An introduction to Jupyter notebooks and the Noteable serviceJisc
A presentation at Connect More in Scotland, 4 June 2019.
Speaker: James Slack, e-learning officer for computational notebooks DLAM, University of Edinburgh.
Over the past year, the University of Edinburgh has been developing and piloting the Noteable service to help supporting programming and computational teaching.
The Noteable services provide cloud access to Jupyter notebooks; live editable documents that allow you to run code whilst also containing text, data tables and other rich media items such as images and videos. Jupyter allows students to quickly get hands-on with programming content without having to brave an intimidating IDE (integrated development environment) or grapple with the terminal.
This session will give an overview of what Jupyter notebooks are and why they are becoming popular for introductory programming courses. There'll be a discussion around how Jupyter has been adopted at the University of Edinburgh and how the Noteable service has been developed to support computational education.
Introduction to IPython & Jupyter NotebooksEueung Mulyana
The document discusses IPython and the Jupyter Notebook. IPython is an interactive shell for Python that provides features like command history, tab completion, object introspection, and support for parallel computing. It has three main components: an enhanced interactive Python shell, a two-process communication model that allows clients to connect to a computation kernel, and architecture for interactive parallel computing. The Jupyter Notebook provides a browser-based notebook interface that allows code, text, plots and other media to be combined. IPython QtConsole provides a graphical interface for IPython with features like inline figures and multiline editing.
Jupyter Notebooks allow users to write and run code interactively in the browser by combining code and rich text in a single document. They can be run locally on localhost:8888 after installing Anaconda, a Python distribution containing popular scientific libraries, or Jupyter, which is launched by typing $ jupyter notebook in a terminal. Jupyter Notebooks provide code, text, and some terminal functionality in an interactive browser-based environment for data science and scientific computing.
This document discusses Jupyter, an open-source tool for interactive data science and scientific computing. Jupyter allows for interactive exploration, development, and communication through code, equations, visualizations and narrative text. It supports over 50 programming languages and has found widespread adoption in academia and industry for individual and collaborative work across the entire workflow of a scientific idea from data collection to publication. The document outlines Jupyter's history and architecture, ecosystem of related projects, and future development plans to enhance collaboration and software engineering capabilities.
This document provides an introduction to data science with Python. It discusses key concepts in data science including visualization, statistics, machine learning, deep learning, and big data. Various Python packages are introduced for working with data, including Jupyter, NumPy, SciPy, Matplotlib, Pandas, Scikit-learn and others. The document outlines the main steps in a data science analysis process, including defining assumptions, validating assumptions with data, and iterating. Specific techniques are covered like preprocessing, dimensionality reduction, statistical modeling, and machine learning modeling. The document emphasizes an iterative approach to learning through applying concepts to problems and data.
This document discusses data visualization tools in Python. It introduces Matplotlib as the first and still standard Python visualization tool. It also covers Seaborn which builds on Matplotlib, Bokeh for interactive visualizations, HoloViews as a higher-level wrapper for Bokeh, and Datashader for big data visualization. Additional tools discussed include Folium for maps, and yt for volumetric data visualization. The document concludes that Python is well-suited for data science and visualization with many options available.
This document provides an overview of Continuum Analytics and Python for data science. It discusses how Continuum created two organizations, Anaconda and NumFOCUS, to support open source Python data science software. It then describes Continuum's Anaconda distribution, which brings together 200+ open source packages like NumPy, SciPy, Pandas, Scikit-learn, and Jupyter that are used for data science workflows involving data loading, analysis, modeling, and visualization. The document outlines how Continuum helps accelerate adoption of data science through Anaconda and provides examples of industries using Python for data science.
This document provides an overview of data visualization in Python. It discusses popular Python libraries and modules for visualization like Matplotlib, Seaborn, Pandas, NumPy, Plotly, and Bokeh. It also covers different types of visualization plots like bar charts, line graphs, pie charts, scatter plots, histograms and how to create them in Python using the mentioned libraries. The document is divided into sections on visualization libraries, version overview of updates to plots, and examples of various plot types created in Python.
An introduction to Jupyter notebooks and the Noteable serviceJisc
A presentation at Connect More in Scotland, 4 June 2019.
Speaker: James Slack, e-learning officer for computational notebooks DLAM, University of Edinburgh.
Over the past year, the University of Edinburgh has been developing and piloting the Noteable service to help supporting programming and computational teaching.
