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Organice sus actividades: Como responsable de su proceso, es importante que identifique los tiempos que debe dedicar a sus actividades de estudio, con el objetivo de sacar el mejor provecho de la modalidad virtual.

Conózcase: Si es su primera experiencia en la virtualidad, aprenda a reconocer aquellas actividades que exigen mayor atención e identifique sus alcances.

Mantenga su mente abierta: En la virtualidad, el conocimiento puede llegar desde diferentes medios, puesto que puede darse a partir de diferentes contenidos (esto no se refiere solo a los formatos), interacciones con los compañeros y el profesor y el uso de herramientas que pueden facilitar su aprendizaje.

Integre las TIC a su formación: Estas pueden ser sus grandes aliadas porque, además de ser un medio, favorecen el aprendizaje autónomo, ofrecen flexibilidad con los tiempos de dedicación para estudiar los temas, facilitan el acceso desde cualquier lugar, siempre y cuando se tenga una conexión a internet, y fomentan la creación de comunidades para interactuar con otros que tienen intereses similares, posibilitando la creación y transferencia de conocimiento.

Motívese: La virtualidad le puede traer grandes retos y beneficios, sea paciente con su proceso, identifique y reconozca sus potencialidades y aquello en lo que debe trabajar para mejorar su experiencia de aprendizaje en esta modalidad.

Identifique estrategias de estudio: Haga resúmenes, organice sus materiales, revise los videos explicativos, tome nota de lo más importante y realice las actividades propuestas.

Explore los medios de comunicación: Anote todas sus dudas inmediatamente se le presenten. Lleve sus dudas e inquietudes a foros y comunidades de estudio para que juntos podamos construir soluciones y seguir avanzando.

Controle sus tiempos: Construya una agenda, mantenga al día sus lecturas y actividades, de esta manera, podrá resolver dudas y prepararse en el tiempo adecuado.

Adecúe su lugar de estudio: Las distracciones pueden ser un gran obstáculo en su proceso de formación, elija cuidadosamente su lugar de estudio, asegúrese del funcionamiento de sus equipos, tenga a la mano sus materiales y respete su tiempo de formación.

Tome la iniciativa: Sea autónomo en su proceso de aprendizaje, no es necesario esperar instrucciones del dinamizador, si es riguroso con la lectura de los contenidos encontrará el camino. De igual forma, interactúe con otros, búsque espacios de aprendizaje conjunto, forme grupos de estudio e intercambie ideas.

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Recomendaciones para estudiar - Vida universitaria

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Recomendaciones para estudiar - Vida universitaria

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DIPLOMADO EN INTELIGENCIA ARTIFICIAL APLICADO A LA INVESTIGACIÓN

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Login to access and create your next design.

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¡BIENVENIDOS/AS A LA ASIGNATURA FUNDAMENTOS DE INTELIGENCIA ARTIFICIAL!

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¡BIENVENIDOS/AS A LA ASIGNATURA FUNDAMENTOS DE INTELIGENCIA ARTIFICIAL!

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En un mundo donde la inteligencia artificial (IA) está transformando todos los campos del conocimiento, la investigación científica no es la excepción. Aprender sobre esta rama de la informática ofrece una oportunidad única para abordar problemas complejos, optimizar recursos y generar conocimiento de manera más eficiente en el ámbito investigativo.

La asignatura "Fundamentos de Inteligencia Artificial en la Investigación" tiene como objetivo proporcionar los conocimientos fundamentales sobre la IA, su evolución, principales áreas de desarrollo y aplicaciones en contextos científicos. Durante el curso, los participantes explorarán conceptos clave como Machine Learning, Deep Learning e inteligencia artificial general (IAG). Asimismo, analizarán el impacto de estas tecnologías en el modelado predictivo, el análisis de resultados y la automatización de procesos de investigación.

Actividades individuales a realizar:

Leer los materiales ubicados en el apartado de recursos de aprendizaje.

Completar el cuestionario de evaluación de la asignatura. Nota: El cuestionario permite dos intentos y el sistema registrará la calificación más alta obtenida.

Observar el video disponible y responder las preguntas durante su reproducción.

¡Les invito a participar activamente y a aprovechar al máximo esta experiencia formativa!

Les deseo muchos éxitos,

Su tutor/a

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Bienvenida

La asignatura Fundamentos de Inteligencia Artificial en la Investigación ofrece una introducción integral a los conceptos, evolución y aplicaciones de la inteligencia artificial (IA) en el ámbito científico. Este curso tiene como objetivo preparar a los estudiantes para comprender y aplicar herramientas de IA que optimicen los procesos investigativos, promuevan la innovación y resuelvan problemas complejos de manera efectiva.

Estructura y Desarrollo de la Asignatura

Recursos de Aprendizaje

Durante el curso, tendrás acceso a:

Foro de dudas e inquietudes de la unidad.

Materiales digitales, como lecturas, tutoriales y presentaciones de los docentes.

Artículos académicos actualizados y guías de apoyo disponibles en la webgrafía.

Actividades de Aprendizaje

El curso incluye actividades diseñadas para fomentar el aprendizaje activo, como:

Lectura de materiales: Revisión de recursos alojados en el curso y artículos complementarios.

