We found 115 results that contain "data"
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Data communications
Data communications refers to the transmission of this digital data between two or more computers and a computer network or data network is a telecommunications network that allows computers to exchange data. The physical connection between networked computing devices is established using either cable media or wireless media. The best-known computer network is the Internet.
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Data communications
Data communications refers to the transmission of this digital data between two or more computers and a computer network or data network is a telecommunications network that allows computers to exchange data. The physical connection between networked computing devices is established using either cable media or wireless media. The best-known computer network is the Internet.
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Data Science in Digital Marketing
The digital revolution is now sweeping small towns and villages perhaps driven by increased accessibility at affordable data costs? The whole world is in our hands and the increase in the usage of digital in rural India, where more than two-thirds of active internet users are present is a great opportunity and a win-win situation for both. They are trying to meet their entertainment and communication needs and we can help them provide the best of service in addition to winning their hearts and pocket.
ASSESSING LEARNING
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Database Compression based on Flash
With Airtable, you get a database that works with any team and any use case. Manage your processes, organize your information, create and customize the right tool. Secured. 200+ Templates. iOS & Android Apps. Free & Pro Plans. Trusted by 200K Teams. Project Tracker.
NAVIGATING CONTEXT
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Introduction of DBMS (Database Management System)
A Database Management System (DBMS) is a software solution designed to efficiently manage, organize, and retrieve data in a structured manner. It serves as a critical component in modern computing, enabling organizations to store, manipulate, and secure their data effectively. From small applications to enterprise systems, DBMS plays a vital role in supporting data-driven decision-making and operational efficiency.
In this article, we will explain the key concepts, benefits, and types of Database Management Systems (DBMS). We’ll also cover how DBMS solutions work, why they’re important for modern applications, and what features they offer to ensure data integrity, security, and efficient retrieval.
What is a DBMS?
A DBMS is a system that allows users to create, modify, and query databases while ensuring data integrity, security, and efficient data access. Unlike traditional file systems, DBMS minimizes data redundancy, prevents inconsistencies, and simplifies data management with features like concurrent access and backup mechanisms. It organizes data into tables, views, schemas, and reports, providing a structured approach to data management.
Example:
A university database can store and manage student information, faculty records, and administrative data, allowing seamless retrieval, insertion, and deletion of information as required.
In this article, we will explain the key concepts, benefits, and types of Database Management Systems (DBMS). We’ll also cover how DBMS solutions work, why they’re important for modern applications, and what features they offer to ensure data integrity, security, and efficient retrieval.
What is a DBMS?
A DBMS is a system that allows users to create, modify, and query databases while ensuring data integrity, security, and efficient data access. Unlike traditional file systems, DBMS minimizes data redundancy, prevents inconsistencies, and simplifies data management with features like concurrent access and backup mechanisms. It organizes data into tables, views, schemas, and reports, providing a structured approach to data management.
Example:
A university database can store and manage student information, faculty records, and administrative data, allowing seamless retrieval, insertion, and deletion of information as required.
JUSTICE AND BELONGING
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Neural networks, or artificial neural networks
Neural networks, or artificial neural networks (ANNs), are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. The “deep” in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm or a deep neural network. A neural network that only has two or three layers is just a basic neural network.
ASSESSING LEARNING
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Playlist: Importance of Software Testing ?
It’s common for many startups to skip testing. They might say that their budget is the reason why they overlook such an important step. They think it would lead to no major consequences. But to make a strong and positive first impression, it needs to be top-notch. And for that, testing the product for bugs is a must.
To really understand why software testing is important, we need to correlate it with real world examples, which has caused serious issues in the past, a few examples include:
In October 2014, Flipkart an e-commerce in India company had an offer called the “Big Billion Sale.” When it was launched it had a lot of traffic and as a result, its website couldn’t handle the enormous load of traffic leading to the website downtime, cancellation of orders etc. The reputation of the organization was badly impacted by this issue.
In 2015, the Royal Bank of Scotland, due to a bug, couldn’t process about 600,000 payments. Because of this, they were fined 66 million pounds
Yahoo in September 2016, had a major data breach where 500 million users’ credentials got compromised.
Recently, Okta, an American authentication firm, had a digital breach due to a software bug that may have affected their user’s details. This has also affected the reputation of Okta.
Similarly, established organizations need to maintain their client base and their impression. So they have to ensure the delivery of flawless products to the end-user. Let’s take a look at some points and see why software testing is vital to good software development.