The Noteable services provide cloud access to Jupyter notebooks; live editable documents that allow you to run code whilst also containing text, data tables and other rich media items such as images and videos. Jupyter allows students to quickly get hands-on with programming content without having to brave an intimidating IDE (integrated development environment) or grapple with the terminal.
This session will give an overview of what Jupyter notebooks are and why they are becoming popular for introductory programming courses. There'll be a discussion around how Jupyter has been adopted at the University of Edinburgh and how the Noteable service has been developed to support computational education.
Introduction to IPython & Jupyter NotebooksEueung Mulyana
The document discusses IPython and the Jupyter Notebook. IPython is an interactive shell for Python that provides features like command history, tab completion, object introspection, and support for parallel computing. It has three main components: an enhanced interactive Python shell, a two-process communication model that allows clients to connect to a computation kernel, and architecture for interactive parallel computing. The Jupyter Notebook provides a browser-based notebook interface that allows code, text, plots and other media to be combined. IPython QtConsole provides a graphical interface for IPython with features like inline figures and multiline editing.
Jupyter Notebooks allow users to write and run code interactively in the browser by combining code and rich text in a single document. They can be run locally on localhost:8888 after installing Anaconda, a Python distribution containing popular scientific libraries, or Jupyter, which is launched by typing $ jupyter notebook in a terminal. Jupyter Notebooks provide code, text, and some terminal functionality in an interactive browser-based environment for data science and scientific computing.
This document discusses Jupyter, an open-source tool for interactive data science and scientific computing. Jupyter allows for interactive exploration, development, and communication through code, equations, visualizations and narrative text. It supports over 50 programming languages and has found widespread adoption in academia and industry for individual and collaborative work across the entire workflow of a scientific idea from data collection to publication. The document outlines Jupyter's history and architecture, ecosystem of related projects, and future development plans to enhance collaboration and software engineering capabilities.
This document provides an introduction to data science with Python. It discusses key concepts in data science including visualization, statistics, machine learning, deep learning, and big data. Various Python packages are introduced for working with data, including Jupyter, NumPy, SciPy, Matplotlib, Pandas, Scikit-learn and others. The document outlines the main steps in a data science analysis process, including defining assumptions, validating assumptions with data, and iterating. Specific techniques are covered like preprocessing, dimensionality reduction, statistical modeling, and machine learning modeling. The document emphasizes an iterative approach to learning through applying concepts to problems and data.
This document discusses data visualization tools in Python. It introduces Matplotlib as the first and still standard Python visualization tool. It also covers Seaborn which builds on Matplotlib, Bokeh for interactive visualizations, HoloViews as a higher-level wrapper for Bokeh, and Datashader for big data visualization. Additional tools discussed include Folium for maps, and yt for volumetric data visualization. The document concludes that Python is well-suited for data science and visualization with many options available.
This document provides an overview of Continuum Analytics and Python for data science. It discusses how Continuum created two organizations, Anaconda and NumFOCUS, to support open source Python data science software. It then describes Continuum's Anaconda distribution, which brings together 200+ open source packages like NumPy, SciPy, Pandas, Scikit-learn, and Jupyter that are used for data science workflows involving data loading, analysis, modeling, and visualization. The document outlines how Continuum helps accelerate adoption of data science through Anaconda and provides examples of industries using Python for data science.
This document provides an overview of data visualization in Python. It discusses popular Python libraries and modules for visualization like Matplotlib, Seaborn, Pandas, NumPy, Plotly, and Bokeh. It also covers different types of visualization plots like bar charts, line graphs, pie charts, scatter plots, histograms and how to create them in Python using the mentioned libraries. The document is divided into sections on visualization libraries, version overview of updates to plots, and examples of various plot types created in Python.
Data Analysis and Visualization using PythonChariza Pladin
The document is a presentation about data analysis and visualization using Python libraries. It discusses how data is everywhere and growing exponentially, and introduces a 5-step process for data analysis and decision making. It emphasizes the importance of visualizing data to analyze patterns, discover insights, support stories, and teach others. The presentation then introduces Jupyter Notebook and highlights several Python libraries for data visualization, including matplotlib, seaborn, ggplot, Bokeh, pygal, plotly, and geoplotlib.