Actividad: Panel IA como herramienta para la optimización de proyectos de investigación.

Evaluación Final

La evaluación está estructurada de la siguiente manera:

Cuestionarios: Disponibles con dos intentos; se tomará la calificación más alta.

Participación activa: Valoración de la interacción en las actividades de aprendizaje.

Programa Adjunto:

Al final de esta guía, encontrarás el programa completo de la asignatura, donde se detallan las unidades, objetivos y metodologías de trabajo.

Recuerda: La inteligencia artificial no reemplaza nuestra humanidad; la potencia. Es una herramienta que, en tus manos, puede transformar ideas en soluciones y sueños en realidades.

¡Te invito a participar activamente en este curso y a aprovechar los recursos proporcionados!

Su tutor/a

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En esta carpeta se encuentran las presentaciones de los panelistas que participaron en el Panel sobre Inteligencia Artificial:

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En esta carpeta se encuentran las presentaciones de los panelistas que participaron en el Panel sobre Inteligencia Artificial:

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Ing. Andrés Rodríguez Conde

En su presentación, el Ing. Rodríguez Conde aborda los fundamentos de la Inteligencia Artificial (IA), explicando sus principios básicos, las tecnologías clave involucradas y cómo estos fundamentos están sentando las bases para su aplicación en diversos campos.

Dr. Darwin Muñoz

El Dr. Muñoz, por su parte, profundiza en la aplicación de la Inteligencia Artificial en la investigación, explorando cómo la IA está revolucionando los métodos y procesos de investigación.

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2501Presentación Andres Rodriguez Diplomado UASD v1

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INTELIGENCIA ARTIFICIAL APLICADA A LA INVESTIGACIÓN (1)

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Apertura Diplomado Internacional en la IA aplicado a la investigación | Apertura del Diplomado Internacional en la IA Aplicado a la Investigación | By Universidad Autónoma de Santo Domingo (UASD) | Facebook

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Apertura del Diplomado Internacional en la IA Aplicado a la Investigación

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Potencialidades de la Inteligencia Artificial en Educación Superior: Un Enfoque desde la Personalización

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ASIGNATURA 2. HERRAMIENTAS DE IA EN LA INVESTIGACIÓN

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ASIGNATURA 2. HERRAMIENTAS DE IA EN LA INVESTIGACIÓN

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¡BIENVENIDOS/AS A LA ASIGNATURA HERRAMIENTAS DE IA EN LA INVESTIGACIÓN!

En un mundo donde la inteligencia artificial (IA) está revolucionando los procesos de generación y análisis de conocimiento, la investigación científica se encuentra en el centro de esta transformación. Esta asignatura permitirá explorar cómo las herramientas basadas en IA pueden optimizar procesos, mejorar la precisión de los resultados y generar nuevos enfoques en los proyectos investigativos.

La asignatura "Herramientas de IA en la Investigación" tiene como objetivo principal proporcionar conocimientos prácticos sobre el uso de herramientas de inteligencia artificial en diversas fases de la investigación científica. A lo largo del curso, los participantes se familiarizarán con aplicaciones clave de IA, como el análisis de datos, la predicción de tendencias, la automatización de tareas repetitivas y el diseño de modelos avanzados para resolver problemas complejos.

Actividades individuales a realizar:

1. Revisar los materiales de aprendizaje:

Explora los recursos ubicados en el apartado de recursos de aprendizaje para comprender los conceptos fundamentales y el uso de herramientas específicas.

2. Completar el cuestionario de evaluación de la asignatura:

Evalúa tus conocimientos respondiendo al cuestionario diseñado para medir tu comprensión. Nota: El cuestionario permite dos intentos, y el sistema registrará la calificación más alta obtenida.

3. Observar el video disponible y responder preguntas:

Visualiza el video proporcionado y responde las preguntas planteadas durante su reproducción para afianzar los conceptos clave.

¡Les invito a participar activamente y a aprovechar al máximo esta experiencia formativa!

Les deseo mucho éxito,

Su tutor/a.

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Herramientas de Inteligencia Artificial para Investigación- Dr. Darwin Muñoz

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Vicerrectoría de Investigación y Postgrado UASD

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Diplomado en Inteligencia Artificial - DIA-1

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Catálogo de Herramientas de Inteligencia Artificial para la Investigación

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Domina la Formula para Crear Prompts de Experto en Chat GPT

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LITMAP: ¿Cómo encontrar artículos científicos con Inteligencia Artificial?

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Las 5 mejores herramientas de IA para la investigación

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Herramientas de Inteligencia Artificial Generativa 

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Inteligencia artificial para investigar. Scholar GPT.

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🔍 Research Rabbit: Tu Asistente de IA Definitivo 🐇

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PaperPal - Inteligencias Artificiales #13 - Parte 1

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DIA-03 PROGRAMA Analisis de Datos con  IA

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Vicerrectoría de Investigación y Postgrado UASD

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Análisis de Datos -Diplomado en Inteligencia Artificial

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Como APRENDER INTELIGENCIA ARTIFICIAL Con PYTHON en 5 MINUTOS | IA Python Ejemplos {2023}

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Download Visual Studio Code - Mac, Linux, Windows

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Visual Studio Code is free and available on your favorite platform - Linux, macOS, and Windows. Download Visual Studio Code to experience a redefined code editor, optimized for building and debugging modern web and cloud applications.