To really understand why software testing is important, we need to correlate it with real world examples, which has caused serious issues in the past, a few examples include:
In October 2014, Flipkart an e-commerce in India company had an offer called the “Big Billion Sale.” When it was launched it had a lot of traffic and as a result, its website couldn’t handle the enormous load of traffic leading to the website downtime, cancellation of orders etc. The reputation of the organization was badly impacted by this issue.
In 2015, the Royal Bank of Scotland, due to a bug, couldn’t process about 600,000 payments. Because of this, they were fined 66 million pounds
Yahoo in September 2016, had a major data breach where 500 million users’ credentials got compromised.
Recently, Okta, an American authentication firm, had a digital breach due to a software bug that may have affected their user’s details. This has also affected the reputation of Okta.
Similarly, established organizations need to maintain their client base and their impression. So they have to ensure the delivery of flawless products to the end-user. Let’s take a look at some points and see why software testing is vital to good software development.
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Hierarchical Inheritance in C++
Hierarchical Inheritance in C++ is useful in the cases where a hierarchy has to be maintained. Most of the schools and colleges maintain the data of their students in hierarchical form. For example, a college has to maintain the data of the engineering students and segregate them according to their branches such as the IT branch, mechanical branch, and so on. You can achieve such a scenario can by Hierarchical Inheritance in C++ easily. Similar is the case with the companies where they have to maintain the data of their employees according to the different departments.
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Data availability
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. [9] That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. [9] That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. [9] That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Posted by: Chathuri Super admin..
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Data communications
Data communications refers to the transmission of this digital data between two or more computers and a computer network or data network is a telecommunications network that allows computers to exchange data. The physical connection between networked computing devices is established using either cable media or wireless media. The best-known computer network is the Internet.
Posted by: Super Admin
Posted on: #iteachmsu

What Is Big Data? and How Big Data Works?
Big data:Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered (known as the "three v's" of big data).
Big data is a great quantity of diverse information that arrives in increasing volumes and with ever-higher velocity.
Big data can be structured (often numeric, easily formatted and stored) or unstructured (more free-form, less quantifiable).
Nearly every department in a company can utilize findings from big data analysis, but handling its clutter and noise can pose problems.
Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins.
Big data is most often stored in computer databases and is analyzed using software specifically designed to handle large, complex data sets.
How Big Data Works
Big data can be categorized as unstructured or structured. Structured data consists of information already managed by the organization in databases and spreadsheets; it is frequently numeric in nature. Unstructured data is information that is unorganized and does not fall into a predetermined model or format. It includes data gathered from social media sources, which help institutions gather information on customer needs.
Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins. The presence of sensors and other inputs in smart devices allows for data to be gathered across a broad spectrum of situations and circumstances.
Big data is a great quantity of diverse information that arrives in increasing volumes and with ever-higher velocity.
Big data can be structured (often numeric, easily formatted and stored) or unstructured (more free-form, less quantifiable).
Nearly every department in a company can utilize findings from big data analysis, but handling its clutter and noise can pose problems.
Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins.
Big data is most often stored in computer databases and is analyzed using software specifically designed to handle large, complex data sets.
How Big Data Works
Big data can be categorized as unstructured or structured. Structured data consists of information already managed by the organization in databases and spreadsheets; it is frequently numeric in nature. Unstructured data is information that is unorganized and does not fall into a predetermined model or format. It includes data gathered from social media sources, which help institutions gather information on customer needs.
Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins. The presence of sensors and other inputs in smart devices allows for data to be gathered across a broad spectrum of situations and circumstances.
Authored by: Rupali
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Business Intelligence and Data Science
Business Intelligence and Data Science are two of the most recurring terms in the digital era. While both of them involve the use of data, they are totally different from one another. Data Science is the bigger pool containing greater information, BI can be thought of as a part of the bigger picture.
What is Business Intelligence?
Business Intelligence is a process of collecting, integrating, analyzing, and presenting the data. With Business Intelligence, executives and managers can have a better understanding of decision-making. This process is carried out through software services and tools.
Using Business Intelligence, organizations are able to several strategic and operational business decisions. Furthermore, BI tools are used for the analysis and creation of reports. They are also used for producing graphs, dashboards, summaries, and charts to help the business executives to make better decisions.
What is Business Intelligence?
Business Intelligence is a process of collecting, integrating, analyzing, and presenting the data. With Business Intelligence, executives and managers can have a better understanding of decision-making. This process is carried out through software services and tools.