This Edureka Python Programming tutorial will help you learn python and understand the various basics of Python programming with examples in detail. Below are the topics covered in this tutorial:
1. Python Installation
2. Python Variables
3. Data types in Python
4. Operators in Python
5. Conditional Statements
6. Loops in Python
7. Functions in Python
8. Classes and Objects
pandas: Powerful data analysis tools for PythonWes McKinney
Wes McKinney introduced pandas, a Python data analysis library built on NumPy. Pandas provides data structures and tools for cleaning, manipulating, and working with relational and time-series data. Key features include DataFrame for 2D data, hierarchical indexing, merging and joining data, and grouping and aggregating data. Pandas is used heavily in financial applications and has over 1500 unit tests, ensuring stability and reliability. Future goals include better time series handling and integration with other Python data science packages.
This document provides an overview and objectives of a Python course for big data analytics. It discusses why Python is well-suited for big data tasks due to its libraries like PyDoop and SciPy. The course includes demonstrations of web scraping using Beautiful Soup, collecting tweets using APIs, and running word count on Hadoop using Pydoop. It also discusses how Python supports key aspects of data science like accessing, analyzing, and visualizing large datasets.
The document provides an introduction and overview of the Python programming language. It discusses that Python is an interpreted, object-oriented, high-level programming language that is easy to learn and read. It also covers Python features such as portability, extensive standard libraries, and support for functional, structured, and object-oriented programming. The document then discusses Python data types including numbers, strings, and various Python syntax elements before concluding with the history and evolution of the Python language through various versions.
The amount of data available to us is growing rapidly, but what is required to make useful conclusions out of it?
Outline
1. Different tactics to gather your data
2. Cleansing, scrubbing, correcting your data
3. Running analysis for your data
4. Bring your data to live with visualizations
5. Publishing your data for rest of us as linked open data
Python is a widely-used and powerful computer programming language that has helped system administrators manage computer networks and problem solve computer systems for decades. Python has also built some popular applications like BitTorrent, Blender, Calibre, Dropbox, and much more. Going further, the “Pi” in Raspberry Pi stands for Python, so learning Python will instill more confidence when working with Raspberry Pi projects. Python is usually the first programming language people learn primarily because it is easy to learn and provides a solid foundation to learn other computer programming languages. In this webinar,
• Learn what Python is and what it is capable of doing.
• Install Python’s IDE for Windows and work in the Python shell.
• Use calculations, variables, strings, lists, and if statements.
• Discover Python’s built-in functions and understand modules.
• Create simple programs to build on later.
The recording is available at https://youtu.be/ThcWmJFf-ho.
Python For Data Analysis | Python Pandas Tutorial | Learn Python | Python Tra...Edureka!
This Edureka Python Pandas tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) will help you learn the basics of Pandas. It also includes a use-case, where we will analyse the data containing the percentage of unemployed youth for every country between 2010-2014. Below are the topics covered in this tutorial:
1. What is Data Analysis?
2. What is Pandas?
3. Pandas Operations
4. Use-case
This document provides an introduction to data science. It discusses that data science uses computer science, statistics, machine learning, visualization, and human-computer interaction to collect, clean, analyze, visualize, and interact with data to create data products. It also describes the data science lifecycle as involving discovery, data preparation, model planning, model building, operationalizing models, and communicating results. Finally, it lists some common tools used in data science like Python, R, SQL, and Tableau.
This document discusses machine learning with Python. It provides an overview of Python, highlighting that it is easy to learn, has a vast community and documentation, and is versatile. It then defines machine learning and discusses popular Python libraries for machine learning like NumPy, SciPy, Matplotlib, Pandas, and OpenCV. It provides examples of operations that can be performed with OpenCV, like reading and manipulating images. Overall the document serves as an introduction to machine learning with Python and the main libraries used.
Data Analysis and Statistics in Python using pandas and statsmodelsWes McKinney
The document summarizes Wes McKinney's talk on statistical computing using Python. The talk introduces the scientific Python stack, including pandas for data structures and data analysis, and statsmodels for statistical modeling. It discusses the "research-production gap" in current statistical tools and how Python aims to bridge that gap. McKinney asserts that Python is the best solution for both research and production use of statistics and data analysis. He then demonstrates pandas and statsmodels functionality.
Python libraries presentation Contains all top 10 labraries information like numpy,tenslorflow,scikit-learn,Numpy,keras,PyToruch,LightGBM,Eli5,scipy,theano,pandas
1) The document introduces data science and its core disciplines, including statistics, machine learning, predictive modeling, and database management.