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In this tutorial, you will use Python 3 to create a simple Python "Hello World" application in Visual Studio Code. By using the Python extension, you turn VS Code into a great, lightweight Python editor (which you may find a productive alternative to PyCharm).

This tutorial introduces you to VS Code for Python development - primarily how to edit, run, and debug code through the following tasks:

Write, run, and debug a Python "Hello World" Application

Learn how to install packages by creating Python virtual environments

Write a simple Python script to plot figures within VS Code

This tutorial is not intended to teach you Python itself. Once you are familiar with the basics of VS Code, you can then follow any of the programming tutorials on python.org within the context of VS Code for an introduction to the language.

If you have any problems, you can search for answers or ask a question on the Python extension Discussions Q&A.

Prerequisites

To successfully complete this tutorial, you need to first setup your Python development environment. Specifically, this tutorial requires:

Install Visual Studio Code and the Python Extension

1. If you have not already done so, install VS Code.

2. Next, install the Python extension for VS Code from the Visual Studio Marketplace. For additional details on installing extensions, see Extension Marketplace. The Python extension is named Python and it's published by Microsoft.

Install a Python interpreter

Along with the Python extension, you need to install a Python interpreter. Which interpreter you use is dependent on your specific needs, but some guidance is provided below.

Windows

Install Python from python.org. You can typically use the Download Python button that appears first on the page to download the latest version.

Note: If you don't have admin access, an additional option for installing Python on Windows is to use the Microsoft Store. The Microsoft Store provides installs of supported Python versions.

For additional information about using Python on Windows, see Using Python on Windows at Python.org

macOS

The system install of Python on macOS is not supported. Instead, a package management system like Homebrew is recommended. To install Python using Homebrew on macOS use brew install python3 at the Terminal prompt.

Note On macOS, make sure the location of your VS Code installation is included in your PATH environment variable. See these setup instructions for more information.

Linux

The built-in Python 3 installation on Linux works well, but to install other Python packages you must install pip with get-pip.py.

Other options

Data Science: If your primary purpose for using Python is Data Science, then you might consider a download from Anaconda. Anaconda provides not just a Python interpreter, but many useful libraries and tools for data science.

Windows Subsystem for Linux: If you are working on Windows and want a Linux environment for working with Python, the Windows Subsystem for Linux (WSL) is an option for you. If you choose this option, you'll also want to install the WSL extension. For more information about using WSL with VS Code, see VS Code Remote Development or try the Working in WSL tutorial, which will walk you through setting up WSL, installing Python, and creating a Hello World application running in WSL.

Verify the Python installation

To verify that you've installed Python successfully on your machine, run one of the following commands (depending on your operating system):

If the installation was successful, the output window should show the version of Python that you installed.

Note You can use the py -0 command in the VS Code integrated terminal to view the versions of python installed on your machine. The default interpreter is identified by an asterisk (*).

Start VS Code in a workspace folder

By starting VS Code in a folder, that folder becomes your "workspace". VS Code stores settings that are specific to that workspace in .vscode/settings.json, which are separate from user settings that are stored globally.

Using a command prompt or terminal, create an empty folder called "hello", navigate into it, and open VS Code ( code) in that folder (.) by entering the following commands:

Note: If you're using an Anaconda distribution, be sure to use an Anaconda command prompt.

Alternately, you can run VS Code through the operating system UI, then use File > Open Folder to open the project folder.

Select a Python interpreter

Python is an interpreted language. Thus, in order to run Python code and get Python IntelliSense, you must tell VS Code which interpreter to use.

From within VS Code, select a Python 3 interpreter by opening the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), start typing the Python: Select Interpreter command to search, then select the command. You can also use the Select Python Environment option on the Status Bar if available (it may already show a selected interpreter, too):

The command presents a list of available interpreters that VS Code can find automatically, including virtual environments. If you don't see the desired interpreter, see Configuring Python environments.

Note: When using an Anaconda distribution, the correct interpreter should have the suffix ('base':conda), for example Python 3.7.3 64-bit ('base':conda).

Selecting an interpreter sets which interpreter will be used by the Python extension for that workspace.

Note: If you select an interpreter without a workspace folder open, VS Code sets python.defaultInterpreterPath in User scope instead, which sets the default interpreter for VS Code in general. The user setting makes sure you always have a default interpreter for Python projects. The workspace settings lets you override the user setting.

Create a Python Hello World source code file

From the File Explorer toolbar, select the New File button on the hello folder:

Name the file hello.py, and it automatically opens in the editor:

By using the .py file extension, you tell VS Code to interpret this file as a Python program, so that it evaluates the contents with the Python extension and the selected interpreter.

Note: The File Explorer toolbar also allows you to create folders within your workspace to better organize your code. You can use the New folder button to quickly create a folder.

Now that you have a code file in your Workspace, enter the following source code in hello.py:

When you start typing print, notice how IntelliSense presents auto-completion options.

