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Ce que vous allez apprendre
Perform regression analysis, least squares and inference using regression models.
Build and apply prediction functions
Develop public data products
Understand the process of drawing conclusions about populations or scientific truths from data
Compétences que vous acquerrez
À propos de ce Spécialisation
Projet d'apprentissage appliqué
Each course in this Data Science: Statistics and Machine Learning Specialization includes a hands-on, peer-graded assignment. To earn the Specialization Certificate, you must successfully complete the hands-on, peer-graded assignment in each course, including the final Capstone Project.
Certaines connaissances prérequises.
Certaines connaissances prérequises.
Cette Spécialisation compte 5 cours
Inférence statistique
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
Modèles de régression
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
Apprentissage mechanique pratique
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
Développement de produits de données
A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.
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Université Johns-Hopkins
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Foire Aux Questions
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
Can I just enroll in a single course?
Puis-je m'inscrire à un seul cours ?
Can I take the course for free?
Puis-je suivre le cours gratuitement ?
Ce cours est-il vraiment accessible en ligne à 100 % ? Dois-je assister à certaines activités en personne ?
Quelle est la durée nécessaire pour terminer la Spécialisation ?
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
Can I sign up for the course without paying or applying for financial aid?
D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.