable version of your textbook?
Wish you could reference your textbook online while you’re doing
your assignments?
Want to get more value from your textbook purchase?
Think learning MIS should be a bit more interesting?
Connect® Plus MIS eBook
If you choose to use Connect™ Plus MIS, you have an affordable and
searchable online version of your book integrated with your other
online tools.
Connect® Plus MIS eBook offers features like:
• Topic search
• Direct links from assignments
• Adjustable text size
• Jump to page number
• Print by section
Check out the STUDENT RESOURCES
section under the Connect® Library tab.
Here you’ll find a wealth of resources designed to help you
achieve your goals in the course. You’ll find things like quizzes,
PowerPoints, and Internet activities to help you study.
Every student has different needs, so explore the STUDENT
RESOURCES to find the materials best suited to you.
haa7685X_fm_i-xxxv.indd iihaa7685X_fm_i-xxxv.indd ii 12/20/11 9:29 PM12/20/11 9:29 PM
Confirming Pages
Management Information Systems
FOR THE INFORMATION AGE
NINTH EDITION
Stephen Haag
DANIELS COLLEGE OF BUSINESS
UNIVERSITY OF DENVER
Maeve Cummings
KELCE COLLEGE OF BUSINESS
PITTSBURG STATE UNIVERSITY
haa7685X_fm_i-xxxv.indd iiihaa7685X_fm_i-xxxv.indd iii 12/26/11 5:37 PM12/26/11 5:37 PM
Confirming Pages
MANAGEMENT INFORMATION SYSTEMS FOR THE INF ...
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The document discusses input and output streams in Java. It defines a stream as a sequence of data and explains that input streams read data from a source while output streams write data to a destination. It then describes the hierarchies of byte stream classes like InputStream, OutputStream, FileInputStream and FileOutputStream. It also covers the hierarchies of character stream classes like Reader, Writer, FileReader and FileWriter. Code examples are provided to demonstrate reading and writing data using byte streams and character streams.
This document outlines the key applications of a multiple relationship management system, including inventory management, purchasing, sales and marketing, manufacturing, financial management, customer relationship management, and human resource management. The system aims to help organizations achieve integration and planning among employees and clients through modules that track inventory, manage procurement and sales processes, monitor manufacturing, oversee finances, improve customer service, and address human resource functions.
This document discusses backpropagation, an algorithm used to train feedforward neural networks. It begins by explaining gradient descent and how it is used to minimize error in the network by adjusting weights. It then describes how backpropagation specifically works to calculate the gradient of the error with respect to the weights in each layer by propagating error backwards from the output layer through the hidden layers. The general backpropagation rule is provided to update weights based on this error gradient calculation.
This document provides an overview of artificial neural networks. It discusses the biological inspiration from the brain and properties of artificial neural networks. Perceptrons and their limitations are described. Gradient descent and backpropagation algorithms for training multi-layer networks are introduced. Activation functions and network architectures are also summarized.
1. An interface is a blueprint of a class that defines abstract methods but does not provide method implementations. Interfaces are used to achieve abstraction and multiple inheritance in Java.
2. The properties of interfaces are that they can only contain abstract methods and static final fields, cannot be instantiated, and implemented classes must implement all interface methods.
3. A sample program demonstrates defining a Drawable interface with a draw() method and implementing class Rectangle that provides the draw() method implementation.
The document provides an introduction to machine learning and neural networks. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It also discusses different machine learning algorithms like supervised learning, unsupervised learning, and reinforcement learning. The document then describes neural networks and their biological inspiration from the human brain. It explains the basic structure and functioning of artificial neurons and neural networks. Finally, it discusses common neural network training techniques like backpropagation that are used to minimize errors and update weights in multi-layer neural networks.
This registration form collects personal, educational, career, and contact information from alumni of Hindusthan College of Engineering & Technology. It requests the alumnus's name, registration number, gender, date of birth, degree earned, department or branch, year of graduation, current employer, job title, work locations, addresses, email addresses, phone numbers, additional qualifications, marital status, and any other suggestions. The form is for the Hindusthan College of Engineering & Technology Alumni Association and includes their contact details.