IntelliSense and auto-completions work for standard Python modules as well as other packages you've installed into the environment of the selected Python interpreter. It also provides completions for methods available on object types. For example, because the msg variable contains a string, IntelliSense provides string methods when you type msg.:

Feel free to experiment with IntelliSense some more, but then revert your changes so you have only the msg variable and the print call, and save the file ().

For full details on editing, formatting, and refactoring, see Editing code. The Python extension also has full support for Linting.

Run Hello World

It's simple to run hello.py with Python. Just click the Run Python File in Terminal play button in the top-right side of the editor.

The button opens a terminal panel in which your Python interpreter is automatically activated, then runs python3 hello.py (macOS/Linux) or python hello.py (Windows):

There are three other ways you can run Python code within VS Code:

Right-click anywhere in the editor window and select Run Python File in Terminal (which saves the file automatically):

Select one or more lines, then press Shift+Enter or right-click and select Run Selection/Line in Python Terminal. This command is convenient for testing just a part of a file.

From the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), select the Python: Start REPL command to open a REPL terminal for the currently selected Python interpreter. In the REPL, you can then enter and run lines of code one at a time.

Configure and run the debugger

Let's now try debugging our simple Hello World program.

First, set a breakpoint on line 2 of hello.py by placing the cursor on the print call and pressing . Alternately, just click in the editor's left gutter, next to the line numbers. When you set a breakpoint, a red circle appears in the gutter.

Next, to initialize the debugger, press . Since this is your first time debugging this file, a configuration menu will open from the Command Palette allowing you to select the type of debug configuration you would like for the opened file.

Note: VS Code uses JSON files for all of its various configurations; launch.json is the standard name for a file containing debugging configurations.

These different configurations are fully explained in Debugging configurations; for now, just select Python File, which is the configuration that runs the current file shown in the editor using the currently selected Python interpreter.

You can also start the debugger by clicking on the down-arrow next to the run button on the editor, and selecting Debug Python File in Terminal.

The debugger will stop at the first line of the file breakpoint. The current line is indicated with a yellow arrow in the left margin. If you examine the Local variables window at this point, you will see now defined msg variable appears in the Local pane.

A debug toolbar appears along the top with the following commands from left to right: continue (), step over (), step into (), step out (⇧F11 (Windows, Linux Shift+F11)), restart (⇧⌘F5 (Windows, Linux Ctrl+Shift+F5)), and stop (⇧F5 (Windows, Linux Shift+F5)).

The Status Bar also changes color (orange in many themes) to indicate that you're in debug mode. The Python Debug Console also appears automatically in the lower right panel to show the commands being run, along with the program output.

To continue running the program, select the continue command on the debug toolbar (). The debugger runs the program to the end.

Tip Debugging information can also be seen by hovering over code, such as variables. In the case of msg, hovering over the variable will display the string Hello world in a box above the variable.

You can also work with variables in the Debug Console (If you don't see it, select Debug Console in the lower right area of VS Code, or select it from the ... menu.) Then try entering the following lines, one by one, at the > prompt at the bottom of the console:

Select the blue Continue button on the toolbar again (or press F5) to run the program to completion. "Hello World" appears in the Python Debug Console if you switch back to it, and VS Code exits debugging mode once the program is complete.

If you restart the debugger, the debugger again stops on the first breakpoint.

To stop running a program before it's complete, use the red square stop button on the debug toolbar (⇧F5 (Windows, Linux Shift+F5)), or use the Run > Stop debugging menu command.

For full details, see Debugging configurations, which includes notes on how to use a specific Python interpreter for debugging.

Tip: Use Logpoints instead of print statements: Developers often litter source code with print statements to quickly inspect variables without necessarily stepping through each line of code in a debugger. In VS Code, you can instead use Logpoints. A Logpoint is like a breakpoint except that it logs a message to the console and doesn't stop the program. For more information, see Logpoints in the main VS Code debugging article.

Install and use packages

Let's now run an example that's a little more interesting. In Python, packages are how you obtain any number of useful code libraries, typically from PyPI. For this example, you use the matplotlib and numpy packages to create a graphical plot as is commonly done with data science. (Note that matplotlib cannot show graphs when running in the Windows Subsystem for Linux as it lacks the necessary UI support.)

Return to the Explorer view (the top-most icon on the left side, which shows files), create a new file called standardplot.py, and paste in the following source code:

Tip: If you enter the above code by hand, you may find that auto-completions change the names after the as keywords when you press Enter at the end of a line. To avoid this, type a space, then Enter.

Next, try running the file in the debugger using the "Python: Current file" configuration as described in the last section.

Unless you're using an Anaconda distribution or have previously installed the matplotlib package, you should see the message, "ModuleNotFoundError: No module named 'matplotlib'". Such a message indicates that the required package isn't available in your system.

To install the matplotlib package (which also installs numpy as a dependency), stop the debugger and use the Command Palette to run Terminal: Create New Terminal (⌃⇧` (Windows, Linux Ctrl+Shift+`)). This command opens a command prompt for your selected interpreter.