2) It explains that data science uses scientific methods and algorithms to extract knowledge and insights from both structured and unstructured data.
3) The roles of data scientists are discussed, noting that they have skills in programming, statistics, analytics, business analysis, and machine learning.
Python is an interpreted, object-oriented programming language created by Guido van Rossum in 1990. It has a clear, readable syntax and is designed to be highly extensible. Python code is often much shorter than equivalent code in other languages like C++ or Java due to features like indentation-based blocks and dynamic typing. It is used for web development, scientific computing, and more.
This document provides an overview of a data science course. It discusses topics like big data, data science components, use cases, Hadoop, R, and machine learning. The course objectives are to understand big data challenges, implement big data solutions, learn about data science components and prospects, analyze use cases using R and Hadoop, and understand machine learning concepts. The document outlines the topics that will be covered each day of the course including big data scenarios, introduction to data science, types of data scientists, and more.
Python is a popular programming language used in a variety of applications, including data analysis, web development, and artificial intelligence. Here's an introduction to the Basics of Python - A Beginners Guide! Whether you're new to programming or looking to brush up on your skills, this video covers the basics of Python programming language. From data types and operators to loops, functions and libraries, you'll get a solid foundation to start coding in Python.
Visit us: https://www.elewayte.com/
Pandas is a powerful Python library for data analysis and manipulation. It provides rich data structures for working with structured and time series data easily. Pandas allows for data cleaning, analysis, modeling, and visualization. It builds on NumPy and provides data frames for working with tabular data similarly to R's data frames, as well as time series functionality and tools for plotting, merging, grouping, and handling missing data.
A walk through the maze of understanding Data Visualization using several tools such as Python, R, Knime and Google Data Studio.
This workshop is hands-on and this set of presentations is designed to be an agenda to the workshop
The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.
WikiTouch is a personal wiki application for iPhone and iPod Touch users that allows them to easily create, organize, and share multimedia notes on the go. It helps users better manage their business and personal digital lives. Key features include the ability to access and edit notes from any device either online or offline, share notes and content collaboratively, and synchronize content automatically between devices over WiFi or 3G. The application provides business users with mobility and easy access to critical information from any location.
This document lists and describes various technologies and online tools used in the Zootopia eTwinning project. It discusses the use of a whiteboard, webcam, laptop, Skype, Gimp, Audacity, Windows Movie Maker, PowerPoint, blogs, Twinspace, Rayuela, Prezi and various educational websites for creating puzzles, animated videos, photo presentations, music, and more. The goal was to facilitate communication and sharing of project activities between partners in Spain and Cyprus.
Data Analysis and Visualization using PythonChariza Pladin
The document is a presentation about data analysis and visualization using Python libraries. It discusses how data is everywhere and growing exponentially, and introduces a 5-step process for data analysis and decision making. It emphasizes the importance of visualizing data to analyze patterns, discover insights, support stories, and teach others. The presentation then introduces Jupyter Notebook and highlights several Python libraries for data visualization, including matplotlib, seaborn, ggplot, Bokeh, pygal, plotly, and geoplotlib.
This Edureka Python Programming tutorial will help you learn python and understand the various basics of Python programming with examples in detail. Below are the topics covered in this tutorial:
1. Python Installation
2. Python Variables
3. Data types in Python
4. Operators in Python
5. Conditional Statements
6. Loops in Python
7. Functions in Python
8. Classes and Objects
pandas: Powerful data analysis tools for PythonWes McKinney
Wes McKinney introduced pandas, a Python data analysis library built on NumPy. Pandas provides data structures and tools for cleaning, manipulating, and working with relational and time-series data. Key features include DataFrame for 2D data, hierarchical indexing, merging and joining data, and grouping and aggregating data. Pandas is used heavily in financial applications and has over 1500 unit tests, ensuring stability and reliability. Future goals include better time series handling and integration with other Python data science packages.
This document provides an overview and objectives of a Python course for big data analytics. It discusses why Python is well-suited for big data tasks due to its libraries like PyDoop and SciPy. The course includes demonstrations of web scraping using Beautiful Soup, collecting tweets using APIs, and running word count on Hadoop using Pydoop. It also discusses how Python supports key aspects of data science like accessing, analyzing, and visualizing large datasets.