Using Business Intelligence, organizations are able to several strategic and operational business decisions. Furthermore, BI tools are used for the analysis and creation of reports. They are also used for producing graphs, dashboards, summaries, and charts to help the business executives to make better decisions.
Authored by: Rupali
Assessing Learning
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Develop and actively communicate your course-level generative AI policy
1. Consider how AI technology might compel you to revise your course assignments, quizzes, and tests to avoid encouraging unethical or dishonest use of generative AI. 2. Develop and integrate a generative AI policy throughout the course resources:
Provide clear definitions, expectations, and repercussions of what will happen if students violate the policy.
Explain the standards of academic integrity in the course, especially as related to use of AI technologies, and review the Integrity of Scholarship and Grades Policy.
Be clear about what types of AI are acceptable and what versions of the technology students can use or not use.
Put this policy into D2L and any assignment instructions consistently.
3. Discuss these expectations when talking about course policies at the beginning of the course and remind students about them as you discuss course assignments:
Take time to explain to students the pros and cons of generative AI technologies relative to your course.
Explain the development of your policy and make clear the values, ethics, and philosophies underpinning its development.
Explain the repercussions of not following the course policy and submit an Academic Dishonesty Report if needed.
4. If you want to integrate AI in the classroom as an allowed or required resource:
Consult with MSU IT guidance about recommendations for use and adoption of generative AI technology, including guidelines for keeping you and your data safe.
Determine if MSU already has access to the tools you desire for free, and if not available through MSU, consider the cost and availability of the resources you will allow or require, and go through MSU's procurement process.
If you want to require students to use an AI technology that comes with a cost, put the resource into the scheduling system as you would a textbook, so students know that is an anticipated cost to them.
Provide clear definitions, expectations, and repercussions of what will happen if students violate the policy.
Explain the standards of academic integrity in the course, especially as related to use of AI technologies, and review the Integrity of Scholarship and Grades Policy.
Be clear about what types of AI are acceptable and what versions of the technology students can use or not use.
Put this policy into D2L and any assignment instructions consistently.
3. Discuss these expectations when talking about course policies at the beginning of the course and remind students about them as you discuss course assignments:
Take time to explain to students the pros and cons of generative AI technologies relative to your course.
Explain the development of your policy and make clear the values, ethics, and philosophies underpinning its development.
Explain the repercussions of not following the course policy and submit an Academic Dishonesty Report if needed.
4. If you want to integrate AI in the classroom as an allowed or required resource:
Consult with MSU IT guidance about recommendations for use and adoption of generative AI technology, including guidelines for keeping you and your data safe.
Determine if MSU already has access to the tools you desire for free, and if not available through MSU, consider the cost and availability of the resources you will allow or require, and go through MSU's procurement process.
If you want to require students to use an AI technology that comes with a cost, put the resource into the scheduling system as you would a textbook, so students know that is an anticipated cost to them.
Authored by: Super admin user
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Development Tools
MSU IT offers a number of valuable tools and services that can help you create an experience that facilitates student success regardless of bandwidth, time zones, or class size. To make an appointment with an instructional technologist, fill out the appointment form located at https://tech.msu.edu/service-catalog/teaching/instructional-design-development/ or e-mail the MSU IT Service Desk at ithelp@msu.edu and request a consultation with Instructional Technology and Development. If you prefer the phone, you can also contact them at (517)432-6200.
Authored by: Berry, R. W. (2009). Meeting the challenges of teaching large online classes: Shifting to a learn100
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My Class Size Exceeds the Zoom License Limits – What Now?
Need more help?
Hopefully some of the ideas shared here have helped you think about some alternatives to pursue should Zoom not be an option for your courses. The instructional technology and development team in MSU IT is happy to consult with you on how you can leverage academic technologies to make your large course experience feel smaller. We can help you think about how to live without Zoom and embrace the asynchronous teaching life if you choose.
Hopefully some of the ideas shared here have helped you think about some alternatives to pursue should Zoom not be an option for your courses. The instructional technology and development team in MSU IT is happy to consult with you on how you can leverage academic technologies to make your large course experience feel smaller. We can help you think about how to live without Zoom and embrace the asynchronous teaching life if you choose.