The document provides information about the Theory of Computation course offered as part of the B.Tech program. The course aims to teach students about basic concepts of automata theory including finite automata, regular languages and expressions, context free grammars, pushdown automata, and Turing machines. The course is divided into 5 units that cover these topics over 45 instructional hours. The expected learning outcomes are for students to understand theoretical concepts of automata, apply automata to regular languages, apply context free grammar normalization, understand PDA and Turing machines, and apply concepts of decidability and tractability. References for two textbooks and three other reference books are also provided.
This document discusses backpropagation, an algorithm used to train feedforward neural networks. It begins by explaining gradient descent and how it is used to minimize error in the network by adjusting weights. It then describes how backpropagation specifically works to calculate the gradient of the error with respect to the weights in each layer by propagating error backwards from the output layer through the hidden layers. The general backpropagation rule is provided to update weights based on this error gradient calculation.
This document provides an overview of artificial neural networks. It discusses the biological inspiration from the brain and properties of artificial neural networks. Perceptrons and their limitations are described. Gradient descent and backpropagation algorithms for training multi-layer networks are introduced. Activation functions and network architectures are also summarized.
This document provides information on the B.Tech course "Social Networks" including its objectives, units, outcomes and references. The course aims to help students understand social networks and their components, represent knowledge using ontology, mine user behavior and communities, model network evolution, and mine text, opinions and multimedia data. The 5 units cover introduction to social networks and analysis, knowledge representation using ontology, mining communities and social media, models of network evolution, and text/opinion/multimedia mining. The outcomes include working with network internals, ontology-based knowledge representation, mining user behavior, predicting network evolution, and opinion mining in networks. References provided are textbooks and papers on social network analysis, mining, technologies and applications.
The document discusses Java I/O streams and collections framework. It covers byte streams, character streams, reading console input, writing console output, and reading and writing files. It also discusses different I/O stream classes like InputStream, OutputStream, Reader, Writer and their subclasses. It provides examples of reading input from the keyboard and writing output to the console using System.in, System.out and System.err streams. It also discusses reading characters and strings from the keyboard using BufferedReader class.
This document contains a question bank for the Java Programming course at Hindusthan College of Engineering And Technology. The question bank is divided into three parts - Part A contains 22 multiple choice questions worth 2 marks each and assessing Concept Outcome 1 (CO1). Part B contains 11 questions worth 14 marks each, also assessing CO1. Part C contains 9 questions worth 10 marks each, again assessing CO1. The document provides a structured list of questions to aid students in their studying and preparation for exams in the Java Programming course.
The document provides information about the syllabus for a mobile computing course. It includes:
- Definitions of mobile computing and related terms like mobility and computing.
- Characteristics of mobile computing like ubiquity, location awareness, and personalization.
- Differences between mobile computing, wireless networking, and issues faced in wireless transmission.
- MAC protocols for wireless networks including CSMA/CA and RTS/CTS schemes.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
1. 1
Hindusthan College of Engineering and Technology
An Autonomous Institution Affiliated to Anna University | Approved by AICTE, New Delhi
Accreditedwith ‘A’ Grade by NAAC | Accredited by NBA (ECE, MECH, EEE, IT & CSE)
Valley Campus, Pollachi Highway, Coimbatore 641 032.| www.hicet.ac.in
Course Information Sheet (CIS)
1. Academic Year : 2021 – 22 EVEN Semester
2. Name of Course Coordinator :R.Gayathri
3. Department : Computer science and Engineering
4. Programme : B.E(CSE)
5. Class and semester : III/VI
6. Course code and title : 19CS6301 Business Intelligence- Data Warehousing and Analytics
7. Regulations : R2019
8. Course Category : PE
9. Contact hours : 45
10. Type of course : Theory
11. Credit : 3
12. Course Attainment level :Level 1: 66-75% ; Level 2 : 76-85% ; Level 3: >85%
13. Course pre-requisites : 19CS5251 Data Mining
13. Course Learning Objectives (CLO) :
1. To study about Transaction Processing and Analytical applications.
2. To demonstrate Business Intelligence framework.
3. To demonstrate Data Warehouse implementation and methodology.
4. To apply a business scenario, identify the metrics, indicators to achieve the business goal
5. To apply application of concepts using open source/MS Office
14. Course Outcomes (COs) :
Upon successful completion of this course, the student will be able to:
CO1 - Understand difference between Transaction Processing and Analytical applications and
describe the need for Business Intelligence
CO2 - Demonstrate to understand technology and processes associated with Business
Intelligence framework
CO3 - Demonstrate to understand Data Warehouse implementation methodology and project
life cycle
CO4 - Formulate given a business scenario, identify the metrics, indicators and make
2. 2
recommendations to achieve the business goal
CO5 - Demonstrate application of concepts using open source/MS Office.