The document provides an introduction and overview of the Python programming language. It discusses that Python is an interpreted, object-oriented, high-level programming language that is easy to learn and read. It also covers Python features such as portability, extensive standard libraries, and support for functional, structured, and object-oriented programming. The document then discusses Python data types including numbers, strings, and various Python syntax elements before concluding with the history and evolution of the Python language through various versions.
The amount of data available to us is growing rapidly, but what is required to make useful conclusions out of it?
Outline
1. Different tactics to gather your data
2. Cleansing, scrubbing, correcting your data
3. Running analysis for your data
4. Bring your data to live with visualizations
5. Publishing your data for rest of us as linked open data
Python is a widely-used and powerful computer programming language that has helped system administrators manage computer networks and problem solve computer systems for decades. Python has also built some popular applications like BitTorrent, Blender, Calibre, Dropbox, and much more. Going further, the “Pi” in Raspberry Pi stands for Python, so learning Python will instill more confidence when working with Raspberry Pi projects. Python is usually the first programming language people learn primarily because it is easy to learn and provides a solid foundation to learn other computer programming languages. In this webinar,
• Learn what Python is and what it is capable of doing.
• Install Python’s IDE for Windows and work in the Python shell.
• Use calculations, variables, strings, lists, and if statements.
• Discover Python’s built-in functions and understand modules.
• Create simple programs to build on later.
The recording is available at https://youtu.be/ThcWmJFf-ho.
Python For Data Analysis | Python Pandas Tutorial | Learn Python | Python Tra...Edureka!
This Edureka Python Pandas tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) will help you learn the basics of Pandas. It also includes a use-case, where we will analyse the data containing the percentage of unemployed youth for every country between 2010-2014. Below are the topics covered in this tutorial:
1. What is Data Analysis?
2. What is Pandas?
3. Pandas Operations
4. Use-case
This document provides an introduction to data science. It discusses that data science uses computer science, statistics, machine learning, visualization, and human-computer interaction to collect, clean, analyze, visualize, and interact with data to create data products. It also describes the data science lifecycle as involving discovery, data preparation, model planning, model building, operationalizing models, and communicating results. Finally, it lists some common tools used in data science like Python, R, SQL, and Tableau.
This document discusses machine learning with Python. It provides an overview of Python, highlighting that it is easy to learn, has a vast community and documentation, and is versatile. It then defines machine learning and discusses popular Python libraries for machine learning like NumPy, SciPy, Matplotlib, Pandas, and OpenCV. It provides examples of operations that can be performed with OpenCV, like reading and manipulating images. Overall the document serves as an introduction to machine learning with Python and the main libraries used.
Data Analysis and Statistics in Python using pandas and statsmodelsWes McKinney
The document summarizes Wes McKinney's talk on statistical computing using Python. The talk introduces the scientific Python stack, including pandas for data structures and data analysis, and statsmodels for statistical modeling. It discusses the "research-production gap" in current statistical tools and how Python aims to bridge that gap. McKinney asserts that Python is the best solution for both research and production use of statistics and data analysis. He then demonstrates pandas and statsmodels functionality.
Python libraries presentation Contains all top 10 labraries information like numpy,tenslorflow,scikit-learn,Numpy,keras,PyToruch,LightGBM,Eli5,scipy,theano,pandas
1) The document introduces data science and its core disciplines, including statistics, machine learning, predictive modeling, and database management.
2) It explains that data science uses scientific methods and algorithms to extract knowledge and insights from both structured and unstructured data.
3) The roles of data scientists are discussed, noting that they have skills in programming, statistics, analytics, business analysis, and machine learning.
Python is an interpreted, object-oriented programming language created by Guido van Rossum in 1990. It has a clear, readable syntax and is designed to be highly extensible. Python code is often much shorter than equivalent code in other languages like C++ or Java due to features like indentation-based blocks and dynamic typing. It is used for web development, scientific computing, and more.
This document provides an overview of a data science course. It discusses topics like big data, data science components, use cases, Hadoop, R, and machine learning. The course objectives are to understand big data challenges, implement big data solutions, learn about data science components and prospects, analyze use cases using R and Hadoop, and understand machine learning concepts. The document outlines the topics that will be covered each day of the course including big data scenarios, introduction to data science, types of data scientists, and more.