A best practice among Python developers is to avoid installing packages into a global interpreter environment. You instead use a project-specific virtual environment that contains a copy of a global interpreter. Once you activate that environment, any packages you then install are isolated from other environments. Such isolation reduces many complications that can arise from conflicting package versions. To create a virtual environment and install the required packages, enter the following commands as appropriate for your operating system:

Note: For additional information about virtual environments, see Environments. v

1. Create a virtual environment using the Create Environment command

From within VS Code, you can create non-global environments, using Venv or Anaconda, by opening the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), start typing the Python: Create Environment command to search, and then select the command. You can also trigger the Python: Create Environment command through the Getting Started with Python page.

The command presents a list of environment types, Venv or Conda. For this example, select Venv.

The command then presents a list of interpreters that can be used for your project.

After selecting the desired interpreter, a notification will show the progress of the environment creation and the environment folder will appear in your workspace.

The command will also install necessary packages outlined in a requirements/dependencies file, such as requirements.txt, pyproject.toml, or environment.yml, located in the project folder.

Note: If you want to create an environment manually, or run into error in the environment creation process, visit the Environments page.

2. Ensure your new environment is selected by using the Python: Select Interpreter command from the Command Palette.

3. Install the packages

# Don't use with Anaconda distributions because they include matplotlib already. # macOS python3 -m pip install matplotlib # Windows (may require elevation) py -m pip install matplotlib # Linux (Debian) apt-get install python3-tk python3 -m pip install matplotlib

4. Rerun the program now (with or without the debugger) and after a few moments a plot window appears with the output:

5. Once you are finished, type deactivate in the terminal window to deactivate the virtual environment.

For additional examples of creating and activating a virtual environment and installing packages, see the Django tutorial and the Flask tutorial.

Next steps

You can configure VS Code to use any Python environment you have installed, including virtual and conda environments. You can also use a separate environment for debugging. For full details, see Environments.

To learn more about the Python language, follow any of the programming tutorials listed on python.org within the context of VS Code.

To learn to build web apps with the Django and Flask frameworks, see the following tutorials:

There is then much more to explore with Python in Visual Studio Code:

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Get Started Tutorial for Python in Visual Studio Code

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In this tutorial, you will use Python 3 to create a simple Python "Hello World" application in Visual Studio Code. By using the Python extension, you turn VS Code into a great, lightweight Python editor (which you may find a productive alternative to PyCharm).

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Herramientas y librerías de Machine Learning en Python | OpenWebinars

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¿Quieres conocer las mejores herramientas y librerías de Machine Learning en Python? Descubre cómo te ayudan a crear modelos de ML precisos y eficientes.

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Learn about Visual Studio Code as a Python IDE (code completion, debugging, linting).

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Build with Visual Studio Code, anywhere, anytime, entirely in your browser.

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Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming. Python has been built with extraordinary Python libraries for data science that are used by programmers every day in solving problems. Here's the top 10 Python libraries for data science:

Top 10 Python Libraries for Data Science

TensorFlow

NumPy

SciPy

Pandas

Matplotlib

Keras

SciKit-Learn

PyTorch

Scrapy

BeautifulSoup

1.TensorFlow

The first in the list of python libraries for data science is TensorFlow. TensorFlow is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of around 1,500 contributors. It's used across various scientific fields. TensorFlow is basically a framework for defining and running computations that involve tensors, which are partially defined computational objects that eventually produce a value.

Features:

Better computational graph visualizations

Reduces error by 50 to 60 percent in neural machine learning

Parallel computing to execute complex models

Seamless library management backed by Google

Quicker updates and frequent new releases to provide you with the latest features

TensorFlow is particularly useful for the following applications:

2. SciPy

SciPy (Scientific Python) is another free and open-source Python library for data science that is extensively used for high-level computations. SciPy has around 19,000 comments on GitHub and an active community of about 600 contributors. It's extensively used for scientific and technical computations, because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations.

Features:

Collection of algorithms and functions built on the NumPy extension of Python

High-level commands for data manipulation and visualization

Multidimensional image processing with the SciPy ndimage submodule

Includes built-in functions for solving differential equations

Applications:

Multidimensional image operations

Solving differential equations and the Fourier transform

Optimization algorithms

Linear algebra

3. NumPy

NumPy (Numerical Python) is the fundamental package for numerical computation in Python; it contains a powerful N-dimensional array object. It has around 18,000 comments on GitHub and an active community of 700 contributors. It's a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.

Features:

Provides fast, precompiled functions for numerical routines

Array-oriented computing for better efficiency

Supports an object-oriented approach

Compact and faster computations with vectorization

Applications:

Extensively used in data analysis

Creates powerful N-dimensional array

Forms the base of other libraries, such as SciPy and scikit-learn

Replacement of MATLAB when used with SciPy and matplotlib

4. Pandas

Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitHub and an active community of 1,200 contributors, it is heavily used for data analysis and cleaning. Pandas provides fast, flexible data structures, such as data frame CDs, which are designed to work with structured data very easily and intuitively.

Also Read: What is Data Analysis: Methods, Process and Types Explained

Features:

Eloquent syntax and rich functionalities that gives you the freedom to deal with missing data

Enables you to create your own function and run it across a series of data

High-level abstraction

Contains high-level data structures and manipulation tools

Applications:

General data wrangling and data cleaning

ETL (extract, transform, load) jobs for data transformation and data storage, as it has excellent support for loading CSV files into its data frame format

Used in a variety of academic and commercial areas, including statistics, finance and neuroscience

Time-series-specific functionality, such as date range generation, moving window, linear regression and date shifting.