15. Syllabus:
UNIT I - INTRODUCTION TO BUSINESS INTELLIGENCE 9
CO1
Introduction to digital data and its types – structured, semi-structured and unstructured, Introduction to
OLTP and OLAP (MOLAP, ROLAP, HOLAP)
UNIT II - BUSINESS INTELLIGENCE PROCESS AND FRAMEWORK 9
CO2 BI Definitions & Concepts, BI Framework, Data Warehousing concepts and its role in BI, BI
Infrastructure Components – BI Process, BI Technology, BI Roles & Responsibilities, Business
Applications of BI, BI best practices.
UNIT III - BASICS OF DATA INTEGRATION (EXTRACTION TRANSFORMATION
LOADING)
9
CO3 Concepts of data integration, needs and advantages of using data integration, introduction to common
data integration approaches, Meta data - types and sources, Introduction to data quality, data profiling
concepts and applications, introduction to ETL using Pentaho data Integration (formerly Kettle)
UNIT IV - INTRODUCTION TO MULTI-DIMENSIONAL DATA MODELING 9
CO4 Introduction to data and dimension modeling, multidimensional data model, ER Modeling vs. multi-
dimensional modeling, concepts of dimensions, facts, cubes, attribute, hierarchies, star and snowflake
schema, introduction to business metrics and KPIs, creating cubes using Microsoft Excel
UNIT V - BASICS OF ENTERPRISE REPORTING 9
CO5 A typical enterprise, Malcolm Baldrige - quality performance framework, balanced scorecard, enterprise
dashboard, balanced scorecard vs. enterprise dashboard, enterprise reporting using MS Access / MS
Excel, best practices in the design of enterprise dashboards
Total Instructional Hours - 45
16. Text books and Reference books:
T1: “Fundamentals of Business Analytics” by R.N.Prasad and Seema Acharya, Wiley 2011.
T2: “Data Strategy: How To Profit From A World Of Big Data, Analytics And The Internet Of
Things” by Bernard Marr.
R1: Business Intelligence by David Loshin, Second Edition, Elsevier, 2012.
R2: Business intelligence for the enterprise by Mike Biere, IBM Press, 2003.
R3: Business intelligence roadmap by Larissa Terpeluk Moss, Shaku Atre, Addison-Wesley
Professional, 2003.
R4: “Data Analytics For Beginners: Your Ultimate Guide To Learn And Master Data Analysis. Get
Your Business Intelligence Right – Accelerate Growth And Close More Sales” by Victor Finch.
3. 3
Video Links:
5
17. Course plan:
S.
No
Name of the Topic
No of
Hours
Cumul.
Hours
Teaching
Methods
Teaching
Aids
Text/
Referen
ce
books
UNIT I - INTRODUCTION TO BUSINESS INTELLIGENCE
GROUP I
1 Introduction to digital data and its types 1 1 Lecture
Video
T1
GROUP II
2 Structured 1 2
Lecture
PPT,
Video,
Animation
s
T1, R2
3 Semi-Structured 1 3
4 Unstructured 1 4
5 Introduction To OLTP 1 5
GROUP III
6 Introduction to OLAP 1 6
Lecture,
Quiz
Power
point
presentatio
n,
Video
T1, T2
7 MOLAP 1 7
8 ROLAP 1 8
9 HOLAP 1 9
Scheduled completion of Unit I : 9 hours
UNIT II - BUSINESS INTELLIGENCE PROCESS AND FRAMEWORK
GROUP I
10 BI Definitions & Concepts 1 10
Lecture Power
point
presentatio
n
T1, T2
11 BI Framework 1 11
12 Data Warehousing concepts and its role in BI 1 12
13 BI Infrastructure Components 1 13
Flipped
Class
T1
GROUP II
14 BI Process 1 14 Group
Discussi
on /
PPT/
Blackboar
d
15 BI Technology 1 15
4. 4
S.