Python is a popular programming language used in a variety of applications, including data analysis, web development, and artificial intelligence. Here's an introduction to the Basics of Python - A Beginners Guide! Whether you're new to programming or looking to brush up on your skills, this video covers the basics of Python programming language. From data types and operators to loops, functions and libraries, you'll get a solid foundation to start coding in Python.
Visit us: https://www.elewayte.com/
Pandas is a powerful Python library for data analysis and manipulation. It provides rich data structures for working with structured and time series data easily. Pandas allows for data cleaning, analysis, modeling, and visualization. It builds on NumPy and provides data frames for working with tabular data similarly to R's data frames, as well as time series functionality and tools for plotting, merging, grouping, and handling missing data.
A walk through the maze of understanding Data Visualization using several tools such as Python, R, Knime and Google Data Studio.
This workshop is hands-on and this set of presentations is designed to be an agenda to the workshop
The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.
WikiTouch is a personal wiki application for iPhone and iPod Touch users that allows them to easily create, organize, and share multimedia notes on the go. It helps users better manage their business and personal digital lives. Key features include the ability to access and edit notes from any device either online or offline, share notes and content collaboratively, and synchronize content automatically between devices over WiFi or 3G. The application provides business users with mobility and easy access to critical information from any location.
This document lists and describes various technologies and online tools used in the Zootopia eTwinning project. It discusses the use of a whiteboard, webcam, laptop, Skype, Gimp, Audacity, Windows Movie Maker, PowerPoint, blogs, Twinspace, Rayuela, Prezi and various educational websites for creating puzzles, animated videos, photo presentations, music, and more. The goal was to facilitate communication and sharing of project activities between partners in Spain and Cyprus.
Some see the iPad as one more way for people to find endless distractions and entertainments, a nail in the coffin for those who seek to "amuse themselves to death." Others believe the iPad is the best exemplar to date of the possibilities for extending human abilities to learn, connect, and create via powerful portable computing devices. Either way, the iPad and its ilk deserve attention from educators considering the future of teaching and learning. This session will open a conversation about the possibilities, in the hopes of helping participants to move beyond their preconceptions and biases.
The document is a thesis written by Muhammad Amir Irfan bin Mazian for his Bachelor's degree in Information Technology (Informatics Media) from Universiti Sultan Zainal Abidin in 2018. The thesis proposes developing an augmented reality mobile application called E-Library to provide an interactive experience for visitors of the university's main library. E-Library would allow users to explore a 3D model of the library and access information about its facilities in the real world using AR technologies like Unity and Vuforia. The goal is to make the library map and resources more engaging for users compared to traditional 2D printed maps.
75+ Tools for Investigative Journalists (English Version)Ezra Eeman
This document provides a list of 75+ tools for investigative journalists organized into categories like monitoring news, locations, social media research, collecting data, mapping stories, production, privacy, reporting, data stories, and multimedia. It introduces the tool, provides a brief description of its use, and links to the tool's website. The document encourages following the Twitter and Pinterest accounts of Journalism2ls for more tools and workshops on digital journalism.
1. Multimedia authoring tools provide the framework for organizing and editing multimedia projects and allow combining content and functions. Common authoring tools include Macromedia Flash, Director, and Authorware.
2. Authoring systems use different metaphors like card-based, icon-based, and time-based to organize multimedia elements. Elements have properties and form hierarchical relationships.
3. Multimedia production involves specialized roles and the design phase includes prototyping. Authoring tools help with creation, testing, and final development.
Here are some additional resources and supports:
Contact Andrew or Erin for help or to share ideas
Check out the Tech Integration website for tutorials
and lesson plans: http://techintegration.wikispaces.com/
Join or start a Tech Integration PLC
Attend future workshops on specific tools
Check out the Tech Tool of the Month handouts
Let us know if you have any other questions!
Digi-Tools is a one-semester exploratory course that introduces students to project-based learning using computer literacy skills, input technologies, career research, and digital communication tools necessary for today's world. Students will create presentations and projects using new technologies being utilized in the workplace to increase productivity. Major units of study include electronic note taking, web 2.0 applications, mobile applications, and global collaboration. At the end of the course, students will have gained experience with skills like creating infographics, presentations, videos, and blogs using various digital tools.
Here are a few ideas to get you started:
1. Create a class wiki to share resources and student work. Start with a simple layout and structure and expand it throughout the year as you learn more tools.
