Acerca de este Curso

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Certificado para compartir
Obtén un certificado al finalizar
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. 8 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Habilidades que obtendrás

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
Certificado para compartir
Obtén un certificado al finalizar
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. 8 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

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IBM

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

4 horas para completar

Data Analysis

4 horas para completar
6 videos (Total 26 minutos), 11 lecturas, 6 cuestionarios
6 videos
Introduction to Data Visualizations3m
Data Visualizations7m
Introduction to Missing Values4m
Missing Values4m
Case Study Introduction2m
11 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 Missing Data2m
Simple Imputation2m
Bayesian Imputation10m
Case Study: Getting started2m
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

Semana 2

3 horas para completar

Data Investigation

3 horas para completar
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

Revisiones

Principales revisiones sobre AI WORKFLOW: DATA ANALYSIS AND HYPOTHESIS TESTING

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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

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
  • 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.

  • Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.

  • Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el enlace de Ayuda económica que está debajo del botón “Inscribirse” a la izquierda. Se te pedirá que completes una solicitud. Recibirás una notificación en caso de que se apruebe. Deberás completar este paso para cada uno de los cursos que forman parte del Programa especializado, incluido el proyecto final. Obtén más información.

  • 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. The certification exam is administered by Pearson VUE and must be taken at one of their testing facilities. You may visit their site at https://home.pearsonvue.com/ for more information.

  • Please visit the Pearson VUE web site at https://home.pearsonvue.com/ for the latest information on taking the AI Enterprise Workflow certification test.

  • It is highly recommended that you have at least a basic working knowledge of design thinking and Watson Studio prior to taking this course. Please visit the IBM Skills Gateway at http://ibm.com/training/badges and "Find a Badge" related to "design thinking" or "Watson Studio". From there you will be directed to courses covering these topics.

  • 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.

  • Este Curso no otorga crédito universitario, pero algunas universidades pueden aceptar los Certificados del curso para obtener crédito. Consulta con tu institución para obtener más información. Los Títulos en línea y los Certificados Mastertrack™ de Coursera brindan la oportunidad de obtener créditos universitarios.

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