Python Students Also Learn

Data Science | Machine Learning | Tableau | Data Analysis | Statistics

JavaScript | Excel | Deep Learning | Artificial Intelligence

5. Matplotlib

Matplotlib has powerful yet beautiful visualizations. It's a plotting library for Python with around 26,000 comments on GitHub and a very vibrant community of about 700 contributors. Because of the graphs and plots that it produces, it's extensively used for data visualization. It also provides an object-oriented API, which can be used to embed those plots into applications.

Features:

Usable as a MATLAB replacement, with the advantage of being free and open source

Supports dozens of backends and output types, which means you can use it regardless of which operating system you're using or which output format you wish to use

Pandas itself can be used as wrappers around MATLAB API to drive MATLAB like a cleaner

Low memory consumption and better runtime behavior

Applications:

Correlation analysis of variables

Visualize 95 percent confidence intervals of the models

Outlier detection using a scatter plot etc.

Visualize the distribution of data to gain instant insights

Also Read: Exploring The Data Science Learning Path

Build your career in Data Analytics with our Data Analyst Master's Program! Cover core topics and important concepts to help you get started the right way!

6. Keras

Similar to TensorFlow, Keras is another popular library that is used extensively for deep learning and neural network modules. Keras supports both the TensorFlow and Theano backends, so it is a good option if you don't want to dive into the details of TensorFlow.

Also Read: Keras vs Tensorflow vs Pytorch

Features:

Keras provides a vast prelabeled datasets which can be used to directly import and load.

It contains various implemented layers and parameters that can be used for construction, configuration, training, and evaluation of neural networks

Applications:

One of the most significant applications of Keras are the deep learning models that are available with their pretrained weights. You can use these models directly to make predictions or extract its features without creating or training your own new model.

7. Scikit-learn

Next in the list of the top python libraries for data science comes Scikit-learn, a machine learning library that provides almost all the machine learning algorithms you might need. Scikit-learn is designed to be interpolated into NumPy and SciPy.

Applications:

clustering

classification

regression

model selection

dimensionality reduction

8. PyTorch

Next in the list of top python libraries for data science is PyTorch, which is a Python-based scientific computing package that uses the power of graphics processing units. PyTorch is one of the most commonly preferred deep learning research platforms built to provide maximum flexibility and speed.

Applications:

PyTorch is famous for providing two of the most high-level features

tensor computations with strong GPU acceleration support

building deep neural networks on a tape-based autograd system

9. Scrapy

The next known python libraries for data science is Scrapy. Scrapy isone of the most popular, fast, open-source web crawling frameworks written in Python. It is commonly used to extract the data from the web page with the help of selectors based on XPath.

Applications:

Scrapy helps in building crawling programs (spider bots) that can retrieve structured data from the web

Scrappy is also used to gather data from APIs and follows a 'Don't Repeat Yourself' principle in the design of its interface, influencing users to write universal codes that can be reused for building and scaling large crawlers.

10. BeautifulSoup

BeautifulSoup - the next python library for data science. This is another popular python library most commonly known for web crawling and data scraping. Users can collect data that's available on some website without a proper CSV or API, and BeautifulSoup can help them scrape it and arrange it into the required format.

Looking forward to making a move to the programming field? Take up the Python Training Course and begin your career as a professional Python programmer.

Become a Python Pro

In addition to these top 10 Python libraries for data science, there are many other helpful python libraries ones that deserve to be looked at. And as a next step, if you are interested in learning and mastering data science with python, head onto Simpliearn's Data Science with Python Certification Course. Do explore the frequently asked Data Science interview question, and unlock your career as a data scientist!

1.TensorFlow

The first in the list of python libraries for data science is TensorFlow. TensorFlow is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of around 1,500 contributors. It's used across various scientific fields. TensorFlow is basically a framework for defining and running computations that involve tensors, which are partially defined computational objects that eventually produce a value.

Features:

Better computational graph visualizations

Reduces error by 50 to 60 percent in neural machine learning

Parallel computing to execute complex models

Seamless library management backed by Google

Quicker updates and frequent new releases to provide you with the latest features

TensorFlow is particularly useful for the following applications:

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2. SciPy

SciPy (Scientific Python) is another free and open-source Python library for data science that is extensively used for high-level computations. SciPy has around 19,000 comments on GitHub and an active community of about 600 contributors. It's extensively used for scientific and technical computations, because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations.

Features:

Collection of algorithms and functions built on the NumPy extension of Python

High-level commands for data manipulation and visualization

Multidimensional image processing with the SciPy ndimage submodule

Includes built-in functions for solving differential equations

Applications:

Multidimensional image operations

Solving differential equations and the Fourier transform

Optimization algorithms

Linear algebra

3. NumPy

NumPy (Numerical Python) is the fundamental package for numerical computation in Python; it contains a powerful N-dimensional array object. It has around 18,000 comments on GitHub and an active community of 700 contributors. It's a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.