Authored by: Berry, R. W. (2009). Meeting the challenges of teaching large online classes: Shifting to a learner-focus. MERLOT Journal of Online Learning and Teaching, Boettcher, J. (2011). Ten best practices for teaching online. Quick Guide for New Online faculty.255
Disciplinary Content
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Article: The Most Important - Excellent Nutrition
Good food is a key to good development and a good mood as well. A healthy body nurtures a healthy mind. Fortunately, my daughter is not a picky eater but there are days when she gives me a hard time.
A balanced nutritious diet is essential. Vegetables, seasonal fruits, eggs, pulses are all building blocks for a proper diet. Milk with #Junior Horlicks is also a great choice as it includes nutrients that support brain development, such as choline, iron, and iodine. It also contains nutrients that support physical growth, such as calcium, Vitamin D, Vitamin K, and proteins, and also nutrients to support healthy immune function such as Vitamin E, A, Selenium & Copper. You can design a wholesome diet with roti, rice, whole-grain bread, wheat pasta, curd, cheese and maybe a scoop of ice cream and some sweets once in a while.
A balanced nutritious diet is essential. Vegetables, seasonal fruits, eggs, pulses are all building blocks for a proper diet. Milk with #Junior Horlicks is also a great choice as it includes nutrients that support brain development, such as choline, iron, and iodine. It also contains nutrients that support physical growth, such as calcium, Vitamin D, Vitamin K, and proteins, and also nutrients to support healthy immune function such as Vitamin E, A, Selenium & Copper. You can design a wholesome diet with roti, rice, whole-grain bread, wheat pasta, curd, cheese and maybe a scoop of ice cream and some sweets once in a while.
Posted by: Super Admin
Assessing Learning
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Big data is more than high-volume, high-velocity data. Learn what big data is, why it matters and how it can help you make better decisions every day.
Posted by: E3 PGA
Disciplinary Content
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Data Science::
Data Science is a comprehensive process that involves preprocessing, analysis, visualization, and prediction. On the other hand, AI is the implementation of a predictive model to forecast future events.
A Data Scientist, on the other hand, helps the company and businesses to make careful data-driven decisions. A Data Scientist is responsible for extracting data using SQL and NoSQL queries, cleaning various anomalies in the data, analyzing the patterns in data, and applying predictive models.
Data Science is a comprehensive process that involves preprocessing, analysis, visualization, and prediction. On the other hand, AI is the implementation of a predictive model to forecast future events.
A Data Scientist, on the other hand, helps the company and businesses to make careful data-driven decisions. A Data Scientist is responsible for extracting data using SQL and NoSQL queries, cleaning various anomalies in the data, analyzing the patterns in data, and applying predictive models.
Posted by: Rupali Jagtap
Assessing Learning
Posted on: #iteachmsu
Data Science is a process of extracting, manipulating, visualizing, maintaining data as well as generating predictions.
A Data Scientist is supposed to have knowledge of various data operations as well as machine learning algorithms. Using Data Science, industries are able to extract insights and forecast their performance.
A Data Scientist is supposed to have knowledge of various data operations as well as machine learning algorithms. Using Data Science, industries are able to extract insights and forecast their performance.
Posted by: Chathuri Super admin..
Assessing Learning
Posted on: #iteachmsu
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. Testing of these datasets involves various tools, techniques and frameworks to process. Big data relates to data creation, storage, retrieval and analysis that is remarkable in terms of volume, variety, and velocity. You can learn more about Big Data, Hadoop and Mapreduce here In this tutorial we will learn, Testing Big Data application is more a verification of its data processing rather than testing the individual features of the software product. When it comes to Big data testing, performance and functional testing are the key. In Big data testing QA engineers verify the successful processing of terabytes of data using commodity cluster and other supportive components. It demands a high level of testing skills as the processing is very fast. Processing may be of three types Along with this, data quality is also an important factor in big data testing. Before testing the application, it is necessary to check the quality of data and should be considered as a part of database testing. It involves checking various characteristics like conformity, accuracy, duplication, consistency, validity, data completeness, etc.
Posted by: E3 PGA
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Data communications refers to the transmission of this digital data between two or more computers and a computer network or data network is a telecommunications network that allows computers to exchange data. The physical connection between networked computing devices is established using either cable media or wireless media. The best-known computer network is the Internet.
Posted by: Super Admin
Posted on: #iteachmsu
The tools of business intelligence are also limited to the analysis of management information and curation of business strategies. However, the tools of a data scientist involve complex algorithmic models, data processing, and even big data tools. While BI focuses on generating reports based on the internal structured data, Data Science focuses on generating insights out of the data.
