IPN-Dharma IA Lab

    Bienvenidos
    IPN-Dharma IA Lab

    Es una iniciativa de Laboratorio de Inteligencia Artificial del CIC del IPN con la colaboración de DHARMA para motivar a investigadores, profesores y estudiantes a aprovechar los cursos, recursos y herramientas de las principales plataformas tecnológicas de la industria en las áreas de Aprendizaje Automático, Ciencia de Datos, Computación en la Nube, Inteligencia Artificial e Internet de las Cosas con el propósito de generar una experiencia práctica a través de un modelo de aprendizaje entre pares y por objetivos.

    Nivel 1: Alfabetización y Fundamentos

    Data Science for Beginners

    Data Science for Beginners is a curriculum of 20 lessons that focus on the foundations of Data Science and requires no prior knowledge to get started. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. The project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.

    Each lesson includes:

    • Optional sketchnote
    • Optional supplemental video
    • Pre-lesson warmup quiz
    • Written lesson
    • For project-based lessons, step-by-step guides on how to build the project
    • Knowledge checks
    • A challenge
    • Supplemental reading
    • Assignment
    • Post-lesson quiz

    Cursos en este programa

    1) Data Science for Beginners

    Data Science for Beginners focuses on foundational concepts and practical applications of Data Science.

    By the end of this series, students will have learned basic principles of data science, including ethical concepts, data preparation, different ways of working with data, data visualization, data analysis, real-world use cases of data science, and more.

    Esfuerzo  Esfuerzo estimado 10 semanas

    Idioma  Idioma inglés

    Link  GitHub

    Introduction
    01
    Defining Data Science
    Learn the basic concepts behind data science and how it’s related to artificial intelligence, machine learning, and big data.
    02
    Data Science Ethics
    Data Ethics Concepts, Challenges & Frameworks.
    03
    Defining Data
    How data is classified and its common sources.
    04
    Introduction to Statistics & Probability
    The mathematical techniques of probability and statistics to understand data.
    Working With Data
    05
    Working with Relational Data
    Introduction to relational data and the basics of exploring and analyzing relational data with the Structured Query Language, also known as SQL (pronounced “see-quell”).
    06
    Working with NoSQL Data
    Introduction to non-relational data, its various types and the basics of exploring and analyzing document databases.
    07
    Working with Python
    Basics of using Python for data exploration with libraries such as Pandas. Foundational understanding of Python programming is recommended.
    08
    Data Preparation
    Topics on data techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data.
    Data Visualization
    09
    Visualizing Quantities
    Learn how to use Matplotlib to visualize bird data.
    10
    Visualizing Distributions of Data
    Visualizing observations and trends within an interval.
    11
    Visualizing Proportions
    Visualizing discrete and grouped percentages.
    12
    Visualizing Relationships
    Visualizing connections and correlations between sets of data and their variables.
    13
    Meaningful Visualizations
    Techniques and guidance for making your visualizations valuable for effective problem solving and insights.
    Lifecycle
    14
    Introduction to the Data Science lifecycle
    Introduction to the data science lifecycle and its first step of acquiring and extracting data.
    15
    Analyzing
    This phase of the data science lifecycle focuses on techniques to analyze data.
    16
    Communication
    This phase of the data science lifecycle focuses on presenting the insights from the data in a way that makes it easier for decision makers to understand.
    Cloud Data
    17
    Data Science in the Cloud
    This series of lessons introduces data science in the cloud and its benefits.
    18
    Data Science in the Cloud
    Training models using Low Code tools.
    19
    Data Science in the Cloud
    Deploying models with Azure Machine Learning Studio.
    In the Wild
    20
    Data Science in the Wild
    Data science driven projects in the real world.
    © 2015 |Laboratorio de Microtecnología y Sistemas Embebidos | Centro de Investigación en Computación | Instituto Politécnico Nacional