Features:

Provides fast, precompiled functions for numerical routines

Array-oriented computing for better efficiency

Supports an object-oriented approach

Compact and faster computations with vectorization

Applications:

Extensively used in data analysis

Creates powerful N-dimensional array

Forms the base of other libraries, such as SciPy and scikit-learn

Replacement of MATLAB when used with SciPy and matplotlib

4. Pandas

Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitHub and an active community of 1,200 contributors, it is heavily used for data analysis and cleaning. Pandas provides fast, flexible data structures, such as data frame CDs, which are designed to work with structured data very easily and intuitively.

Also Read: What is Data Analysis: Methods, Process and Types Explained

Features:

Eloquent syntax and rich functionalities that gives you the freedom to deal with missing data

Enables you to create your own function and run it across a series of data

High-level abstraction

Contains high-level data structures and manipulation tools

Applications:

General data wrangling and data cleaning

ETL (extract, transform, load) jobs for data transformation and data storage, as it has excellent support for loading CSV files into its data frame format

Used in a variety of academic and commercial areas, including statistics, finance and neuroscience

Time-series-specific functionality, such as date range generation, moving window, linear regression and date shifting.

Python Students Also Learn

Data Science | Machine Learning | Tableau | Data Analysis | Statistics

JavaScript | Excel | Deep Learning | Artificial Intelligence

5. Matplotlib

Matplotlib has powerful yet beautiful visualizations. It's a plotting library for Python with around 26,000 comments on GitHub and a very vibrant community of about 700 contributors. Because of the graphs and plots that it produces, it's extensively used for data visualization. It also provides an object-oriented API, which can be used to embed those plots into applications.

Features:

Usable as a MATLAB replacement, with the advantage of being free and open source

Supports dozens of backends and output types, which means you can use it regardless of which operating system you're using or which output format you wish to use

Pandas itself can be used as wrappers around MATLAB API to drive MATLAB like a cleaner

Low memory consumption and better runtime behavior

Applications:

Correlation analysis of variables

Visualize 95 percent confidence intervals of the models

Outlier detection using a scatter plot etc.

Visualize the distribution of data to gain instant insights

Also Read: Exploring The Data Science Learning Path

Build your career in Data Analytics with our Data Analyst Master's Program! Cover core topics and important concepts to help you get started the right way!

6. Keras

Similar to TensorFlow, Keras is another popular library that is used extensively for deep learning and neural network modules. Keras supports both the TensorFlow and Theano backends, so it is a good option if you don't want to dive into the details of TensorFlow.

Also Read: Keras vs Tensorflow vs Pytorch

Features:

Keras provides a vast prelabeled datasets which can be used to directly import and load.

It contains various implemented layers and parameters that can be used for construction, configuration, training, and evaluation of neural networks

Applications:

One of the most significant applications of Keras are the deep learning models that are available with their pretrained weights. You can use these models directly to make predictions or extract its features without creating or training your own new model.

7. Scikit-learn

Next in the list of the top python libraries for data science comes Scikit-learn, a machine learning library that provides almost all the machine learning algorithms you might need. Scikit-learn is designed to be interpolated into NumPy and SciPy.

Applications:

clustering

classification

regression

model selection

dimensionality reduction

8. PyTorch

Next in the list of top python libraries for data science is PyTorch, which is a Python-based scientific computing package that uses the power of graphics processing units. PyTorch is one of the most commonly preferred deep learning research platforms built to provide maximum flexibility and speed.

Applications:

PyTorch is famous for providing two of the most high-level features

tensor computations with strong GPU acceleration support

building deep neural networks on a tape-based autograd system

9. Scrapy

The next known python libraries for data science is Scrapy. Scrapy isone of the most popular, fast, open-source web crawling frameworks written in Python. It is commonly used to extract the data from the web page with the help of selectors based on XPath.

Applications:

Scrapy helps in building crawling programs (spider bots) that can retrieve structured data from the web

Scrappy is also used to gather data from APIs and follows a 'Don't Repeat Yourself' principle in the design of its interface, influencing users to write universal codes that can be reused for building and scaling large crawlers.

Your Dream Career is Just Around The Corner!

Data Scientist Master's Program Explore Program

10. BeautifulSoup

BeautifulSoup - the next python library for data science. This is another popular python library most commonly known for web crawling and data scraping. Users can collect data that's available on some website without a proper CSV or API, and BeautifulSoup can help them scrape it and arrange it into the required format.

Looking forward to making a move to the programming field? Take up the Python Training Course and begin your career as a professional Python programmer.

Become a Python Pro

In addition to these top 10 Python libraries for data science, there are many other helpful python libraries ones that deserve to be looked at. And as a next step, if you are interested in learning and mastering data science with python, head onto Simpliearn's Data Science with Python Certification Course. Do explore the frequently asked Data Science interview question, and unlock your career as a data scientist!

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in data science which of the python library are more popular

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Top 10 Python Libraries for Data Science for 2023

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Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming.

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The primary colors of data: hacking skills, math and stats knowledge, and substantive expertise

On Monday we spent a lot of time talking about "where" a course on data science might exist at a university. The conversation was largely rhetorical, as everyone was well aware of the inherent interdisciplinary nature of the these skills; but then, why have I highlighted these three? First, none is discipline specific, but more importantly, each of these skills are on their own very valuable, but when combined with only one other are at best simply not data science, or at worst downright dangerous.