No
Name of the Topic
No of
Hours
Cumul.
Hours
Teaching
Methods
Teaching
Aids
Text/
Referen
ce
books
16 BI Roles & Responsibilities 1 16 Lecture
/ Quiz
T1,R2
17 Business Applications of BI 1 17
18 BI best practices 1 18
Scheduled completion of Unit II : 9 hours
UNIT III - BASICS OF DATA INTEGRATION (EXTRACTION TRANSFORMATION LOADING
GROUP I
19 Concepts Of Data Integration 1 19
Lecture Video T1
20
Needs And Advantages Of Using Data
Integration
1 20
21
Introduction To Common Data Integration
Approaches
1 21
22 Meta data 1 22
GROUP II
23 Types And Source 1 23
Lecture,
Quiz
Video T1, R1
24 Introduction to data quality 1 24
25 Data Profiling Concepts And Applications, 1 25
Group III
26
Introduction To ETL Using Pentaho Data
Integration (Formerly Kettle)
1 26
Flipped
Class
Room
Power
Point
Presentati
on
T1
27
Introduction To ETL Using Pentaho Data
Integration (Formerly Kettle)
1 27
Scheduled completion of Unit III : 9 hours
UNIT IV - INTRODUCTION TO MULTI-DIMENSIONAL DATA MODELING
GROUP I
28
Introduction To Data And Dimension
Modeling, 1 28
Flipped
class
room
PPT R1
29 Multidimensional Data Model, 1 29
Lecture Power
point
presentatio
T1, R1
30
ER Modeling Vs. Multi-Dimensional
Modeling,
1 30
31 Concepts Of Dimensions, 1 31
32 Facts, Cubes, Attribute 1 32
5. 5
S.
No
Name of the Topic
No of
Hours
Cumul.
Hours
Teaching
Methods
Teaching
Aids
Text/
Referen
ce
books
n
Group Ii
33 Hierarchies, Star 1 33
Lecture Video,
Online
reference
video
T1,R1,
R2
34 Snowflake Schema 1 34
35 Introduction To Business Metrics And Kpis 1 35
36 Creating Cubes Using Microsoft Excel 1 36
Scheduled completion of Unit IV : 9 hours
UNIT V - BASICS OF ENTERPRISE REPORTING
GROUP I
37 A Typical Enterprise 1 37
Lecture Video
T1, R3
38 Malcolm Baldrige 1 38
GROUP II
39
Quality Performance Framework
1 39
Lecture Video,
Online
reference
video
T1,T2
40
Balanced Scorecard
1 40
41 Enterprise Dashboard 1 41
42
Balanced Scorecard Vs. Enterprise
Dashboard, 1 42
43
Enterprise Reporting Using MS Access / MS
Excel
1 43
44
Best Practices In The Design Of Enterprise
Dashboards
1 44
GROUP III
45
Best Practices In The Design Of Enterprise
Dashboards
1 45
Lecture,
Group
Discussio
n
Video T1,R3
Scheduled completion of Unit V : 9 hours
6. 6
18. Weightage of unit contents:
Factors considered,
F1 - Number of periods allotted for teaching the unit and weightage per hour is equal 1.
F2 - Usefulness of the content matter of the unit in the students’ learning point of view and
its weightage equal to 1 if useful, otherwise zero.
F3 - Usefulness of the content matter of the unit in understanding other units of the same
subject and its weightage equal to 1 if useful, otherwise zero.
F4- Usefulness of the content matter of the unit in understanding other subjects prescribed
for the programme and its weightage equal to 1 if useful, otherwise zero.
