Isabelle Claire Valette successfully completed the Johns Hopkins University course "Regression Models" on Coursera with distinction. The course taught students how to fit and interpret regression models, investigate residuals and variability, and use techniques like dummy variables, multivariable adjustment, and extensions to Poisson and logistic regression. The course was offered online through Coursera and did not reflect the entire on-campus curriculum at Johns Hopkins University.
Fit regression models, interpret coefficients, and investigate residuals and variability. Students also learn to use dummy variables, multi-variable adjustment, and extensions to generalized linear models, especially Poisson and logistic
regression.
Fit regression models, interpret coefficients, and investigate residuals and variability. Students also learn to use dummy variables, multi-variable adjustment, and extensions to generalized linear models, especially Poisson and logistic
regression.
Statement of Accomplishment: Data Science Specialization VI - Statistical Inf...Folco Bombardieri
Statement of Accomplishment for the "Statistical Inference" Course from Coursera - 6th Course of the Data Science Specialization series (offered by Johns Hopkins University)
Duration: 4 weeks
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.
Statement of Accomplishment: Data Science Specialization VI - Statistical Inf...Folco Bombardieri
Statement of Accomplishment for the "Statistical Inference" Course from Coursera - 6th Course of the Data Science Specialization series (offered by Johns Hopkins University)
Duration: 4 weeks
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.
Broad overview of the goals, assumptions, and modes of statistical inference. Can perform inferential tasks in highly targeted settings and are able to use the skills developed for more complex inferential challenges.
Understand the components of a machine learning algorithm and how to apply multiple basic machine learning tools. Build and Evaluate Predictors on real data.
1. coursera.org
Statement of Accomplishment
WITH DISTINCTION
MARCH 04, 2015
ISABELLE CLAIRE VALETTE
HAS SUCCESSFULLY COMPLETED THE JOHNS HOPKINS UNIVERSITY'S OFFERING OF
Regression Models
Students learn how to fit regression models, interpret coefficients,
and investigate residuals and variability. Students also learn to
use dummy variables, multivariable adjustment, and extensions
to generalized linear models, especially Poisson and logistic
regression.
BRIAN CAFFO, PHD, MS
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
ROGER D. PENG, PHD
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
JEFFREY LEEK, PHD
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
PLEASE NOTE: THE ONLINE OFFERING OF THIS CLASS DOES NOT REFLECT THE ENTIRE CURRICULUM OFFERED TO STUDENTS ENROLLED AT
THE JOHNS HOPKINS UNIVERSITY. THIS STATEMENT DOES NOT AFFIRM THAT THIS STUDENT WAS ENROLLED AS A STUDENT AT THE JOHNS
HOPKINS UNIVERSITY IN ANY WAY. IT DOES NOT CONFER A JOHNS HOPKINS UNIVERSITY GRADE; IT DOES NOT CONFER JOHNS HOPKINS
UNIVERSITY CREDIT; IT DOES NOT CONFER A JOHNS HOPKINS UNIVERSITY DEGREE; AND IT DOES NOT VERIFY THE IDENTITY OF THE
STUDENT.