For better or worse, data is a commodity traded electronically; therefore, in order to be in this market you need to speak hacker. This, however, does not require a background in computer science-in fact-many of the most impressive hackers I have met never took a single CS course. Being able to manipulate text files at the command-line, understanding vectorized operations, thinking algorithmically; these are the hacking skills that make for a successful data hacker.

Once you have acquired and cleaned the data, the next step is to actually extract insight from it. In order to do this, you need to apply appropriate math and statistics methods, which requires at least a baseline familiarity with these tools. This is not to say that a PhD in statistics in required to be a competent data scientist, but it does require knowing what an ordinary least squares regression is and how to interpret it.

In the third critical piece-substance-is where my thoughts on data science diverge from most of what has already been written on the topic. To me, data plus math and statistics only gets you machine learning, which is great if that is what you are interested in, but not if you are doing data science. Science is about discovery and building knowledge, which requires some motivating questions about the world and hypotheses that can be brought to data and tested with statistical methods. On the flip-side, substantive expertise plus math and statistics knowledge is where most traditional researcher falls. Doctoral level researchers spend most of their time acquiring expertise in these areas, but very little time learning about technology. Part of this is the culture of academia, which does not reward researchers for understanding technology. That said, I have met many young academics and graduate students that are eager to bucking that tradition.

Finally, a word on the hacking skills plus substantive expertise danger zone. This is where I place people who, "know enough to be dangerous," and is the most problematic area of the diagram. In this area people who are perfectly capable of extracting and structuring data, likely related to a field they know quite a bit about, and probably even know enough R to run a linear regression and report the coefficients; but they lack any understanding of what those coefficients mean. It is from this part of the diagram that the phrase "lies, damned lies, and statistics" emanates, because either through ignorance or malice this overlap of skills gives people the ability to create what appears to be a legitimate analysis without any understanding of how they got there or what they have created. Fortunately, it requires near willful ignorance to acquire hacking skills and substantive expertise without also learning some math and statistics along the way. As such, the danger zone is sparsely populated, however, it does not take many to produce a lot of damage.

I hope this brief illustration has provided some clarity into what data science is and what it takes to get there. By considering these questions at a high level it prevents the discussion from degrading into minutia, such as specific tools or platforms, which I think hurts the conversation.

I am sure I have overlooked many important things, but again the purpose was not to be speific. As always, I welcome any and all comments.
Cross-posed at dataists

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The Data Science Venn Diagram - Drew Conway

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On Monday I-humbly-joined a group of NYC's most sophisticated thinkers on all things data for a half-day unconference to help O'Reily organize their upcoming Strata conference . The break out sessions were fantastic, and the number of people in each allowed for outstanding, expert driven, discu

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Como APRENDER INTELIGENCIA ARTIFICIAL Con PYTHON en 5 MINUTOS | IA Python Ejemplos {2023}

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Introducción a Google Colab para principiantes ¿Qué es? ¿cómo se usa? | Guía, Mini curso

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UA - Universitat d'Alacant / Universidad de Alicante

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El impacto de la Inteligencia Artificial en la investigación

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DIPLOMADO INTERNACIONAL IA APLICADA A LA INVESTIGACIÓN

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CONFERENCIA: USO DE LA IA GENERATIVA EN INVESTIGACION

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https://code.visualstudio.com/download

https://code.visualstudio.com/docs/python/python-tutorial

https://code.visualstudio.com/docs/languages/python

https://vscode.dev/?vscode-lang=es-us

https://colab.research.google.com/

https://www.simplilearn.com/top-python-libraries-for-data-science-article

http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

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Herramientas y librerías de Machine Learning en Python | OpenWebinars

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¿Quieres conocer las mejores herramientas y librerías de Machine Learning en Python? Descubre cómo te ayudan a crear modelos de ML precisos y eficientes.

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Vicerrectoría de Investigación y Postgrado UASD

# data[].attributes.attachment_link.title
Diplomado en Inteligencia Artificial - Análisis de Datos

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Machine Learning: Algorithms, Real-World Applications and Research Directions - SN Computer Science

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In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

# data[].attributes.body
https://code.visualstudio.com/download

https://code.visualstudio.com/docs/python/python-tutorial

https://code.visualstudio.com/docs/languages/python

https://vscode.dev/?vscode-lang=es-us

https://colab.research.google.com/

https://www.simplilearn.com/top-python-libraries-for-data-science-article

http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

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Herramientas y librerías de Machine Learning en Python | OpenWebinars

# data[].attributes.attachment_link.description
¿Quieres conocer las mejores herramientas y librerías de Machine Learning en Python? Descubre cómo te ayudan a crear modelos de ML precisos y eficientes.

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GitHub - jakevdp/PythonDataScienceHandbook: Python Data Science Handbook: full text in Jupyter Notebooks

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Python Data Science Handbook: full text in Jupyter Notebooks - GitHub - jakevdp/PythonDataScienceHandbook: Python Data Science Handbook: full text in Jupyter Notebooks

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Introducción a Google Colab para principiantes ¿Qué es? ¿cómo se usa? | Guía, Mini curso