Acerca de este Curso
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Aprox. 5 horas para completar

Sugerido: This course requires 7.5 to 9 hours of study....

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Habilidades que obtendrás

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel avanzado

Aprox. 5 horas para completar

Sugerido: This course requires 7.5 to 9 hours of study....

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
3 horas para completar

Data Analysis

6 videos (Total 26 minutos), 12 lecturas, 4 cuestionarios
6 videos
Introduction to Data Visualizations3m
Data Visualizations7m
Introduction to Missing Values4m
Missing Values4m
Case Study Introduction2m
12 lecturas
Why is exploratory data analysis necessary?3m
Data Visualization: Through the eyes of our Working Example3m
Getting Started / Unit Materials2m
Data visualization in Python3m
Missing Data: Introduction2m
Strategies for missing data3m
Categories of missingness2m
Simple imputation2m
Bayesian imputation10m
Case Study: Getting started2m
Build a deliverable1h 30m
Summary/Review5m
4 ejercicios de práctica
Check for Understanding: EDA2m
Check for Understanding: Data Visualization4m
Check for Understanding: Missing Data4m
Data Analysis Module Quiz5m
Semana
2
3 horas para completar

Data Investigation

3 videos (Total 16 minutos), 14 lecturas, 3 cuestionarios
3 videos
Hypothesis testing10m
Case Study Introduction2m
14 lecturas
TUTORIAL: IBM Watson Studio dashboard10m
Hypothesis Testing: Through the eyes of our Working Example10m
Overview2m
Statistical Inference2m
Business scenarios and probability3m
Variants on t-tests2m
One-way Analysis of Variance (ANOVA)4m
p-value limitations10m
Multiple Testing4m
Explain methods for dealing with multiple testing3m
Getting Started3m
Import the Data4m
Data Processing (Includes Assessment)2h
Summary/Review4m
3 ejercicios de práctica
Check for Understanding: Hypothesis Testing4m
Check for Understanding: Hypothesis Testing Limitations2m
Data Investigation Module Quiz5m

Instructores

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Mark J Grover

Digital Content Delivery Lead
IBM Data & AI Learning
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Ray Lopez, Ph.D.

Data Science Curriculum Leader
IBM Data & Artificial Intelligence

Acerca de IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

Acerca de Programa especializado IBM AI Enterprise Workflow

This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow....
IBM AI Enterprise Workflow

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

  • This course assumes that you are already familiar with basic data science concepts including probability and statistics, linear algebra, machine learning, and the use of Python and Jupyter. Additionally, you should have already completed the first course in this specialization: AI Workflow: Business Priorities and Data Ingestion.

  • No. Most of the exercises may be completed with open source tools running on your personal computer. However, the exercises are designed with an enterprise focus and are intended to be run in an enterprise environment that allows for easier sharing and collaboration. The exercises in the last two modules of the course are heavily focused on deployment and testing of machine learning models and use the IBM Watson tooling found on the IBM Cloud.

  • Yes. All IBM Cloud Data and AI services are based upon open source technologies.

  • The exercises in the course may be completed by anyone using the IBM Cloud "Lite" plan, which is free for use.

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