Topic F1 F2 F3 F4 A1
(Weightage)
A2
(%)
UNIT I - INTRODUCTION TO BUSINESS INTELLIGENCE
Introduction to digital data and its types
9
1 1
18 19.6
Structured, Semi-Structured, Unstructured 1 1
Introduction To OLTP and OLAP 1
MOLAP 1 1 1
ROLAP 1
HOLAP 1 1 1
UNIT II - BUSINESS INTELLIGENCEPROCESS ANDFRAMEWORK
16 17.4
BI Definitions & Concepts 1
BI Framework, Data Warehousing concepts and its role
in BI
1
BI Infrastructure Components, BI Process , BI
Technology
1 1
BI Roles & Responsibilities , Business Applications of
BI
1
UNIT III - BASICS OF DATA INTEGRATION (EXTRACTION
TRANSFORMATION LOADING
21 22.8
Concepts Of Data Integration, Needs And Advantages
Of Using Data Integration, 1
Introduction To Common Data Integration Approaches,
Meta Data - Types And Sources,
1 1
Introduction To Data Quality, Data Profiling Concepts
And Applications,
1 1
Introduction To ETL Using Pentaho Data Integration
(Formerly Kettle)
1 1
UNIT IV - INTRODUCTION TO MULTI-DIMENSIONAL DATA
MODELING
19 20.7
Introduction To Data And Dimension Modeling,
Multidimensional Data Model, 1
ER Modeling Vs. Multi-Dimensional Modeling, ,
Concepts Of Dimensions, Facts, Cubes, Attribute,
1
7. 7
Hierarchies, Star And Snowflake Schema
Introduction To Business Metrics And Kpis, Creating
Cubes Using Microsoft Excel 1 1
UNIT V - BASICS OF ENTERPRISE REPORTING
18 19.6
A Typical Enterprise, Malcolm Baldrige - Quality
Performance Framework, Balanced Scorecard,
Enterprise Dashboard,
9
1 1
Balanced Scorecard Vs. Enterprise Dashboard, 1
Enterprise Reporting Using MS Access / MS Excel,
Best Practices In The Design Of Enterprise
Dashboards
1 1
Total 92 100%
A1 – Total weightage
A2 – % of Weightage
19. Mapping syllabus with Bloom’s Taxonomy LOT and HOT:
Lower Order Thinking
R Remembering
Students are expected to Recall the information through Recognizing,
listing, describing, retrieving, naming, finding
U Understanding
Students are expected to Explain an ideas or concepts through
Interpreting, summarizing, paraphrasing, classifying, explaining
Ap Applying
Students are expected to Use the information in another familiar
situation through Implementing, carrying out, using, executing
Higher Order Thinking
A Analyzing
Students are expected to Break the information into parts to explore
understandings and relationships through Comparing, organizing,
deconstructing, interrogating, finding
E Evaluating
Students are expected to Evaluate the Justifying a decision or course of
action through Checking, hypothesizing, experimenting, judging
C Creating
Students are expected to Generate new ideas, products, or ways of
viewing things through Designing, constructing, planning, producing,
inventing.
UNIT I - INTRODUCTION TO BUSINESS INTELLIGENCE (Weightage 26%)
Sl.No Name of the Topic Process verb Types of thinking
1 Introduction to digital data and its types Explain, Discuss
Understanding
CO1
2 Structured, Semi-Structured, Unstructured
Explain, Discuss,
Explore, Depreciate
Applying
CO1
3
Introduction To OLTP and OLAP-
MOLAP,ROLAP& HOLAP
Expose, Infer,
Examine
Analyzing
CO1
R U Ap A E C Total
Type of thinking in Nos 0 1 1 1 0 0 3
Weightage,% 0 6.53 6.53 6.53 0 0 19.6
UNIT II - BUSINESS INTELLIGENCE PROCESS AND FRAMEWORK (Weightage 19%)
Sl.No Name of the Topic Process verb Types of thinking
8. 8
1
BI Definitions & Concepts- BI Framework, Data
Warehousing concepts and its role in BI
Define, Explain,
Discuss,
Describe
Understanding
CO2
2
BI Infrastructure Components, BI Process , BI
Technology
Classify,
Compare, Build,
Define, Develop,
Explain, Relate,
Utilize
Applying
CO2
3
BI Roles & Responsibilities , Business
Applications of BI
Analyze,
Examine,
Classify
Analyzing
CO2
R U Ap A E C Total