2. Design a lesson that uses Animoto, Photostory or Toon Doo to have students create a digital story or comic strip about a topic you are covering. This allows for creative expression and assessment of understanding.
3. Plan a lesson where students can choose to create a website, poster, brochure or other option to showcase what they learned. Provide examples of strong work and allow students to select the format based on their interests and skills. Offering technology-based choices engages more students.
Multimedia involves representing information through various digital media like audio, video, animation, and text. A multimedia system integrates these different types of media and allows them to be stored, processed, and interactively presented on a computer. Key challenges for multimedia systems include synchronizing and properly rendering different media streams. Popular multimedia applications include the World Wide Web, presentations, digital video editing, and virtual reality.
The document discusses prototyping tools for interaction design. It describes the purposes of prototyping as creating early versions of ideas to gain empathy, explore designs, and test with users. A variety of prototyping tools are presented, ranging from low-fidelity sketching and paper prototypes to high-fidelity interactive digital prototypes. Key tools mentioned include UXPin, Axure, Balsamiq, Origami, App Inventor, and Arduino. The document emphasizes that prototypes allow for elaboration and refinement of ideas through repeated testing and user feedback.
The document describes a workshop on designing learning spaces with Web 2.0 tools. The workshop aims to provide an overview of how Web 2.0 tools can be integrated into formal, non-formal and informal learning environments. The agenda includes case studies of Web 2.0 tool integration, a discussion of various Web 2.0 tools and models of their use in education, and a question and answer session.
Online: the rise and rise. How Web 2.0 is changing construction PR and marketingpwcom.co.uk Ltd
Slides used at Be2camp Brum (12 August 2009). Opening presentation gave an overview of the range of social media tools available for use in corporate PR and marketing (not solely for construction organisations - but that was the main focus of the event)
ONLINE CREATION TOOLS AND APPLICATIONS FOR ICT CONTENT DEVELOPMENTSunsunGarden
This document discusses various online tools and platforms for creating and sharing different types of digital content. It describes audio and visual content creation tools that allow users to create, edit and publish audio like music and sounds using software like Audio Director, Logic Pro, Cubase Pro and Adobe Audition. It also discusses tools for creating infographics, videos, presentations and memes. It provides examples of online platforms like Prezi and Zoho for presentations, and content management systems that allow publishing and organizing web content.
Cs8092 computer graphics and multimedia unit 5SIMONTHOMAS S
This document discusses multimedia authoring tools and techniques. It covers several topics:
1. Types of multimedia authoring tools including card/page based tools, icon based tools, and time based tools. Popular examples are discussed.
2. Key features and capabilities of authoring tools including editing, programming, interactivity, playback, delivery, and project organization.
3. Authoring system metaphors like hierarchical, flow control, and different technologies focused on like hypermedia.
4. Considerations for multimedia production, presentation, and automatic authoring. Professional development tools are also outlined.
1) The document discusses the emerging field of connecting embedded devices to the internet and modeling their services using RESTful APIs to make them part of the World Wide Web.
2) Several prototypes are described that implement a "Web of Things" approach, including projects for energy monitoring and physical mashups.
3) Challenges are discussed around client-server interactions for embedded devices, discovery of connected devices, and the need for standards or best practices as the Web of Things continues to grow.
INTERACTIVE TOOLS FOR MEETINGS, CONFERENCES, CONVENTIONS, INCENTIVES, EXHIBIT...Jorge Torio, CASE
The document discusses various interactive technology options for meetings, conventions and exhibits including Poken, Digivote, Digitoon, interactive screens and floors, kiosks, mobile apps, and TPIC games. These technologies allow for networking, information sharing, audience engagement, feedback collection, and branding opportunities in a greener digital format. Videos are available to demonstrate how each interactive technology can be used.
When You Can’t Go All In on SwiftUI, Build a Hybrid UI App Instead!Vui Nguyen
There will be times when building an app entirely in SwiftUI is not an option. Maybe your company has a large UIKit app that you can’t afford to migrate to SwiftUI overnight. Or you start building a new SwiftUI app, and run into technical limitations (due to SwiftUI’s relative “newness”) or a tight deadline, and wish you could pull in an existing, custom-built UIKit component to solve the problem. One solution is to start wherever you’re at, a UIKit or SwiftUI app, and swap in a component from the other framework as needed. I will demonstrate how to do both, starting with a single screen app in SwiftUI, and also in a similar UIKit app. With a hybrid UI app, you get the benefits of working with a modern UI framework, SwiftUI, while minimizing your project risks!