Posted by: Rupali Jagtap
Assessing Learning
Posted on: #iteachmsu
Machine-generated data is information automatically generated by a computer process, application, or other mechanism without the active intervention of a human. While the term dates back over fifty years,[1] there is some current indecision as to the scope of the term. Monash Research's Curt Monash defines it as "data that was produced entirely by machines OR data that is more about observing humans than recording their choices."[2] Meanwhile, Daniel Abadi, CS Professor at Yale, proposes a narrower definition, "Machine-generated data is data that is generated as a result of a decision of an independent computational agent or a measurement of an event that is not caused by a human action."[3] Regardless of definition differences, both exclude data manually entered by a person.[4] Machine-generated data crosses all industry sectors. Often and increasingly, humans are unaware their actions are generating the data.[
Posted by: Chathuri Super admin..
Assessing Learning
Posted on: #iteachmsu
Data Science is a field that makes use of scientific methods and algorithms in order to extract knowledge and discover insights from data (structured on unstructured). Data Analytics is the process of using specialized systems and software to inspect information in datasets in order to derive conclusions
Posted by: Rupali Jagtap
Assessing Learning
Host: MSU Libraries
Best Practices in Data Visualization
Learn general best practices for creating data visualizations. This workshop will describe the overarching goals of data visualization and provide criteria for evaluating the effectiveness of a visualization. This workshop will also offer tool suggestions for beginners exploring data visualization.
Navigating Context
Host: MSU Libraries
Data Management Plans: Yes, you need one and here is how to create them
An overview of why data management plans are important and often required, and how to develop one. We will look at the items commonly included in all data management plans regardless of agency/institutional requirements, some examples of specific funding agency templates and review what resources are available to help you draft your plan.
Navigating Context
Host: MSU Libraries
Research Data Management: Best Practices for organizing and managing your data
Why is research data management so important? This workshop will help you plan for organizing and managing your data from the outset of your project.
We will look at some basic best practices for:
organizing your data
cleaning/prepping/working with your data
working with multiple data files
storage solutions
long-term archiving and making your data accessible to other researchers
Navigating Context
Host: MSU Libraries
3D Terrain Elevation Models for 3D Printing (Online)
Learn how to produce a 3D model of terrain elevation for printing on a 3D printer. We will learn about 3D models for 3D printing, digital elevation models (DEMs), where to find DEM data to create our printable export, and then use a plug in DEMto3D in the open source software QGIS to create a model. If time allows, basic tools for 3D model editing in Meshmixer or slicing software will be demonstrated.
Navigating Context
Host: MSU Libraries
Intro to 360 Cameras, 3D Scanners, & Other Digitization Equipment @ DSL: Drop-in Session
Immersive data is everywhere. It's in every digital asset you see in a video game, a YouTube 360 video and the panoramic pictures you take with your mobile device. Drop in to learn about how 360 cameras, 3D scanners and our digitization equipment is used to create these new experiences and apply them to your research, assignments and presentations.
Navigating Context
Host: MSU Libraries
Learn QGIS: Making a color shaded map in QGIS (Online)
Learn the basics of QGIS, the free open-source geospatial software—this workshop will demonstrate how to make a choropleth (color shaded) map and place graduated symbols representing data on it, load shape-files and .csv table files into QGIS, join data to spatial information and edit features. No experience with QGIS or Geographic Information Systems is required.
Navigating Context
Host: MSU Libraries
Zotero Workshop (Online)
An introduction to the free open source citation management program Zotero. In this workshop, participants will learn how to:
Download references from MSU's article databases and websites
Format citations and bibliographies in a Word document
Create groups and share references with other users
Registration for this event is required.
You will receive a link to join a Zoom meeting before the workshop. Please install the Zotero software and Zotero browser connector on your computer before the session begins. More information is available from https://libguides.lib.msu.edu/zotero/setup.
Questions or need more information? Contact the MSU Libraries Zotero training team at lib.dl.zotero@msu.edu.
To schedule a separate session for your class or research group, please contact the Zotero team at lib.dl.zotero@msu.edu.
Navigating Context
Host: #iteachmsu

Computer Fundamentals
Computer is an advanced electronic device that takes raw data as an input from the user and processes it under the control of a set of instructions (called program), produces a result (output), and saves it for future use. This tutorial explains the foundational concepts of computer hardware, software, operating systems, peripherals, etc. along with how to get the most value and impact from computer technology.