Type of thinking in Nos 0 1 1 1 0 0 3
Weightage,% 0 5.8 5.8 5.8 0 0 17.4
UNIT III BASICS OF DATA INTEGRATION (EXTRACTION TRANSFORMATION LOADING
(Weightage 21%)
Sl.No Name of the Topic Process verb Types of thinking
1
Concepts Of Data Integration, Needs And
Advantages Of Using Data Integration,
Introduction To Common Data Integration
Approaches, Meta Data - Types And Sources
Classify,
Compare, Build,
Define, Develop,
Explain, Relate,
Utilize
Applying
CO3
2
Introduction To Data Quality, Data Profiling
Concepts And Applications, Introduction To ETL
Using Pentaho Data Integration (Formerly Kettle)
Analyze,
Examine,
Classify, Infer,
Expose,
Categorize
Analyzing
CO3
R U Ap A E C Total
Type of thinking in Nos 0 0 1 1 0 0 2
Weightage,% 0 0 11.4 11.4 0 0 22.8%
UNIT IV INTRODUCTION TO MULTI-DIMENSIONAL DATA MODELING (Weightage 17%)
Sl.No Name of the Topic Process verb Types of thinking
1
Introduction To Data And Dimension Modeling,
Multidimensional Data Model,
Define, Explain,
Discuss,
Describe
Understanding
CO4
2
ER Modeling Vs. Multi-Dimensional Modeling, ,
Concepts Of Dimensions, Facts, Cubes, Attribute,
Hierarchies, Star And Snowflake Schema
Classify,
Compare, Build,
Define, Develop,
Explain, Relate,
Utilize
Applying
CO4
3
Introduction To Business Metrics And Kpis,
Creating Cubes Using Microsoft Excel Analyze,
Examine,
Classify
Analyzing
CO4
9. 9
R U Ap A E C Total
Type of thinking in Nos 0 1 1 1 0 0 3
Weightage,% 0 6.9 6.9 6.9 0 0 20.7%
UNIT V UNIT V - BASICS OF ENTERPRISE REPORTING
(Weightage 17%)
Sl.No Name of the Topic Process verb Types of thinking
1 A Typical Enterprise, Malcolm Baldrige - Quality
Performance Framework, Balanced Scorecard,
Enterprise Dashboard,
Classify,
Compare, Build,
Define, Develop,
Explain, Relate,
Utilize
Applying
CO5
2 Balanced Scorecard Vs. Enterprise Dashboard,
Enterprise Reporting Using MS Access / MS Excel,
Best Practices In The Design Of Enterprise
Dashboards
Analyze,
Examine, Classify
Analyzing
CO5
R U Ap A E C Total
Type of thinking in Nos 0 0 1 1 0 0 2
Weightage,% 0 0 9.8 9.8 0 0 19.6%
R U AP A E C TOTAL
UNIT 1 0 6.53 6.53 6.53 0 0 19.6%
UNIT 2 0 5.8 5.8 5.8 0 0 17.4%
UNIT 3 0 0 11.4 11.4 0 0 22.8%
UNIT 4 0 6.9 6.9 6.9 0 0 20.7%
UNIT 5 0 0 9.8 9.8 0 0 19.6%
TOTAL 0 19.23 40.43 40.43 0 0 100 %
Lower Order Thinking 59.6 %
Higher Order Thinking 40.4%
20. Mapping course outcome with Bloom’s Taxonomy LOT and HOT:
R U Ap A E C
CO1
CO2
CO3
CO4
CO5
10. 10
21. Mapping Course Outcome (CO) with Program Outcomes (PO) and Program
Specific Outcomes (PSO):
Program Outcomes Descriptions
PO1 Engineering knowledge Apply the knowledge of mathematics, science, engineering
fundamentals, and an engineering specialization to the
solution of complex engineering problems.
PO2 Problem analysis Identify, formulate, research literature, and analyze
complex engineering problems reaching substantiated
conclusions using first principles of mathematics, natural
Sciences, and engineering sciences.
PO3 Design/development of
solutions
Design solutions for complex engineering problems and
design system components or processes that meet the
specified needs with appropriate consideration for the
public health and safety, and the cultural, societal, and
environmental considerations.
PO4 Conduct investigations of
complex problems
Use research-based knowledge and research methods
including design of experiments, analysis and interpretation
of data, and synthesis of the information to provide valid
conclusions.
PO5 Modern tool usage Create, select, and apply appropriate techniques, resources,
and modern engineering and IT tools including prediction
and modeling to complex engineering activities with an
understanding of the limitations.
