À propos de ce cours
2,990 consultations récentes

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau avancé

Approx. 6 heures pour terminer

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

Anglais

Sous-titres : Anglais

Compétences que vous acquerrez

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
Les étudiants prenant part à ce Course sont
  • Data Scientists
  • Data Analysts
  • Software Engineers

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau avancé

Approx. 6 heures pour terminer

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

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
3 heures pour terminer

Data Analysis

6 vidéos (Total 26 min), 12 lectures, 4 quiz
6 vidéos
Introduction to Data Visualizations3 min
Data Visualizations7 min
Introduction to Missing Values4 min
Missing Values4 min
Case Study Introduction2 min
12 lectures
Why is exploratory data analysis necessary?3 min
Data Visualization: Through the eyes of our Working Example3 min
Getting Started / Unit Materials2 min
Data visualization in Python3 min
Missing Data: Introduction2 min
Strategies for missing data3 min
Categories of missingness2 min
Simple imputation2 min
Bayesian imputation10 min
Case Study: Getting started2 min
Build a deliverable1h 30min
Summary/Review5 min
4 exercices pour s'entraîner
Check for Understanding: EDA2 min
Check for Understanding: Data Visualization4 min
Check for Understanding: Missing Data4 min
Data Analysis Module Quiz5 min
Semaine
2
3 heures pour terminer

Data Investigation

3 vidéos (Total 16 min), 14 lectures, 3 quiz
3 vidéos
Hypothesis testing10 min
Case Study Introduction2 min
14 lectures
TUTORIAL: IBM Watson Studio dashboard10 min
Hypothesis Testing: Through the eyes of our Working Example10 min
Overview2 min
Statistical Inference2 min
Business scenarios and probability3 min
Variants on t-tests2 min
One-way Analysis of Variance (ANOVA)4 min
p-value limitations10 min
Multiple Testing4 min
Explain methods for dealing with multiple testing3 min
Getting Started3 min
Import the Data4 min
Data Processing (Includes Assessment)2 h
Summary/Review4 min
3 exercices pour s'entraîner
Check for Understanding: Hypothesis Testing4 min
Check for Understanding: Hypothesis Testing Limitations2 min
Data Investigation Module Quiz5 min

Enseignants

Avatar

Mark J Grover

Digital Content Delivery Lead
IBM Data & AI Learning
Avatar

Ray Lopez, Ph.D.

Data Science Curriculum Leader
IBM Data & Artificial Intelligence

À propos 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....

À propos du Spécialisation 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

Foire Aux Questions

  • Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.

  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

  • 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.

D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.