Presented at 360iDev 2022, https://360idev.com
Build an i phone, android, or blackberry web app with jq touch and jqueryAntonio Chagoury
1) The document discusses building mobile web apps using jQTouch and jQuery. It announces a hackathon for building DotNetNuke modules with compelling user interfaces.
2) jQTouch is presented as an alternative to using Objective-C to build mobile apps. jQTouch allows building apps with HTML5, JavaScript and deploying without the App Store.
3) The approach demonstrated will be to build a mobile web app for a movie rental website using an ASP.NET MVC site providing JSON data, the jQTouch framework, HTML5, CSS and testing on MobiOne Test Center.
Data science apps powered by Jupyter NotebooksNatalino Busa
Jupyter notebooks are transforming the way we look at computing, coding, and science. But is this the only "data scientist experience" that this technology can provide? In this presentation we will look at how to create interactive web applications for data exploration and machine learning. In the background this code is still powered by the well-understood and well-documented Jupyter Notebooks.
Code on github: https://github.com/natbusa/kernelgateway_demos
To ensure a smooth class, students should upgrade to the latest versions of R and RStudio. R version 3.6.2 and RStudio version 1.2.5033 are the latest. Users can check their versions by entering commands in the terminal or by selecting "Help" then "About RStudio" in RStudio. Upgrading instructions for Windows, Linux, and MacOS are provided.
This document provides instructions for installing R on various operating systems using different methods. For Windows, it describes using Anaconda, downloading from CRAN directly, or using Microsoft Open. For Mac, it provides instructions for downloading from CRAN or using Homebrew. For Linux, it recommends using apt-get to install from open labs. Across operating systems, the instructions recommend checking the R version after installation to confirm successful setup.
The document provides instructions for installing the reshape2 package in R on different operating systems. The preferred method is to install directly from R or RStudio using install.packages("reshape2") and library("reshape2"). For Linux (Ubuntu), commands are given to install via apt-get. For Mac, specific commands using install.binaries are provided. For Windows, multiple commands using install.binaries from different CRAN links are listed as alternatives.
The document provides instructions for installing the dplyr package in R using different methods. The preferred method is to use install.packages("dplyr") in RStudio. If that does not work, alternatives include installing from GitHub using devtools or installing via CRAN, with operating system-specific commands for Ubuntu, Mac, and Windows.
This document provides instructions for installing the ggplot2 package in R on different operating systems. The easiest method is to use install.packages("ggplot2") in RStudio. If that does not work, an alternative is to install from GitHub using devtools::install_github("tidyverse/ggplot2"). For different operating systems like Ubuntu, Mac, and Windows, there are additional commands provided to install via CRAN.
This document provides step-by-step instructions for installing Python on Linux. It discusses selecting a Python version, downloading the installer from python.org, extracting and configuring the installer, making and installing Python, and testing the installation by printing "Hello World!". The instructions cover installing both Python 2 and Python 3 on Ubuntu systems.
Python is an old programming language that has gained new popularity for machine learning. It exists in two versions, Python 2 and Python 3. The tutorial explains how to install both versions on a Mac by downloading them from python.org, running through an interactive installation process, and testing the installation by running sample Python code in the terminal.
This document provides instructions for installing Python version 2 or 3 on Windows. It explains that Python is commonly used for machine learning and deep learning. The user should select the version based on compatibility with their intended projects. The steps then outline downloading the installer from python.org, selecting options during installation like adding to the system PATH, and testing the installation by checking the version and running a simple print command in the terminal.
This document provides information about past events and expertise in the fields of data science, machine learning, deep learning, Python, R programming, and data science. It notes that a past event in Bangalore on AI and machine learning included an award for best paper on security at an international conference.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
4. Jupyter Notebooks
The notebook extends the console-based
approach to interactive computing in a
qualitatively new direction, providing a web-
based application suitable for capturing the
whole computation process: developing,
documenting, and executing code, as well
as communicating the results. The Jupyter
notebook combines two components:
A web application: a browser-based tool for
interactive authoring of documents which
combine explanatory text, mathematics,
computations and their rich media output.
Notebook documents: a representation of
all content visible in the web application,
including inputs and outputs of the
computations, explanatory text,
mathematics, images, and rich media
representations of objects.