PO6 The engineer and society Apply reasoning informed by the contextual knowledge to
assess societal, health, safety, legal and cultural issues and
the consequent responsibilities relevant to the professional
engineering practice
PO7 Environment and
sustainability
Understand the impact of the professional engineering
solutions in societal and environmental contexts, and
demonstrate the knowledge of, and need for sustainable
development.
PO8 Ethics Apply ethical principles and commit to professional ethics
and responsibilities and norms of the engineering practice.
PO9 Individual and team work Function effectively as an individual, and as a member or
leader in diverse teams, and in multidisciplinary settings.
PO10 Communication Communicate effectively on complex engineering activities
with the engineering community and with society at large,
such as, being able to comprehend and write effective
reports and design documentation, make effective
presentations, and give and receive clear instructions.
PO11 Project management and
finance
Demonstrate knowledge and understanding of the
engineering and management principles and apply these to
one’s own work, as a member and leader in a team, to
manage projects and in multidisciplinary environments
PO12 Life-long learning Recognize the need for, and have the preparation and ability
11. 11
to engage in independent and life-long learning in the
broadest context of technological change.
PSO1 An ability to apply, design and develop principles of software engineering, networking
and database concepts for computer-based systems in solving engineering problems
PSO2 An ability to understand, design and code engineering problems using programming
skills.
PO&PSO PO
1
PO
2
PO
3
PO
4
PO
5
PO
6
PO
7
PO
8
PO
9
PO
10
PO
11
PO
12
PSO
1
PSO
2
CO1 3 2 2 3 2
CO2 3 2 2 3 2
CO3 1 2 2 3
CO4 3 2 2 3
CO5 1 3 3 3 2 3
3 High 2 Moderate 1 Low
22. Mapping with Programme Educational Objectives (PEOs):
Programme Educational Objectives:
1. To acquire knowledge in the latest technologies and innovations and an ability to
identify, analyze and solve problems in computer engineering.
2. To be capable of modeling, designing, implementing and verifying a computing system
to meet specified requirements for the benefit of society.
Course PEO1 PEO2
19CS6301 Business
Intelligence- Data
Warehousing and
Analytics
Moderate level High level
3 High level 2 Moderate level 1 Low level
23. Course assessment: (Direct Assessment Method)
Internal test: 15 Marks
Objective To Identify What Students Have Learned and also to identify students strength
and weakness
Product Answer scripts
Frequency Monthly
Format Part –A 6 x 2 = 12 Marks
Part – B 2 x 14 = 28 Marks
Part –C 1 x 10= 10 Marks
Total marks= 50
Duration : 1 Hour and 30 Minutes
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Evaluation Based on answer given in the scripts
criteria Pass mark – 50%
Minimum pass percentage: 50%
If not, remedial action will be taken.
Assignment: 5 marks
Objective To enhance students' understanding of a particular reading
Product Hand written assignment/tutorial sheets
Frequency After completing one unit
Format Important questions from each units
Evaluation Based on rubrics
Criteria No. of assignments: 3
Submit on or before the due date
Attendance: 5 marks
Objective To make all students to attend the class throughout the course
Product Record of class work
Frequency All working days
Format Record of class work format
Evaluation Based on attendance earned by the students
Criteria Marks will be awarded according to attendance percentage of students.
91 and above 5
86 – 90 4
81 – 85 3
75 – 80 2
Less than 75 0
End semester exam: 75 marks
Objective To assess the each student’s knowledge of the course
Product Result analysis
Frequency Every Semester
Format Part –A 10 x 2= 20 marks
Part –B 5 x 14= 70 marks
Part – C 1 x 10 = 10 Marks
Total marks= 100
Duration : 3 Hours
Evaluation Based on answer given in the scripts
Criteria Minimum pass percentage: 50%
If not, remedial action will be taken.
24. Course assessment: (Indirect Assessment Method)
Course Exit Survey: Course Exit Survey consists of few critical questions that evaluate
the level of students’ satisfaction level with curriculum and course being taught.
13. 13
Prepared by, Checked by,
Course Coordinator Head of the Department
(Name and Dept.)
Approved by,
Dean (Academics) PRINCIPAL